C100DBA MongoDB Associate Database Administrator(Based on MongoDB 6.0 and 7.0+) Questions and Answers

MongoDB exposes server parameters that can be configured through the parameter framework. Among the choices, --setParameter is the option specifically designed to set a server parameter on the mongod command line, using syntax such as mongod --setParameter < name > = < value > . A configuration file can also contain a setParameter section, but --config by itself merely tells mongod which configuration file to load; it is not the parameter-setting mechanism identified by the question. Connection-string options control how clients connect and cannot be used to configure arbitrary server runtime parameters. Option D is also incorrect because the server must already be running for mongosh to issue administrative commands. A technical nuance is important: parameters that support modification while the process is already running are changed with the administrative setParameter command, for example db.adminCommand({setParameter:1, ...}). Such runtime changes may not persist across a restart unless the value is also configured for startup. Given the options provided and the exam wording, --setParameter is the intended method, so A is correct.

Study Guide reference/topic:MongoDB Architecture and Core Operations - server parameters, --setParameter, configuration files, and runtime administration.

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mongosh, the MongoDB Shell, is the interactive command-line environment used to connect to MongoDB deployments and perform administrative and data operations. Through mongosh, a DBA can run CRUD queries, aggregation pipelines, user and role administration, replica-set commands, server diagnostics, index operations, and many other management tasks. mongod, option B, is the MongoDB database server process itself; it stores data and serves requests but is not the interactive administration client. mongostat, option C, is a monitoring utility that reports a concise stream of server activity metrics such as operation rates, memory, connections, and queue information. mongotop, option D, reports time spent reading and writing data by namespace and is likewise a diagnostic utility rather than a general administration shell. DBAs may also use MongoDB Compass, Atlas interfaces, Ops Manager, or automation tooling depending on the deployment, but among these four command-line tools, mongosh is the one intended for broad interactive access and administration. Therefore option A is correct.

Study Guide reference/topic:MongoDB Architecture and Core Operations - MongoDB Shell, administration tools, server processes, and monitoring utilities.

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Transport Layer Security protects MongoDB network communications while data travels between clients and servers and, when configured, between MongoDB deployment members. Its core purpose is therefore encryption of data in transit. TLS helps prevent passive interception and tampering and also supports certificate-based identity validation. It is separate from encryption at rest, which protects files on storage media, and separate from Queryable Encryption or client-side field encryption, which protects selected application data before it reaches the server. Option A is unrelated: backup and restore operations use snapshot, managed backup, or database-tool mechanisms. Option B describes the objective of replication, elections, and redundant deployment architecture, not TLS. Option C is also incorrect; TLS is a security control, not a query-optimization mechanism, and cryptographic processing can add some overhead rather than improving query execution. A secure MongoDB deployment normally combines TLS with strong authentication, least-privilege authorization, appropriate network exposure controls, auditing, and storage or field-level encryption as required. For the specific question, enabling TLS provides encryption for data moving across the network, making option D correct.

Study Guide reference/topic:Security, Networking, and Encryption - TLS/SSL, transport encryption, certificates, and network security.

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The query contains equality predicates on both city and occupation, so an efficient compound index should contain both fields. Options A and C include only one query predicate plus the unrelated name field, forcing MongoDB to perform additional filtering that the index cannot resolve. Options B and D both contain the complete predicate set. For a query consisting entirely of equality predicates, MongoDB does not require those equality keys to appear in a particular order relative to one another for the query itself; { city: 1, occupation: 1 } and { occupation: 1, city: 1 } can both support the two equality conditions effectively. The original question appears to expect B because it mirrors the field order used in the query, but that distinction is not technically required under MongoDB ' s equality-key guidance. Field order becomes much more significant when the workload also needs a particular index prefix, sort order, range predicate, or when one ordering better supports additional common queries. Accordingly, the technically accurate review result is B and D, with B being the likely intended exam choice.

Study Guide reference/topic:Indexing and Query Optimization - compound indexes, equality predicates, and index key ordering.

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The outer $or returns a document if either branch is true. Document 1 satisfies the first branch because product equals " x-small box " , so it is included regardless of the second branch. The nested $and requires both qty != 50 and the absence of " brown " from the colors array. Document 2 has quantity 60 but contains " brown " , so it fails $nin. Document 3 has quantity 45 and its colors do not include brown, so it satisfies both conditions. Document 4 has no brown but quantity is exactly 50, so it fails $ne: 50. Document 5 has quantity 10 and colors [ ' grey ' , ' white ' ], so it also satisfies both nested predicates. The final set is therefore documents 1, 3, and 5. This question also illustrates that MongoDB applies an implicit AND between multiple top-level field predicates in the same document, but here the explicit $and makes the required conjunction clear. Option D is the only answer containing exactly the matching documents.

Study Guide reference/topic:MongoDB Architecture and Core Operations - logical query operators, $or, $and, $ne, and $nin.

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A hidden index remains physically present and continues to be maintained as documents are inserted, updated, or deleted, but the query planner does not consider that index when choosing normal execution plans. This makes hidden indexes especially useful for safely testing whether an index can be removed. An administrator can hide the index and observe production query behavior as though the index were absent. If performance or plan quality degrades, the index can be unhidden immediately without rebuilding it because MongoDB has continued to maintain it in the background. Option A is incorrect because a hidden index is still an index and is not simply removed from index statistics. Option B is also incorrect; hiding an index can actually force collection scans or alternative plans if that index was previously important. Option D misunderstands the feature entirely: hiding an index has no effect on document visibility. Hidden indexes are therefore a risk-reduction mechanism for index lifecycle management, especially before dropping an index in a production environment. Option C accurately states that purpose.

Study Guide reference/topic:Indexing and Query Optimization - hidden indexes, query planner behavior, and safe index-removal testing.

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db.collection.stats() returns statistics for a specific collection, including storage-related values that help a DBA understand that collection ' s disk footprint. Therefore, for a collection named order, db.order.stats() is the relevant command among the choices. The result can include metrics such as logical data size, allocated storage size, document count, index information, and related collection statistics depending on version and storage engine. Option A, db.stats(), reports database-level statistics aggregated across the database rather than isolating the order collection. db.hostInfo(), option C, reports host and operating-system information rather than collection storage consumption. db.serverStatus(), option D, provides broad server-process runtime metrics such as connections, memory, operation counters, replication/storage-engine information, and other health data, but it is not the collection-specific disk-utilization interface requested here. Administrators can also use dedicated helpers such as collection storage-size or total-size methods when they need a more narrowly defined measurement. For a general collection-level statistics query targeting order, however, db.order.stats() is the correct answer, making option B correct.

Study Guide reference/topic:Performance Monitoring and Tuning - collection statistics, storage utilization, db.collection.stats(), and database/server metrics.

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updateMany() distinguishes between documents that match the filter and documents whose stored data actually changes. The filter { diagnosis: " Hypertension " } matches Carlos Martinez and Maria Lopez, so matchedCount is 2. Carlos currently has room: 203, and $set: { room: 207 } changes his document, contributing one to modifiedCount. Maria already has room: 207. She still satisfies the filter and therefore counts as a match, but setting a field to the value it already contains does not produce a material modification, so she does not increment modifiedCount. Juan Perez has diagnosis Arrhythmia, so he is neither matched nor modified. The resulting acknowledged operation therefore reports matchedCount: 2 and modifiedCount: 1, corresponding to option D. This distinction is essential when validating whether an update actually changed stored values rather than merely identifying matching records. This distinction is operationally important: a large difference between matched and modified counts can indicate that many matching documents already contained the requested state, not that the update failed. DBAs should use both counters when validating bulk-update behavior.

Study Guide reference/topic:MongoDB Architecture and Core Operations - updateMany(), $set, matchedCount, and modifiedCount.

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db.currentOp() is the mongosh helper used to inspect operations currently executing on a MongoDB server. Passing true requests a broad view that includes all in-progress operations as well as additional idle/system entries, so it can be used when the administrator wants to see the complete current-operation picture. The database-command form, db.adminCommand({ currentOp: true }), invokes the corresponding administrative command and is also valid for obtaining in-progress operation information. Option A adds a filter restricting the result to operations whose op field is " command " ; it therefore does not represent all operation types. Option B is not valid currentOp command syntax, and option E uses an all field that is not the documented special form for this helper. In modern MongoDB administration, the $currentOp aggregation stage is generally preferred for richer filtering and aggregation, but the question is testing the traditional helper and command interfaces. Therefore, C and D are the two valid choices among those presented.

Study Guide reference/topic:Performance Monitoring and Tuning - current operations, currentOp, and administrative diagnostics.

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Queryable Encryption is designed for client-side protection of sensitive fields while preserving supported query capabilities over encrypted values. With this model, appropriately configured client drivers encrypt selected fields before data is transmitted to the MongoDB server and decrypt authorized results on the client side. That architecture is well suited to highly sensitive PII such as government identification numbers, dates of birth, or regulated demographic information because the database server stores encrypted field values rather than plaintext for those protected fields. The Encrypted Storage Engine, option A, protects database files at rest but the server can still process decrypted data internally. TLS/SSL, option B, protects data while it is moving across the network, not the application field values from the database server itself. Role-Based Access Control, option C, restricts which authenticated principals can perform actions but does not itself cryptographically protect the field contents. Queryable Encryption complements, rather than replaces, TLS, authentication, authorization, auditing, and storage encryption. For the specific requirement of client-side encryption of sensitive application data, option D is the correct MongoDB feature.

Study Guide reference/topic:Security, Networking, and Encryption - Queryable Encryption, client-side encryption, sensitive fields, and key management.

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MongoDB write concern defines the level of acknowledgment requested for a write. Setting the numeric value w: 1 means that a write is considered acknowledged after the primary has accepted it; no secondary acknowledgment is required. In a replica set with one primary and two secondaries, those secondaries can replicate the operation asynchronously after the client has already received success. That configuration is represented by { writeConcern: { w: 1 } }, making option D correct. The numeric type matters: w can be specified as an integer count or certain strings such as " majority " , but the string " 1 " is not the intended numeric node count shown by the question. Options B and C also omit the w field structure expected inside a write concern document. The durability trade-off is important: a w:1 write can be acknowledged before any secondary has replicated it, so a sudden primary failure before replication may result in that write being rolled back after failover. If the workload requires stronger durability, w: " majority " is commonly used. For exactly one acknowledging node, however, the correct setting is D.

Study Guide reference/topic:Cluster Reliability and Data Resilience - write concern, w acknowledgments, replica-set durability, and rollback risk.

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db.collection.insertOne() inserts one BSON document into a collection. In mongosh, string values must be quoted and Boolean literals use JavaScript syntax: lowercase true or false. Therefore the student ' s name must appear as " Randall " , and the passed flag must be true. Option D is the only choice that gets both value types correct. Option A quotes the string properly but uses True, which is not the JavaScript Boolean literal. Options B and C leave Randall unquoted, so mongosh would interpret it as an identifier or variable rather than as the literal string required by the document. Option C additionally uses the invalid capitalized Boolean form. If an _id field is omitted, MongoDB automatically adds a unique _id, typically an ObjectId, before storing the document. That behavior does not affect the two requested fields. This question tests both CRUD syntax and the BSON/JavaScript literal representation used by mongosh. The correct command is db.class101.insertOne({ student: " Randall " , passed: true }), so option D is correct.

Study Guide reference/topic:MongoDB Architecture and Core Operations - insertOne(), BSON values, strings, Booleans, and automatic _id.

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The supported mongosh helpers for replica-set membership changes are rs.remove() and rs.add(). rs.remove(hostname) removes the member whose host string matches the supplied host:port, while rs.add() adds a new member using either a host string or a member configuration document that includes a host field. Option C uses those documented methods in the required sequence: remove node 1, remove node 2, then add node 1 back. The names rs.removeNode() and rs.addNode() shown in the other options are not standard MongoDB replica-set helper methods. Membership changes must be performed carefully because modifying voting or data-bearing membership can affect majority calculations, elections, and availability. Administrators normally execute membership changes while connected to the current primary, allow configuration propagation, and verify replica-set health afterward with rs.status() or related diagnostics. If a removed data-bearing node is later re-added, it may need to synchronize depending on how far its data has diverged from the current replica-set history. Among the choices, option C is the only valid command sequence.

Study Guide reference/topic:Cluster Reliability and Data Resilience - replica-set membership, rs.remove(), rs.add(), and topology changes.

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