DSA-C02 SnowPro Advanced: Data Scientist Certification Exam Questions and Answers

Explanation

What are outliers?

Outliers are the values that look different from the other values in the data. Below is a plot high-lighting the outliers in ‘red’ and outliers can be seen in both the extremes of data.

Reasons for outliers in data

Errors during data entry or a faulty measuring device (a faulty sensor may result in extreme readings).

Natural occurrence (salaries of junior level employees vs C-level employees)

Problems caused by outliers

Outliers in the data may causes problems during model fitting (esp. linear models).

Outliers may inflate the error metrics which give higher weights to large errors (example, mean squared error, RMSE).

Z-score method is of the method for detecting outliers. This methodis generally used when a variable’ distribution looks close to Gaussian. Z-score is the number of standard deviations a value of a variable is away from the variable’ mean.

Z-Score = (X-mean) / Standard deviation

IQR method , Box plots are some more example of methods used to detect data outliers in Data science.

Explanation

Window Functions

A window function operates on a group (“window”) of related rows.

Each time a window function is called, it is passed a row (the current row in the window) and the window of rows that contain the current row. The window function returns one output row for each input row. The output depends on the individual row passed to the function and the values of the other rows in the window passed to the function.

Some window functions are order-sensitive. There are two main types of order-sensitive window functions:

Rank-related functions.

Window frame functions.

Rank-related functions list information based on the “rank” of a row. For example, if you rank stores in descending order by profit per year, the store with the most profit will be ranked 1; the second-most profitable store will be ranked 2, etc.

Window frame functions allow you to perform rolling operations, such as calculating a running total or a moving average, on a subset of the rows in the window.

Explanation

All are correct!

About the Snowflake Marketplace

You can use the Snowflake Marketplace to discover and access third-party data and services, as well as market your own data products across the Snowflake Data Cloud.

As a data provider, you can use listings on the Snowflake Marketplace to share curated data offer-ings with many consumers simultaneously, rather than maintain sharing relationships with each indi-vidual consumer. With Paid Listings, you can also charge for your data products.

As a consumer, you might use the data provided on the Snowflake Marketplace to explore and ac-cess the following:

Historical data for research, forecasting, and machine learning.

Up-to-date streaming data, such as current weather and traffic conditions.

Specialized identity data for understanding subscribers and audience targets.

New insights from unexpected sources of data.

The Snowflake Marketplace is available globally to all non-VPS Snowflake accounts hosted on Amazon Web Services, Google Cloud Platform, and Microsoft Azure, with the exception of Mi-crosoft Azure Government. Support for Microsoft Azure Government is planned.

Explanation

Every machine learning task can be broken down to either Regression or Classification, just like the performance metrics.

Metrics are used to monitor and measure the performance of a model (during training and testing), and do not need to be differentiable.

Regression metrics

Regression models have continuous output. So, we need a metric based on calculating some sort of distance between predicted and ground truth.

In order to evaluate Regression models, we’ll discuss these metrics in detail:

· Mean Absolute Error (MAE),

· Mean Squared Error (MSE),

· Root Mean Squared Error (RMSE),

· R² (R-Squared).

Mean Squared Error (MSE)

Mean squared error is perhaps the most popular metric used for regression problems. It essentially finds the average of the squared difference between the target value and the value predicted by the regression model.

Few key points related to MSE:

· It’s differentiable, so it can be optimized better.

· It penalizes even small errors by squaring them, which essentially leads to an overestimation of how bad the model is.

· Error interpretation has to be done with squaring factor(scale) in mind. For example in our Boston Housing regression problem, we got MSE=21.89 which primarily corresponds to (Prices)².

· Due to the squaring factor, it’s fundamentally more prone to outliers than other metrics.

Mean Absolute Error (MAE)

Mean Absolute Error is the average of the difference between the ground truth and the predicted values.

Few key points for MAE

· It’s more robust towards outliers than MAE, since it doesn’t exaggerate errors.

· It gives us a measure of how far the predictions were from the actual output. However, since MAE uses absolute value of the residual, it doesn’t give us an idea of the direction of the error, i.e. whether we’re under-predicting or over-predicting the data.

· Error interpretation needs no second thoughts, as it perfectly aligns with the original degree of the variable.

· MAE is non-differentiable as opposed to MSE, which is differentiable.

Root Mean Squared Error (RMSE)

Root Mean Squared Error corresponds to the square root of the average of the squared difference between the target value and the value predicted by the regression model.

Few key points related to RMSE:

· It retains the differentiable property of MSE.

· It handles the penalization of smaller errors done by MSE by square rooting it.

· Error interpretation can be done smoothly, since the scale is now the same as the random variable.

· Since scale factors are essentially normalized, it’s less prone to struggle in the case of outliers.

R² Coefficient of determination

R² Coefficient of determination actually works as a post metric, meaning it’s a metric that’s calcu-lated using other metrics.

The point of even calculating this coefficient is to answer the question “How much (what %) of the total variation in Y(target) is explained by the variation in X(regression line)”

Few intuitions related to R² results:

If the sum of Squared Error of the regression line is small => R² will be close to 1 (Ideal), meaning the regression was able to capture 100% of the variance in the target variable.

Conversely, if the sum of squared error of the regression line is high=> R² will be close to 0, meaning the regression wasn’t able to capture any variance in the target variable.

You might think that the range of R² is (0,1) but it’s actually (-∞,1)because the ratio of squared errors of the regression line and mean can surpass the value 1 if the squared error of regression line is too high (>squared error of the mean).

Classification metrics

Classification problems are one of the world’s most widely researched areas. Use cases are present in almost all production and industrial environments. Speech recognition, face recognition, textclassification – the list is endless.

Classification models have discrete output, so we need a metric that compares discrete classes in some form. Classification Metrics evaluate a model’s performance and tell you how good or bad the classification is, but each of them evaluates it in a different way.

So in order to evaluate Classification models, we’ll discuss these metrics in detail:

Accuracy

Confusion Matrix (not a metric but fundamental to others)

Precision and Recall

F1-score

AU-ROC

Accuracy

Classification accuracy is perhaps the simplest metric to use and implement and is defined as the number of correct predictions divided by the total number of predictions, multiplied by 100.

We can implement this by comparing ground truth and predicted values in a loop or simply utilizing the scikit-learn module to do the heavy lifting for us (not so heavy in this case).

Confusion Matrix

Confusion Matrix is a tabular visualization of the ground-truth labels versus model predictions. Each row of the confusion matrix represents the instances in a predicted class and each column represents the instances in an actual class. Confusion Matrix is not exactly a performance metric but sort of a basis on which other metrics evaluate the results.

Each cell in the confusion matrix represents an evaluation factor. Let’s understand these factors one by one:

· True Positive(TP) signifies how many positive class samples your model predicted correctly.

· True Negative(TN) signifies how many negative class samples your model predicted correctly.

· False Positive(FP) signifies how many negative class samples your model predicted incorrectly. This factor represents Type-I error in statistical nomenclature. This error positioning in the confusion matrix depends on the choice of the null hypothesis.

· False Negative(FN) signifies how many positive class samples your model predicted incorrectly. This factor represents Type-II error in statistical nomenclature. This error positioning in the confu-sion matrix also depends on the choice of the null hypothesis.

Precision

Precision is the ratio of true positives and total positives predicted

Recall/Sensitivity/Hit-Rate

A Recall is essentially the ratio of true positives to all the positives in ground truth.

Precision-Recall tradeoff

To improve your model, you can either improve precision or recall – but not both! If you try to re-duce cases of non-cancerous patients being labeled as cancerous (FN/type-II), no direct effect will take place on cancerous patients being labeled as non-cancerous.

F1-score

The F1-score metric uses a combination of precision and recall. In fact, the F1 score is the harmonic mean of the two.

AUROC (Area under Receiver operating characteristics curve)

Better known as AUC-ROC score/curves. It makes use of true positive rates(TPR) and false posi-tive rates(FPR).

Explanation

Secure Data Sharing lets you share selected objects in a database in your account with other Snow-flake accounts. You can share the following Snowflake database objects:

Tables

External tables

Secure views

Secure materialized views

Secure UDFs

Snowflake enables the sharing of databases through shares, which are created by data providers and “imported” by data consumers.

Explanation

What is linear regression?

Linear regression analysis is used to predict the value of a variable based on the value of another variable. The variable you want to predict is called the dependent variable. The variable you are using to predict the other variable's value is called the independent variable.

Linear regression attempts to model the relationship between two variables by fitting a linear equation to observed data. One variable is considered to be an explanatoryvariable, and the other is considered to be a dependent variable. For example, a modeler might want to relate the weights of individuals to their heights using a linear regression model.

A linear regression line has an equation of the form Y = a + bX, where X is the explanatory variable and Y is the dependent variable. The slope of the line is b, and a is the intercept (the value of y when x = 0).

For linear regression Y=a+bx+error.

If neglect error then Y=a+bx. If x increases by 1, then Y = a+b(x+1) which implies Y=a+bx+b. So Y increases by its slope.

For linear regression Y=a+bx+error. If neglect error then Y=a+bx. If x increases by 1, then Y = a+b(x+1) which implies Y=a+bx+b. So Y increases by its slope.

Explanation

To track changing data trends, create a data drift monitor that uses the training data as a baseline and the new data as a target.

Model drift and decay are concepts that describe the process during which the performance of a model deployed to production degrades on new, unseen data or the underlying assumptions about the data change.

These are important metrics to track once models are deployed toproduction. Models must be regularly re-trained on new data. This is referred to as refitting the model. This can be done either on a periodic basis, or, in an ideal scenario, retraining can be triggered when the performance of the model degrades below a certain pre-defined threshold.

Explanation

Automatically Suspend Tasks After Failed Runs

Optionally suspend tasks automatically after a specified number of consecutive runs that either fail or time out. This feature can reduce costs by suspending tasks that consume Snowflake credits but fail to run to completion. Failed task runs include runs in which the SQL code in the task body either produces a user error or times out. Task runs that are skipped, canceled, or that fail due to a sys-tem error are considered indeterminate and are not included in the count of failed task runs.

Set the SUSPEND_TASK_AFTER_NUM_FAILURES = num parameter on a standalone task or the root task in a DAG. When the parameter is set to a value greater than 0, the following behavior applies to runs of the standalone task or DAG:

Standalone tasks are automatically suspended after the specified number of consecutive task runs either fail or time out.

The root task is automatically suspended after the run of any single task in a DAG fails or times out the specified number of times in consecutive runs.

The parameter can be set when creating a task (using CREATE TASK) or later (using ALTER TASK). The setting applies to tasks that rely on either Snowflake-managed compute resources (i.e. serverless compute model) or user-managed compute resources (i.e. a virtual warehouse).

The SUSPEND_TASK_AFTER_NUM_FAILURES parameter can also be set at the account, database, or schema level. The setting applies to all standalone or root tasks contained in the modified object. Note that explicitly setting the parameter at a lower (i.e. more granular) level overrides the parameter value set at a higher level.

Explanation

Vectorized Python UDFs let you define Python functions that receive batches of input rows as Pandas DataFrames and return batches of results as Pandas arrays or Series. You call vectorized Py-thon UDFs the same way you call other Python UDFs.

Advantages of using vectorized Python UDFs compared to the default row-by-row processing pat-tern include:

The potential for better performance if your Python code operates efficiently on batches of rows.

Less transformation logic required if you are calling into libraries that operate on Pandas Data-Frames or Pandas arrays.

When you use vectorized Python UDFs:

You do not need to change how you write queries using Python UDFs. All batching is handled by the UDF framework rather than your own code.

As with non-vectorized UDFs, there is no guarantee of which instances of your handler code will see which batches of input.

Explanation

Partial dependence plots (PDP) is a useful tool for gaining insights into the relationship between features and predictions. It helps us understand how different values of a particular feature impact model’s predictions.

Explanation

The drop_duplicates() method removes duplicate rows.

dataframe.drop_duplicates(subset, keep, inplace, ignore_index)

Remove duplicate rows from the DataFrame:

1.import pandas as pd

2.data = {

3."name": ["Peter", "Mary", "John", "Mary"],

4."age": [50, 40, 30, 40],

5."qualified": [True, False, False, False]

6.}

7.

8.df = pd.DataFrame(data)

9.newdf = df.drop_duplicates()

Explanation

A stream stores an offset for the source object and not any actual table columns or data. When que-ried, a stream accesses and returns the historic data in the same shape as the source object (i.e. the same column names and ordering) with the following additional columns:

METADATA$ACTION

Indicates the DML operation (INSERT, DELETE) recorded.

METADATA$ISUPDATE

Indicates whether the operation was part of an UPDATE statement. Updates to rows in the source object are represented as a pair of DELETE and INSERT records inthe stream with a metadata column METADATA$ISUPDATE values set to TRUE.

Note that streams record the differences between two offsets. If a row is added and then updated in the current offset, the delta change is a new row. The METADATA$ISUPDATE row records a FALSE value.

METADATA$ROW_ID

Specifies the unique and immutable ID for the row, which can be used to track changes to specific rows over time.

Explanation

By using ethnicity as a sensitive field, and comparing disparity between selection rates and performance metrics for each ethnicity value, you can evaluate the fairness of the model.

Explanation

Data sharing is only supported between Snowflake accounts. As a data provider, you might want to share data with a consumer who does not already have a Snowflake account or is not ready to be-come a licensed Snowflake customer.

To facilitate sharing data with these consumers, you can create reader accounts. Reader accounts (formerly known as “read-only accounts”) provide a quick, easy, and cost-effective way to share data without requiring the consumer to become a Snowflake customer.

Each reader account belongs to the provider account that created it. As a provider, you use shares to share databases with reader accounts; however, a reader account can only consume data from the provider account that created it.

So, Data Sharing is possible between Snowflake & Non-snowflake accounts via Reader Account.

Explanation

The area under the ROC curve (AUC) is a common evaluation metric for binary classification, which measures the performance of a classifier at different threshold values for the predicted probabilities. Other common metrics include accuracy, precision, recall, and F1 score, which are based on the confusion matrix of true positives, false positives, true negatives, and false negatives.