CCDM Certified Clinical Data Manager Questions and Answers

When aprotocol is amended mid-study, resulting in additional data collection requirements, theData Management Plan (DMP)must beupdated accordinglyand all relevant stakeholders must benotified.

According to theGCDMP (Chapter: Data Management Planning and Study Start-up), the DMP is aliving documentthat defines all data management processes for a clinical study. It must accurately reflect thecurrent data flow, CRF design, validation procedures, and reporting structure. Any protocol amendments affecting data capture, structure, or analysis require immediate DMP revision and distribution to ensure alignment across data management, clinical, and biostatistics teams.

Failure to update and communicate DMP changes can lead to misalignment in data handling and introduce compliance risks during audits or inspections. Therefore,Option Bis correct: the DMP must be updated and the change communicated to all stakeholders (e.g., sponsor, CRO, clinical operations, biostatistics).

Reference (CCDM-Verified Sources):

SCDM Good Clinical Data Management Practices (GCDMP), Chapter: Data Management Plan (DMP), Section 5.3 – Maintaining and Updating the DMP

ICH E6 (R2) Good Clinical Practice, Section 5.5.3 – Documentation of Protocol Changes and Data Handling Procedures

FDA Guidance for Industry: Computerized Systems Used in Clinical Investigations – Section on Data Management Documentation

UnderICH E6 (R2) Good Clinical Practiceand21 CFR Part 312.52, when asponsor delegates or transfers obligationsfor a clinical trial to aContract Research Organization (CRO), there must be awritten description of each specific obligation being assumed by the CRO.

According to theGood Clinical Data Management Practices (GCDMP), while sponsors may outsource responsibilities such as data management, monitoring, or biostatistics,ultimate accountability remains with the sponsor. The documentation of the transfer of responsibilities ensures regulatory transparency and compliance.

This written agreement, often referred to as aTransfer of Obligations (TOO)document, defines exactly which duties the CRO is responsible for (e.g., CRF design, data cleaning, database lock), as well as any retained sponsor oversight. A general statement that "all obligations are transferred" (option D) is insufficient per regulatory expectations, as sponsors must retain traceability of responsibility.

Therefore,Option Bis correct — a detailed written description of transferred obligations is required.

Reference (CCDM-Verified Sources):

SCDM Good Clinical Data Management Practices (GCDMP), Chapter: Regulatory Compliance and Oversight, Section 5.2 – Sponsor and CRO Responsibilities

ICH E6 (R2) Good Clinical Practice, Section 5.2.1 – Transfer of Trial-Related Duties and Functions

FDA 21 CFR 312.52 – Transfer of Obligations to a Contract Research Organization

To monitorsite variability in eligibility screening, you must analyzethe number of subjects screenedversusthe number of subjects enrolledat each site. This allows identification of sites that are over- or under-screening relative to their enrollment yield.

TheGCDMP (Chapter: Data Quality Assurance and Metrics)emphasizes thatscreening-to-enrollment ratiosare critical indicators of protocol compliance and data quality. Sites with unusually low conversion rates may have unclear understanding of inclusion/exclusion criteria, requiring targeted training or monitoring.

Other options (A, C, D) provide enrollment metrics but do not revealscreening efficiency or variability, which depend on bothscreening and enrollment data.

Thus,option Bcorrectly identifies the data necessary for monitoring eligibility screening performance across sites.

Reference (CCDM-Verified Sources):

SCDM GCDMP, Chapter: Data Quality Assurance and Metrics, Section 5.4 – Site Performance Metrics

ICH E6(R2) GCP, Section 5.18 – Monitoring and Site Oversight Requirements

The most essential Standard Operating Procedure (SOP) formanagement of an Electronic Data Capture (EDC)system isChange Control.

PerGCDMP (Chapter: Computerized Systems and Compliance)andFDA 21 CFR Part 11, any changes made to an EDC system—whether to software configuration, study database design, or system functionality—must followa documented, validated, and auditable change control process. This ensures that:

Modifications are properly authorized, tested, and approved before implementation.

System validation remains intact.

Data integrity, traceability, and regulatory compliance are maintained.

While vendor management (A) and coding maintenance (C) have supporting SOPs,change control (D)ismandatoryfor any system handling regulated clinical data. Measurement of data quality (B) is important but not specifically tied to system management procedures.

Thus,option D (Change control)is the correct answer.

Reference (CCDM-Verified Sources):

SCDM GCDMP, Chapter: Computerized Systems and Compliance, Section 5.3 – Change Control and System Maintenance

FDA 21 CFR Part 11 – Electronic Records and Electronic Signatures, Section 11.10(a–k)

ICH E6(R2) GCP, Section 5.5.3 – Computerized Systems Validation and Change Documentation

Theinner joinoperation retrievesonly the records that exist in both tables, which is theintersectionof two datasets.

In clinical data management, relational databases often store related data in multiple tables—for example, demographic data in one table and lab results in another. When a Data Manager needs to extract records that exist in both (e.g., subjects appearing in both demographics and labs), aninner joinis used.

According to theGCDMP (Chapter: Database Design and Build), joins are fundamental relational operations ensuring data consistency and integrity across multiple data domains.

Inner join:Returns matching records from both tables (intersection).

Left/right outer joins:Return all records from one table and matching records from the other (preserving nonmatches).

Full outer join:Returns all records from both tables, whether matched or not.

Therefore, to select only the50 records commonto both tables, the correct operation is aninner join.

Reference (CCDM-Verified Sources):

SCDM Good Clinical Data Management Practices (GCDMP), Chapter: Database Design and Build, Section 4.3 – Relational Database Concepts and Joins

ICH E6 (R2) Good Clinical Practice, Section 5.5.3 – Data Processing and Validation

Regulatory agencies such as theFDAandICHrequire thatelectronic data be retained in a format that preserves audit trails and traceability.

While PDF images (option C) provide a static representation of data, they do not preserve theunderlying audit trail(i.e., who changed what, when, and why). TheASCII data files with corresponding audit trails(option D) provide complete transparency and comply with21 CFR Part 11andGCDMParchival standards.

Option A(storing computers) is unnecessary and impractical, andOption B(paper source documents) are site records, not system archives.

Hence,option Dis correct —ASCII data files with audit trailsmeet traceability and compliance standards.

Reference (CCDM-Verified Sources):

SCDM GCDMP, Chapter: Database Lock and Archiving, Section 5.4 – Archival Formats and Audit Trail Retention

ICH E6(R2) GCP, Section 5.5.3 – Data Integrity, Audit Trails, and Record Retention

FDA 21 CFR Part 11 – Electronic Records; Audit Trail and Retention Requirements

This event qualifies as aSerious Adverse Event (SAE)because itresulted in a prolonged hospitalization, even though the episode itself was mild.

According toICH E2AandGCDMP (Chapter: Safety Data Handling and Reconciliation), an adverse event is considered“serious”if it results in any of the following outcomes:

Death,

Life-threatening situation,

Hospitalization or prolongation of existing hospitalization,

Persistent or significant disability/incapacity, or

Congenital anomaly/birth defect.

The severity (mild, moderate, severe) describesintensity, while seriousness describesregulatory significance and medical outcome. Thus, a mild tachycardia episode leading to extended hospital stay meets theregulatory definition of an SAE.

Reference (CCDM-Verified Sources):

SCDM Good Clinical Data Management Practices (GCDMP), Chapter: Safety Data Handling and Reconciliation, Section 5.2 – Definition and Classification of Serious Adverse Events

ICH E2A – Clinical Safety Data Management: Definitions and Standards for Expedited Reporting, Section II – Seriousness Criteria

FDA 21 CFR 312.32 – IND Safety Reporting: Serious Adverse Event Definitions

In theEDC database build process, the first logical specifications that require approval are theelectronic Case Report Form (eCRF) fields.

According to theGood Clinical Data Management Practices (GCDMP, Chapter: Database Design and Build), eCRF field specifications define what data elements are collected, their data types, permitted values, field lengths, and any associated metadata. Approval of these specifications forms the foundation for subsequent design components such asedit check programming,query management rules, anddata validation logic.

Edit checks (B)are developed only after fields and structures are finalized.

Metric reports (C)andeCRF guidelines (D)are downstream documentation or tools, not logical specifications required at the build start.

Therefore,option A (eCRF fields)is correct, as their approval marks the first formal milestone in the EDC system development life cycle.

Reference (CCDM-Verified Sources):

SCDM GCDMP, Chapter: Database Design and Build, Section 4.2 – Logical Design and eCRF Field Specifications

ICH E6(R2) GCP, Section 5.5.3 – System Design and Validation Documentation

FDA 21 CFR Part 11 – System Validation and Documentation Controls

Assuming the data transfer, integration, and validation processes are properly controlled and compliant,electronic acquisitionof clinical data from medical devices is preferred because it allowsmore frequent and accurate data collection, leading to higher data resolution and integrity.

Per theGCDMP (Chapter: Technology and Data Integration), automated data collection minimizes manual transcription and reduces latency in data capture, ensuring both efficiency and completeness. While manual processes introduce human transcription errors and limit frequency, continuous electronic data capture can record thousands of accurate, time-stamped measurements, improving the study’s analytical power.

However,option Dslightly overstates the case — human error isreduced, not entirely eliminated, since setup, calibration, and integration still involve human oversight. Therefore,option Cis the best and most precise response, emphasizing the advantage of more robust and complete data capture.

Reference (CCDM-Verified Sources):

SCDM GCDMP, Chapter: Technology and Data Integration, Section 5.4 – Automated Data Acquisition and Validation

ICH E6(R2) GCP, Section 5.5.3 – Validation of Computerized Systems and Electronic Data Sources

FDA Guidance for Industry: Computerized Systems Used in Clinical Investigations, Section 6.3 – Direct Data Capture from Instruments and Devices

Whenlaboratory dataare missing from a paper-based clinical study, theData Managershould directdata-entry personnel to issue a queryto the investigative site for clarification or correction.

According to theGood Clinical Data Management Practices (GCDMP, Chapter: Data Validation and Cleaning), every missing, inconsistent, or out-of-range data point must be reviewed and, if necessary, resolved through the formalquery management process. This ensures that all discrepancies between the source documents and database entries are properly documented, traceable, and auditable.

Data-entry staff arenot authorizedto infer or fill in missing information. They must escalate such discrepancies to the site via query, preservingdata integrityandregulatory compliancewithICH E6 (R2)andFDA 21 CFR Part 11. Calling the patient directly (option B) would violate confidentiality and site communication protocol, while simply flagging or ignoring the issue (options A and D) would not meet GCDMP query resolution standards.

Reference (CCDM-Verified Sources):

SCDM Good Clinical Data Management Practices (GCDMP), Chapter: Data Validation and Cleaning, Section 5.2 – Query Management and Resolution

ICH E6 (R2) Good Clinical Practice, Section 5.18.4 – Communication of Data Discrepancies

FDA 21 CFR Part 11 – Electronic Records; Query Audit Trails Requirements

This question tests workload estimation and resource planning, which are fundamental competencies outlined in the Good Clinical Data Management Practices (GCDMP, Chapter on Project Management in Data Management). The task is to determine the Data Manager effort required based on the frequency and duration of data collection and processing activities.

Let’s calculate step by step:

Number of patients: 100

Frequency: Weekly (once per week)

Duration: 6 months ≈ 26 weeks

Time per file: 20 minutes

Total time per week:

100 patients × 20 minutes = 2,000 minutes per week

= 2,000 ÷ 60 = 33.3 hours per week

Total hours over 6 months:

33.3 hours/week × 26 weeks = 866 hours total

A full-time Data Manager typically works ~160 hours per month, so over six months:

160 × 6 = 960 hours total full-time capacity.

Therefore, the workload of 866 hours is approximately equivalent to one full-time Data Manager working across the six-month period:

866 ÷ 960 ≈ 0.9 FTE (Full-Time Equivalent).

This aligns most closely with Option D: One Data Manager per month (i.e., a full-time resource is required throughout the duration of the trial).

According to the GCDMP Project Management chapter, accurate resource estimation is critical in ensuring data management timelines are met without overloading staff or compromising data quality. The estimation process must consider not just the raw data download time but also associated data processing, verification, and upload into the clinical database.

Other options underestimate the effort significantly:

A (10%) and B (50%) do not account for cumulative weekly workload across multiple patients.

C (Two Data Managers) overestimates, as one Data Manager working full-time can manage the load efficiently.

Therefore, Option D is correct — approximately one full-time Data Manager (1.0 FTE) is required for the activity data alone during the six-month trial.

Reference (CCDM-Verified Sources):

Society for Clinical Data Management (SCDM), Good Clinical Data Management Practices (GCDMP), Chapter: Project Management in Data Management, Section 5.3 – Workload Estimation and Resource Allocation

SCDM GCDMP, Chapter: Data Handling and Processing – Effort Estimation for Repetitive Data Tasks

ICH E6 (R2) Good Clinical Practice, Section 5.1 – Quality Management and Resource Planning

FDA Guidance for Industry: Electronic Source Data in Clinical Investigations, Section 4.3 – Operational Considerations for Data Management Activities

Thestudy endpoints sectionof the protocol best defines thedata required for the primary study analysis.

According to theGood Clinical Data Management Practices (GCDMP, Chapter: Data Management Planning and Study Start-up), theendpoint sectionspecifies the critical efficacy and safety variables upon which the study’s success criteria are based. These endpoints directly determine what data elements must be collected, validated, and analyzed. For example, if the primary endpoint is “change in systolic blood pressure from baseline to week 12,” then data collection must include baseline and week 12 systolic blood pressure values and corresponding timepoints.

Theschedule of events (option A)lists when data are collected but not their analytical relevance. Theprotocol synopsis (option C)provides a summary, while theICH essential documents (option D)refer to trial documentation standards, not endpoint specifications.

Thus, thestudy endpoints sectiondefines thecore analytical data requirementsfor clinical data managers, biostatisticians, and programmers.

Reference (CCDM-Verified Sources):

SCDM Good Clinical Data Management Practices (GCDMP), Chapter: Data Management Planning and Study Start-up, Section 5.2 – Defining Data Needed for Endpoints

ICH E6 (R2) Good Clinical Practice, Section 6.3 – Trial Objectives and Endpoints

FDA Guidance for Industry: Clinical Trial Endpoints for Drug Development and Approval

The most effective method forquality control (QC)of the query resolution process is toperform random audits of resolved query forms. This ensures that queries are being appropriately raised, addressed, and resolved in accordance with the study protocol, data management plan (DMP), and standard operating procedures (SOPs).

According to theGCDMP (Chapter: Data Validation and Cleaning), QC activities should verify that the data review and query management process maintains high accuracy and consistency. Random auditing of resolved queries enables verification that:

Queries were raised for legitimate discrepancies,

The site’s responses were appropriate, and

The resolution actions taken by data management were correct and well-documented.

Metrics such as turnaround time (options A and C) or query counts (option B) measure efficiency butdo not assess quality. True quality control focuses on ensuring that data corrections preserve accuracy, auditability, and traceability — the fundamental principles ofdata integrityin clinical research.

Reference (CCDM-Verified Sources):

SCDM Good Clinical Data Management Practices (GCDMP), Chapter: Data Validation and Cleaning, Section 5.4 – Query Management and Quality Control

ICH E6 (R2) GCP, Section 5.5.3 – Data Integrity and Validation Procedures

This scenario describes asingle-blindtrial, in whichonly one party—typically thesubject—is unaware of the treatment assignment, while theinvestigator or surgeonknows which intervention is being administered.

In this case, thesurgeonreceives instructions on which stent (test or control) to use, meaning they are aware of treatment allocation. However, thesubject is blindedto which device is being implanted. This setup minimizes subject bias while maintaining procedural safety since the surgeon must know which product to use.

Double-blind (A):Neither subject nor investigator knows the treatment.

Open-label (B):Both subject and investigator know the treatment.

Cross-over (D):Each subject receives both treatments in different periods.

Thus, the correct answer isC. Single-blind, as only the participant remains blinded in this surgical device trial design.

Reference (CCDM-Verified Sources):

SCDM GCDMP, Chapter: Clinical Trial Phases and Protocols, Section 3.2 – Study Blinding and Randomization Concepts

ICH E6(R2) GCP, Section 1.10 – Definition of Blinding/Masking

FDA Guidance for Industry: Design Considerations for Pivotal Clinical Investigations for Medical Devices, Section 5.3 – Blinding in Device Studies

For a well-maintained 100-subject study withongoing data cleaningandcompleted reconciliations, thedatabase lockprocess typically takesa few daysafter theLast Patient Last Visit (LPLV).

According to theGCDMP (Chapter: Database Lock and Archiving), the duration of the lock process depends on the level of data cleanliness at LPLV. If the study team has conducted continuous data cleaning, query resolution, and external data reconciliation throughout the trial, then the final lock steps (e.g., final data review, documentation, and approvals) can be completed in2–5 days.

However, if significant cleaning or reconciliation remains outstanding, lock may take several weeks. Since the question states that data are “maintained almost all clean,”Option B – a few days– is the appropriate estimate.

Reference (CCDM-Verified Sources):

SCDM Good Clinical Data Management Practices (GCDMP), Chapter: Database Lock and Archiving, Section 6.2 – Database Lock Preparation and Timelines

ICH E6 (R2) Good Clinical Practice, Section 5.5.3 – Data Quality and Lock Procedures

FDA Guidance for Industry: Computerized Systems Used in Clinical Investigations – Data Lock and Archiving Procedures

When efficacy data arenot covered by existing CDISC SDTM domains, thefirst resourcethe Data Manager should consult is theCDISC Therapeutic Area Implementation Guide (TAIG)for that therapeutic field.

According to theGCDMP (Chapter: Standards and Data Mapping), CDISC’s Therapeutic Area User Guides (TAUGs) and Implementation Guides providestandardized data structures, variable definitions, controlled terminology, and implementation examplesfor specific diseases or therapeutic areas. These guides ensure consistency across studies, promote interoperability, and align data collection with regulatory submission expectations.

Consulting other sponsors’ forms or external registries (options A and C) can be informative but do not provide authoritative CDISC-compliant standards. SNOMED terms (option B) address medical terminology, not structural data domain definitions.

Therefore,Option Dis correct—CDISC TA Implementation Guidesare the recognized primary reference when extending or designing SDTM-compliant CRFs.

Reference (CCDM-Verified Sources):

SCDM Good Clinical Data Management Practices (GCDMP), Chapter: Standards and Data Mapping, Section 4.2 – Use of CDISC Standards

CDISC Therapeutic Area User Guides (TAUGs) – Implementation Guidance for Domain Extension

FDA Data Standards Catalog – CDISC Therapeutic Area Standards

Once a clinical database islocked, it becomesread-only— no further data modifications can be made by any users, including site personnel. This ensures that the data arefinalized, consistent, and auditablefor statistical analysis and regulatory submission.

According to theGCDMP (Chapter: Database Lock and Archiving), the lock process involves freezing the database to prevent accidental or unauthorized changes. After lock, access permissions are restricted, and all edit and update functions are disabled. If any corrections are required post-lock, the database must beunlocked under controlled procedures(with full audit trail documentation).

Thus,option C—The site study coordinator is not able to make the change— correctly reflects standard EDC functionality and regulatory compliance.

Reference (CCDM-Verified Sources):

SCDM GCDMP, Chapter: Database Lock and Archiving, Section 5.2 – Database Lock Procedures and Controls

ICH E6(R2) GCP, Section 5.5.3 – Data Integrity and Audit Trail Requirements

FDA 21 CFR Part 11 – Controls for Electronic Records and System Lock Functions

SAE reconciliationmust becompleted before database lock or closureto ensure all safety data are consistent between theclinical databaseand thepharmacovigilance (safety) database.

According to theGCDMP (Chapter: Safety Data Handling and Reconciliation), SAE reconciliation involves verifying that all adverse events reported in the clinical trial database are also captured and accurately recorded in the safety system (and vice versa). This is essential to confirm thatno SAE is missing, misclassified, or inconsistently dated or codedbetween the two systems.

Performing this reconciliation before database lock ensures that any discrepancies are corrected, and both databases reflect consistent, verified information for regulatory submission. Conducting this after closure (or only at audit time) would risk data inconsistencies in the final submission datasets.

Reference (CCDM-Verified Sources):

SCDM Good Clinical Data Management Practices (GCDMP), Chapter: SAE Reconciliation, Section 6.1 – Timing and Procedures for Reconciliation

ICH E2A/E2F – Clinical Safety Data Management: Definitions and Standards

FDA Guidance for Industry: E2A – Clinical Safety Data Management: Processing Standards for Safety Reports

In the context ofdatabase design and external data management, atest data transfer(or trial data load) is performed to ensure the proper configuration, structure, and integrity of data imported from an external vendor or system. Thesecond test transferis specifically useful to confirm thatdata structures and formatsare consistently aligned between the sending and receiving systems after initial adjustments have been made from the first test.

According to theGood Clinical Data Management Practices (GCDMP), thefile format— including variables, data types, field lengths, delimiters, and encoding — must be validated during test transfers to confirm compatibility and ensure accurate loading into the target database. Once the initial test identifies and corrects errors (e.g., mismatched variable names or data types), the second transfer verifies that the corrections have been implemented correctly and that the file structure functions as intended.

Testing change management (A) involves procedural controls, not data transfers. Thetransfer method (C)andtransfer frequency (D)are validated during initial process setup, not during subsequent test transfers.

Therefore,option B (File format)is correct, as the second test transfer verifies the technical integrity of the file structure before live production transfers begin.

Reference (CCDM-Verified Sources):

SCDM Good Clinical Data Management Practices (GCDMP), Chapter: External Data Transfers and Data Integration, Section 5.2 – Test Transfers and File Validation

FDA Guidance for Industry: Computerized Systems Used in Clinical Investigations, Section 6.3 – Data Import and Validation Controls

Safety datacollected during a clinical trial are used primarily to supportproduct labeling, ensuring accurate communication of a drug’srisks, contraindications, and adverse reactionsto healthcare providers and patients.

According to theGCDMP (Chapter: Safety Data Handling and Reconciliation)andICH E2A/E2Fguidelines, alladverse events (AEs), serious adverse events (SAEs), and laboratory abnormalitiesare analyzed and summarized to define the safety profile of an investigational product. These data form the basis for regulatory submissions such as theClinical Study Report (CSR)andproduct labeling (e.g., prescribing information), as required by theFDAand other regulatory authorities.

While safety data may contribute indirectly to analyses such as survival curves (option A) or quality of life metrics (option D), theirprimary regulatory functionis to informproduct labelingand post-marketing surveillance documentation.

Reference (CCDM-Verified Sources):

SCDM Good Clinical Data Management Practices (GCDMP), Chapter: Safety Data Handling and Reconciliation, Section 4.3 – Use of Safety Data in Regulatory Submissions

ICH E2A – Clinical Safety Data Management: Definitions and Standards for Expedited Reporting

FDA Guidance for Industry: Adverse Event Reporting and Labeling Requirements

When coding adverse events (AEs) usingMedDRA (Medical Dictionary for Regulatory Activities), valid AE terms must correspond to specific, medically meaningful concepts thatmatch directly to a Preferred Term (PT)orLowest Level Term (LLT)in the dictionary.

Among the options,“Elevated HDL”(High-Density Lipoprotein) represents a single, medically interpretable, and standard term that can directly match to a MedDRA LLT or PT. This makes it suitable forauto-coding, where the system automatically maps verbatim terms to MedDRA entries without manual intervention.

In contrast:

ALT (B)andAbnormal SGOT (C)are incomplete or nonspecific; they describe test names or qualitative interpretations rather than events.

Option Dlists multiple findings, making it too complex for automatic mapping. Such compound entries would requiremanual coding review.

According toGCDMP (Chapter: Medical Coding and Dictionaries), a valid AE term should be:

Clinically interpretable(not just a lab test name)

Unambiguous

Single-concept based, not a collection of results

Thus,option A (Elevated HDL)is correct, as it aligns with MedDRA’s single-concept, standard terminology structure suitable for auto-coding.

Reference (CCDM-Verified Sources):

SCDM GCDMP, Chapter: Medical Coding and Dictionaries, Section 5.3 – Auto-coding and Verbatim Term Management

ICH M1 MedDRA Term Selection: Points to Consider, Section 2.1 – Coding Principles

ICH E2B(R3) – Clinical Safety Data Management: Data Elements for Transmission of Individual Case Safety Reports

The described process isBlind Verification, also known asdouble data entry with blind verification. In this method, two independent operators enter the same data. The second operator isblindedto the first entry to avoid bias. When discrepancies arise, the system flags them for review, and the second entry (or an adjudicated value) is retained as the correct one.

According toGCDMP (Chapter: Data Entry and Data Tracking), blind double data entry is used primarily inpaper-based studiesto minimize transcription errors and ensure data accuracy.

Single entry (D):Only one operator enters data.

Manual review (B):Involves post-entry checking, not during entry.

Third-party compare (C):Used for reconciling external data sources, not CRF data.

Hence,option A (Blind verification)is the correct and CCDM-defined process.

Reference (CCDM-Verified Sources):

SCDM GCDMP, Chapter: Data Entry and Data Tracking, Section 5.1 – Double Data Entry and Verification Methods

ICH E6(R2) GCP, Section 5.5.3 – Data Entry and Verification Controls

FDA Guidance for Industry: Computerized Systems Used in Clinical Investigations, Section 6.2 – Data Accuracy and Verification

In anElectronic Data Capture (EDC)system, even after a data manager completes all manual queries and marks data as "clean," the data may later appearuncleanifthe site (study coordinator)makes subsequent updates in the system after re-reviewing thesource documents.

According to theGood Clinical Data Management Practices (GCDMP, Chapter: Electronic Data Capture Systems), site users maintain the authority to modify data entries as long as the system remains open for data entry. TheEDC system audit trailcaptures such changes, which can automatically invalidate prior data reviews, triggering new discrepancies or changing system edit-check statuses.

This situation commonly occurs when the site identifies corrections in the source (e.g., wrong date or lab result) and updates the EDC form accordingly. These post-cleaning changes require additional review cycles to ensure the database reflects accurate and verified information before final lock.

Options B, C, and D are incorrect — CRAs and medical monitors cannot directly change EDC data; they can only raise queries or request updates.

Reference (CCDM-Verified Sources):

SCDM Good Clinical Data Management Practices (GCDMP), Chapter: Electronic Data Capture Systems, Section 6.3 – Post-Cleaning Data Changes and Audit Trails

ICH E6 (R2) GCP, Section 5.5.3 – Data Integrity and Change Control

FDA 21 CFR Part 11 – Electronic Records: Change Documentation Requirements

When theprimary efficacy endpointin a clinical trial is3-month survival, the key data element required is thedeath date. This is because the survival endpoint is determined by calculating whether the subject lived or died within a defined time frame from study enrollment or randomization.

According to theGCDMP (Chapter: Data Management Planning and Study Start-up), the Clinical Data Manager (CDM) must identify and ensure the capture of allcritical data elementsnecessary to evaluate the study endpoints. For time-to-event analyses (e.g., survival studies), accurateevent dates (death date)are essential for endpoint derivation and statistical analysis.

Other data elements such as cause of death or date of autopsy (options B and C) may support secondary analyses or safety reviews but are not necessary to determine the survival endpoint itself. Similarly,birth date(option D) contributes to demographic data but is unrelated to the primary efficacy outcome.

Reference (CCDM-Verified Sources):

SCDM Good Clinical Data Management Practices (GCDMP), Chapter: Data Management Planning and Study Start-up, Section 4.4 – Critical Data Identification for Endpoints

ICH E9 – Statistical Principles for Clinical Trials, Section 2.2.3 – Time-to-Event Data Considerations

FDA Guidance for Industry: Clinical Trial Endpoints for Drug Development

In the context of aninvestigator-initiated trial (IIT)whereStandard Operating Procedures (SOPs)are not available, the most appropriate and compliant approach is todevelop a Data Management Plan (DMP) templateand then create astudy-specific DMPbased on that template (Option C).

According to theGood Clinical Data Management Practices (GCDMP, Chapter on Data Management Planning and Study Start-up), theDMPis the central document that defines all processes, responsibilities, systems, and quality controls related to data collection, processing, validation, and database management throughout the clinical study. The DMP serves as a formal framework for ensuringdata integrity,traceability, andregulatory compliance, especially in the absence of established institutional SOPs.

While SOPs provide organizational-level standards, the DMP providesstudy-specific operational detail. In an investigator-initiated setting, researchers often lack institutional data management infrastructure, so the DMP must substitute for SOP guidance by detailing:

Data entry and validation procedures

Query management and resolution processes

CRF design and data flow specifications

Database design, backup, and security

Responsibilities of study personnel (investigator, data manager, statistician)

Quality control and audit trail practices

OptionA(“Data handling should be documented in a DMP”) is correct in principle but incomplete—without aDMP template, there is no standardized format or consistency across studies.

OptionB(developing full SOPs) is not practical for a single IIT; SOPs are organizational-level documents requiring longer development and approval cycles.

OptionD(briefly describing data management in the protocol) is insufficient, as the protocol should reference data management activities but not serve as the operational manual for them.

Therefore,Option Cprovides the most comprehensive, regulatory-compliant, and practical solution—ensuring structured documentation of all data management activities while maintaining flexibility for investigator-led research.

Reference (CCDM-Verified Sources):

Society for Clinical Data Management (SCDM), Good Clinical Data Management Practices (GCDMP), Chapter: Data Management Planning and Study Start-up, Section 5.2 – Data Management Plan (DMP) Development and Maintenance

ICH E6 (R2) Good Clinical Practice, Section 5.1 – Quality Management and Documentation Requirements

FDA Guidance for Industry: Computerized Systems Used in Clinical Investigations, Section 4 – Data Management and Documentation Practices

SCDM GCDMP, Chapter: Project Management in Data Management – Study-Specific Documentation and Planning in Investigator-Initiated Trials

The best response is thatdiary data to be source data verified should be selected using a risk-based approach.

According to theGCDMP (Chapter: Data Quality Assurance and Control)andFDA Guidance on Risk-Based Monitoring (RBM), not all data require full SDV. Electronic patient-reported outcome (ePRO) or mobile diary data are typicallydirect electronic source data (eSource)captured at the time of entry, which already ensures authenticity and traceability.

Arisk-based SDV approachfocuses verification efforts on data critical tosubject safety and primary efficacy endpoints, as defined in the study’sRisk Assessment PlanorMonitoring Plan. Random or full verification of low-risk data (like diary compliance metrics) adds unnecessary effort and cost.

Thus,Option Caligns with current regulatory expectations and data management best practices.

Reference (CCDM-Verified Sources):

SCDM Good Clinical Data Management Practices (GCDMP), Chapter: Data Quality Assurance and Control, Section 7.3 – Risk-Based Monitoring and SDV

ICH E6 (R2) Good Clinical Practice, Section 5.18 – Risk-Based Quality Management

FDA Guidance for Industry: Oversight of Clinical Investigations — A Risk-Based Approach to Monitoring (2013)

TheStudy Schedule of Events(SoE) section in the protocol is the most concise and comprehensive representation of thetiming of data collectionthroughout a study.

According to theGood Clinical Data Management Practices (GCDMP, Chapter: Data Management Planning and Study Start-up)andICH E6 (R2) GCP, the SoE outlines what assessments, procedures, and data collections occur at each study visit (e.g., screening, baseline, treatment visits, follow-up). This table is a foundational tool forCRF design,database structure, andedit-check development, ensuring alignment between the protocol and data management systems.

While thestudy endpoints section (A)defineswhatis measured, and theprotocol synopsis (C)summarizes the design, only theschedule of events (B)specifieswhendata collection occurs for each parameter. TheICH essential documents (D)pertain to regulatory documentation, not study visit timing.

Reference (CCDM-Verified Sources):

SCDM Good Clinical Data Management Practices (GCDMP), Chapter: Data Management Planning and Study Start-up, Section 4.1 – Using the Schedule of Events for Database Design

ICH E6 (R2) GCP, Section 6.3 – Trial Design and Schedule of Assessments

FDA Guidance for Industry: Protocol Design and Data Collection Standards

When aCRF modificationleads to a sudden increase inEDC queries, the most likely cause is an error or misconfiguration in theedit checksintroduced during or after the change. Therefore, the first step should be toreview the edit checks for correctness.

TheGCDMP (Chapter: Database Design and Validation)emphasizes that any database or CRF modification should triggerretesting of affected validation rules. Incorrect logic, thresholds, or missing conditional statements in automated edit checks can cause false or redundant queries, leading to unnecessary data management burden and site frustration.

Manually handling edit checks (option A) or adding SDV (option B) does not address the root cause. Having monitors close queries (option D) would mask the problem rather than resolve it.

Thus, the correct corrective measure isOption C — review and validate the edit checksto ensure proper functionality.

Reference (CCDM-Verified Sources):

SCDM Good Clinical Data Management Practices (GCDMP), Chapter: Database Design and Validation, Section 5.5 – Edit Check Testing and Review

ICH E6 (R2) GCP, Section 5.5.3 – Validation and Change Control for Electronic Systems

FDA 21 CFR Part 11 – System Validation and Change Documentation

When initially assessingdata cleanlinessin preparation for aninterim analysis, the focus should be onoutstanding issuesthat could affect data completeness and reliability.

According to theGCDMP (Chapter: Data Quality Assurance and Control), key indicators of readiness include:

TheCRF data entry statusof received pages (option A) to confirm completeness.

Identification ofmissing pages or visits(option B) to verify subject-level completeness.

A listing ofoutstanding discrepancies and their aging(option D) to assess unresolved data issues.

Counting the number ofdiscrepancies resolved to date (option C), however, does not reflect data quality or current data readiness—it indicates past actions rather than current unresolved risks. Therefore, it isnot a valid measurefor assessing interim data cleanliness.

Reference (CCDM-Verified Sources):

SCDM Good Clinical Data Management Practices (GCDMP), Chapter: Data Quality Assurance and Control, Section 6.1 – Data Readiness Assessments for Analysis

ICH E6 (R2) GCP, Section 5.18.4 – Ongoing Data Quality Review

FDA Guidance for Industry: Oversight of Clinical Investigations – Risk-Based Monitoring, Section 7 – Data Quality Indicators

TheData Manager (DM)plays a critical role in maintaining transparent communication with the clinical study team regardingdata quality and study progress. One of the most essential metrics regularly reported by the DM is thepercentage of data entered and cleaned.

According to theGood Clinical Data Management Practices (GCDMP, Chapter: Communication and Study Reporting), these metrics provide insight into study status, data readiness for interim analysis, and timeline predictability for database lock. Regular communication includes:

Percent of CRFs entered and verified

Percent of queries resolved

Outstanding data issues or missing pages

Other options fall outside the Data Manager’s direct responsibility:

A (Enrollment)is typically reported by clinical operations.

B (Staffing changes)are handled by site management.

D (Safety events)are communicated by the safety/pharmacovigilance team.

Thus,option Ccorrectly reflects the Data Manager’s responsibility for ongoing study communication.

Reference (CCDM-Verified Sources):

SCDM GCDMP, Chapter: Communication and Study Reporting, Section 5.3 – Study Metrics and Status Updates

ICH E6(R2) GCP, Section 5.1.1 – Communication and Oversight in Quality Management

FDA Guidance for Industry: Computerized Systems Used in Clinical Investigations, Section 6.5 – Data Status Reporting

InElectronic Data Capture (EDC)systems, free text fields should be usedonly when a predefined list of acceptable responses cannot accommodate the full variability of input data— most notably forAdverse Event (AE) verbatim terms.

According to theGood Clinical Data Management Practices (GCDMP, Chapter: CRF Design and Data Collection), AE verbatim terms are initially entered asfree textby site staff to accurately capture the investigator’s exact medical description of the event. These verbatim terms are latercodedusing standardized dictionaries such asMedDRAduring medical coding, ensuring both flexibility and standardization in reporting.

Conversely, fields such asurine sedimentation rate (A),date of birth (C), andBody Mass Index (D)requirestructured numeric or date formatsto enable validation, range checks, and consistency across datasets. Free text would compromise data integrity, accuracy, and validation efficiency for these structured data elements.

Reference (CCDM-Verified Sources):

SCDM Good Clinical Data Management Practices (GCDMP), Chapter: CRF Design and Data Collection, Section 4.3 – Use of Free Text and Coded Fields

ICH E6 (R2) Good Clinical Practice, Section 5.5.3 – Data Structure and Validation

MedDRA Introductory Guide, Section 2.3 – Verbatim Entry and Coding Requirements

Aself-evident correction (SEC)refers to a data correction that is obvious, logical, and unambiguous — such as correcting an impossible date (e.g., 31-APR-2024) or standardizing a known abbreviation (e.g., “BP” to “Blood Pressure”). According to theGood Clinical Data Management Practices (GCDMP), SECs can be applied by data management stafffollowing pre-approved conventions defined in the Data Management Plan (DMP).

The DMP should explicitly describe the criteria for SECs, including the types of errors eligible for this correction method, the required documentation, and the communication procedure to inform the investigative site. The process must maintainaudit trail transparencyand ensure that all changes aretraceable and justified.

Options A and B suggest unauthorized or informal change procedures, which violate audit and compliance standards. Option C is too restrictive, as it prevents the efficient correction of non-clinical transcription or formatting errors.

Therefore,option Dis correct:“Self-evident changes may be made per the listed conventions and documented to the investigative site.”This approach aligns with CCDM expectations for balancing efficiency, accuracy, and regulatory compliance.

Reference (CCDM-Verified Sources):

SCDM GCDMP, Chapter: Data Validation and Cleaning, Section 6.2 – Self-Evident Corrections

FDA 21 CFR Part 11 – Electronic Records; Audit Trails and Traceability Requirements

Thebest practicewhen implementing aprotocol amendmentthat affects CRF content or data collection timing is toupdate the eCRF completion guidelines and notify sites before implementing the change.

According to theGCDMP (Chapter: CRF Design and Data Collection), theeCRF Completion Guidelines (eCRF CG)are an essential study tool that instructs site personnel on accurate and consistent data entry. When new data elements or collection time points are added, the guidelines must berevised, version-controlled, and communicatedto all users prior to implementation to ensure sites collect and enter data correctly.

Simply relying on the revised CRF (option C) or updating the document without notification (option B) violates communication and training standards. Likewise, notifying sites without updating the documentation (option D) leaves insufficient reference material for data entry compliance.

Reference (CCDM-Verified Sources):

SCDM Good Clinical Data Management Practices (GCDMP), Chapter: CRF Design and Data Collection, Section 5.5 – Managing CRF Revisions and Site Communication

ICH E6 (R2) GCP, Section 5.18.4 – Communication of Protocol Amendments and Documentation Updates

FDA Guidance for Industry: Electronic Source Data in Clinical Investigations, Section 4.3 – Site Communication and Documentation Management

Thebest methodto identify sites withhigh subject attritionis to calculate theproportion of patients for which two visit periods have passed without data, by site.

According to theGCDMP (Chapter: Data Quality Assurance and Control), subject attrition is an important performance indicator for data completeness and site compliance. Evaluating missing or delayed data acrossmultiple consecutive visit periodsallows for early detection of potential dropouts or site-level operational issues.

By assessing this proportion at thesite level, the Data Manager can distinguish between random missing data and systematic site underperformance. Counting or proportioning late visits (options B and C) identifies scheduling delays, not attrition. Looking at missing data without site context (option D) fails to identify site-specific patterns, limiting corrective action.

This metric aligns withrisk-based monitoring (RBM)practices recommended byICH E6 (R2)andFDA RBM Guidance, which promote proactive identification of sites at risk of data loss.

Reference (CCDM-Verified Sources):

SCDM Good Clinical Data Management Practices (GCDMP), Chapter: Data Quality Assurance and Control, Section 5.4 – Site Performance Metrics

ICH E6 (R2) Good Clinical Practice, Section 5.18 – Monitoring and Site Performance Evaluation

FDA Guidance for Industry: Oversight of Clinical Investigations – Risk-Based Monitoring, Section 6 – Site Performance Metrics

Standard Operating Procedures (SOPs)are formal, controlled documents that definestandardized processesto ensure clinical trials are conducted in compliance withGood Clinical Practice (GCP), the study protocol, and regulatory requirements (such as ICH and FDA).

According toGood Clinical Data Management Practices (GCDMP)andICH E6(R2) GCP, SOPs are fundamental to quality management systems. They describehowtasks are performed, ensuring consistency, accountability, and traceability across all studies and team members. Proper adherence to SOPs guarantees that data areaccurately generated, documented, and reportedin compliance with ethical and regulatory standards.

Other options serve different purposes:

SAP (B)defines statistical methodology, not compliance control.

DMP (C)focuses on study-specific data handling, not organizational compliance.

CRFs (D)are tools for data collection but do not enforce compliance by themselves.

Therefore,option A (SOP)is correct.

Reference (CCDM-Verified Sources):

SCDM GCDMP, Chapter: Quality Management and Compliance, Section 5.1 – Role of SOPs in Regulatory Compliance

ICH E6(R2) GCP, Section 2.13 and 5.1.1 – Quality Management Systems and SOP Requirements

FDA 21 CFR Part 312.50 – Sponsor Responsibilities and Compliance Systems

Duringdata reconciliationbetween a central laboratory and CRF data, the source of truth is typically thecentral lab database, as it provides directly measured, vendor-generated results.

When thecentral lab has data but the CRF does not (option C), the Data Manager must first query thecentral labto confirm that the result was transmitted correctly, since discrepancies may stem from data processing or timing issues. Once confirmed, a secondary query may be issued to the site to ensure CRF completion and alignment.

Conversely, if the CRF contains data but the central lab is missing results (option B), the issue is site-level, not vendor-level.

According to theGCDMP (Chapter: External Data Transfers and Reconciliation),priority for querying depends on the authoritative source— for lab data, thecentral labis considered the source of record.

Therefore,option Cis correct.

Reference (CCDM-Verified Sources):

SCDM GCDMP, Chapter: External Data Transfers and Reconciliation, Section 6.1 – Reconciliation of Central Lab and CRF Data

ICH E6(R2) GCP, Section 5.5.3 – Source Data Verification and Vendor Reconciliation

FDA Guidance for Industry: Computerized Systems Used in Clinical Investigations, Section 6.4 – Data Reconciliation and Traceability

In arelational database structure, each record in a table is uniquely identified by aprimary key. In this case, theVITAL_SIGNStable uses acomposite primary keyconsisting of:

Patient Identifier,

Visit Number, and

Vital Signs Parameter Name.

This means each record represents aunique measurement of a specific parameter (e.g., blood pressure, pulse)for a patient at a specific visit.

When joiningPATIENT_DEMOGRAPHYandVITAL_SIGNStables on thepatient identifier, the result set will includeone record for every combination of patient, visit, and parameter— i.e.,one record per patient per visit per vital sign parameter.

Therefore,option Ccorrectly describes the expected number of records.

Reference (CCDM-Verified Sources):

SCDM GCDMP, Chapter: Database Design and Build, Section 5.2 – Primary and Foreign Key Relationships in Relational Models

CDISC SDTM Implementation Guide, Section 5.3 – Observation-Level Data Structures

ICH E6(R2) GCP, Section 5.5.3 – Data Organization and Integration Principles

Enrolling subjects before theElectronic Data Capture (EDC) systemis ready poses majordata integrity and compliance risks. The primary issue is thatdata cannot be accurately captured, validated, or monitoredwithout the system in place.

Per theGCDMP (Chapter: Data Management Planning and Study Start-up), data collection systems must befully validated, tested, and releasedbefore enrollment begins to ensure:

Real-time data entry and quality control

Proper tracking of adverse events (AEs/SAEs)

Audit trails and traceability for regulatory compliance

Option A highlights the most critical consequence — without an operational EDC, data collection and verification processes cannot occur, compromising data quality and study oversight.

While options B, C, and D may be partially true, they aresecondary effects. The fundamental consideration is data capture capability and monitoring control, makingoption Acorrect.

Reference (CCDM-Verified Sources):

SCDM GCDMP, Chapter: Data Management Planning and Study Start-up, Section 4.2 – EDC Readiness and System Validation

ICH E6(R2) GCP, Section 5.5.3 – Computerized Systems Validation Before Use

FDA Guidance for Industry: Computerized Systems Used in Clinical Investigations, Section 6.1 – System Qualification Prior to Data Entry

When designing or configuringdata entry screenswithin an Electronic Data Capture (EDC) system, three critical components are required for each field:

Data Type– Defines the nature of the data (e.g., text, numeric, date).

Prompt– The label or question displayed to the user.

Response Format– Specifies how the user enters or selects data (e.g., free text, drop-down, checkbox).

According to theGCDMP (Chapter: EDC Systems and Database Design), these three attributes form thelogical data structurerequired to build and validate data entry interfaces. They ensure consistency in how information is captured, displayed, and validated during data entry.

Whileuser roles (A)andhelp text (D)are system-level configurations, not field-level specifications,page numbers (C)relate to printed CRFs rather than digital data screens.

Therefore,option B (Data type, prompt, and response format)correctly identifies the essential information needed to define data entry screens.

Reference (CCDM-Verified Sources):

SCDM GCDMP, Chapter: EDC Systems and Database Design, Section 4.3 – Screen Design Specifications

CDISC CDASH Implementation Guide, Section 3.2 – Data Field Attributes

ICH E6(R2) GCP, Section 5.5.3 – Data Capture and Input Standards

In a relational database system used in clinical data management, performance refers to how efficiently the system processes transactions, retrieves data, and handles large volumes of information without delay or data integrity issues. Among the listed options, loading a large lab data file into the database (Option B) has the most significant impact on database performance.

According to theGood Clinical Data Management Practices (GCDMP, Chapter on Database Design and Build), the bulk data load process — such as importing large external datasets (e.g., central lab data, ECG results, or imaging metadata) — can be computationally intensive. This process engages the database’s input/output (I/O) subsystem, indexing mechanisms, and transaction logs simultaneously, often locking tables temporarily and consuming significant memory and processing resources.

Unlike standard CRF data entry (Option A) or record updates (Option D), which are incremental and typically processed in smaller transactional batches, bulk loading operations handle thousands or millions of rows at once. If not optimized (e.g., via staging tables, indexing strategies, or commit frequency control), such operations can degrade system performance, slow down concurrent user access, and increase the risk of transaction failure.

Executing a properly designed query (Option C) can also be resource-intensive depending on data volume and join complexity, but when queries are properly optimized (using indexed keys, efficient SQL joins, and selective retrieval), their impact is generally controlled and transient compared to large data imports.

Therefore, as outlined in theGCDMP Database Design and BuildandFDA Computerized Systems Guidance, the most performance-impacting activity in a relational database is bulk loading large external datasets, making Option B the correct answer.

Reference (CCDM-Verified Sources):

Society for Clinical Data Management (SCDM), Good Clinical Data Management Practices (GCDMP), Chapter: Database Design and Build, Section 6.7 – Database Performance and Optimization

FDA Guidance for Industry: Computerized Systems Used in Clinical Investigations, Section 6 – System Performance and Data Handling Efficiency

ICH E6 (R2) Good Clinical Practice, Section 5.5 – Data Handling and Record Integrity

CDISC Operational Data Model (ODM) Implementation Guide – Bulk Data Transfer and Validation Considerations

Even when allrangeandlogic checksare successfully resolved, discrepancies may still exist between theclinical databaseand thesource documents. This typically indicates anerror in data abstraction or transcription, meaning that data were incorrectly entered or extracted from the source records during the data entry or verification process.

According to theGood Clinical Data Management Practices (GCDMP, Chapter on Data Validation and Cleaning),data validation rulessuch as range and logic checks are designed to identify inconsistencies, missing data, or out-of-range valueswithin the databaseitself. However, they donot verify the accuracy of data entry against the original source documents— that responsibility falls undersource data verification (SDV), typically conducted by clinical monitors or auditors.

When an auditor detects discrepancies between source and database values after all edit checks have passed, the most probable explanation is thatdata were not transcribed correctly from the source, rather than a failure in programmed edit checks. This could occur due to human error during manual data entry, misinterpretation of the source document, or oversight during SDV.

OptionC (Data were changed after checks were run)might occur in rare cases but would normally be documented in an audit trail per21 CFR Part 11andICH E6 (R2)standards. OptionBmisinterprets the issue, since “logical and in range” values can still be incorrect relative to the source. OptionA (Auditor error)is possible but statistically less likely, as source data verification follows strict, documented audit procedures.

Therefore, themost likely reasonfor such discrepancies isOption D: Data were not abstracted correctly from the source, emphasizing the importance of robust data entry training, dual data entry, and verification procedures.

Reference (CCDM-Verified Sources):

Society for Clinical Data Management (SCDM), Good Clinical Data Management Practices (GCDMP), Chapter: Data Validation and Cleaning, Section 6.5 – Source Data Verification and Reconciliation

ICH E6 (R2) Good Clinical Practice, Section 5.18 – Monitoring and Source Data Verification

FDA Guidance for Industry: Computerized Systems Used in Clinical Investigations, Section 6 – Source Data Accuracy and Audit Trails

21 CFR Part 11 – Electronic Records and Electronic Signatures, Subpart B: Audit Trails and Record Accuracy

When importing external data (e.g., laboratory or imaging results) into a clinical database, thefirst stepin data import quality control is to verify thatincoming files conform to the pre-specified data transfer specifications (DTS).

According to theGCDMP (Chapter: External Data Transfers and Integration), theData Transfer Specificationdefines file structure, variable names, data types, delimiters, record counts, and validation rules. The initial import check confirms that the received file matches the technical and structural requirements before content or record consistency is evaluated.

Subsequent checks—such as record counts (A), data consistency with existing database (C), and internal logical consistency (D)—are performed only after the file structure is validated and confirmed to match the specifications. Failure to perform this first check may cause import errors or corrupted data loads.

Thus, thefirst and most critical verification stepis ensuringfile conformity to the agreed data transfer specifications, makingoption Bcorrect.

Reference (CCDM-Verified Sources):

SCDM GCDMP, Chapter: External Data Transfers, Section 4.2 – Data Transfer File Validation and Import Checks

ICH E6(R2) GCP, Section 5.5.3 – Validation of Computerized Systems and Data Imports