DSA-C02 Online Practice Questions and Answers

Correct Answer: A

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.

Correct Answer: BC

Explanation:What is a Share?

Shares are named Snowflake objects that encapsulate all of the information required to share a database.

Data providers add Snowflake objects (databases, schemas, tables, secure views, etc.) to a share using either or both of the following options:

Option 1: Grant privileges on objects to a share via a database role. Option 2: Grant privileges on objects directly to a share. You choose which accounts can consume data from the share by adding the accounts to the share.

After a database is created (in a consumer account) from a share, all the shared objects are accessible to users in the consumer account. Shares are secure, configurable, and controlled completely by the provider account:

New objects added to a share become immediately available to all consumers, providing real-time access to shared data.

Access to a share (or any of the objects in a share) can be revoked at any time.

Correct Answer: C

Explanation:

What is an External Function?

An external function calls code that is executed outside Snowflake. The remotely executed code is known as a remote service. Information sent to a remote service is usually relayed through a proxy service. Snowflake stores security-related

external function information in an API integration.

External Function:

An external function is a type of UDF. Unlike other UDFs, an external function does not contain its own code; instead, the external function calls code that is stored and executed outside Snowflake.

Inside Snowflake, the external function is stored as a database object that contains information that Snowflake uses to call the remote service. This stored information includes the URL of the proxy service that relays information to and from

the remote service.

Remote Service:

The remotely executed code is known as a remote service. The remote service must act like a function. For example, it must return a value. Snowflake supports scalar external functions; the remote service must return exactly one row for

each row received.

Proxy Service:

Snowflake does not call a remote service directly. Instead, Snowflake calls a proxy service, which relays the data to the remote service.

The proxy service can increase security by authenticating requests to the remote service. The proxy service can support subscription-based billing for a remote service. For example, the proxy service can verify that a caller to the remote

service is a paid subscriber. The proxy service also relays the response from the remote service back to Snowflake.

Examples of proxy services include:

Amazon API Gateway.

Microsoft Azure API Management service.

API Integration:

An integration is a Snowflake object that provides an interface between Snowflake and third-party services. An API integration stores information, such as security information, that is needed to work with a proxy service or remote service. An

API integration is created with the CREATE API INTEGRATION command. Users can write and call their own remote services, or call remote services written by third parties. These remote services can be written using any HTTP server

stack,including cloud serverless compute services such as AWS Lambda.

Correct Answer: C

Explanation: The EXECUTE TASK command manually triggers a single run of a scheduled task (either a standalone task or the root task in a DAG) independent of the schedule defined for the task. A successful run of a roottask triggers a cascading run of child tasks in the DAG as their precedent task completes, as though the root task had run on its defined schedule. This SQL command is useful for testing new or modified standalone tasks and DAGs before you enable them to execute SQL code in production. Call this SQL command directly in scripts or in stored procedures. In addition, this command sup-ports integrating tasks in external data pipelines. Any third-party services that can authenticate into your Snowflake account and authorize SQL actions can execute the EXECUTE TASK command to run tasks.

Correct Answer: D

Correct Answer: D

Explanation:

Data visualization is the visual depiction of data through the use of graphs, plots, and informational graphics. Its practitioners use statistics and data science to conveythe meaning behind data in ethical and accurate ways.

Correct Answer: B

Explanation:

NULL values are ignored unless all the records are NULL, in which case a NULL value is returned

Correct Answer: A

Explanation:

Jupyter Notebook is a web-based interactive computational environment. The command used to install Jupyter Notebook is pip install jupyter. The command used to start Jupyter Notebook is jupyter notebook.

Correct Answer: AD

Explanation:

Normalization is a scaling technique in Machine Learning applied during data preparation to change the values of numeric columns in the dataset to use a common scale.It is not necessary for all datasets in a model. It is required only when

features of machine learning models have different ranges.

Scikit-Learn provides a transformer called MinMaxScaler for Normalization. This technique uses minimum and max values for scaling of model.Itis useful when feature distribution is unknown.It got affected by outliers.

Correct Answer: ACE

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.

Correct Answer: D

Explanation:

All Snowpark ML modeling and preprocessing classes are in the snowflake.ml.modeling namespace. The Snowpark ML modules have the same name as the corresponding module from the sklearn namespace. For example, the Snowpark

ML module corresponding to sklearn.calibration is snow-flake.ml.modeling.calibration. The xgboost and lightgbm modules correspond to snowflake.ml.modeling.xgboost and snow-flake.ml.modeling.lightgbm, respectively.

Not all of the classes from scikit-learn are supported in Snowpark ML.

Correct Answer: C

Explanation:

Computes length of column A and Sum of Column B values of each group

Correct Answer: B

Explanation:

Leave-one-out cross-validation (LOO cross-validation) is not suitable for very large datasets due to the fact that this validation technique requires one model for every sample in the training set to be created and evaluated.

Cross validation

It is a technique to evaluate a machine learning model and it is the basis for whole class of model evaluation methods. The goal of cross-validation is to test the model's ability to predict new data that was not used in estimating it. It works by

the idea of splitting dataset into number of subsets, keep a subset aside, train the model, and test the model on the holdout subset.

Leave-one-out cross validation

Leave-one-out cross validation is K-fold cross validation taken to its logical extreme, with K equal to N, the number of data points in the set. That means that N separate times, the function approximator is trained on all the data except for one

point and a prediction is made for that point. As be-fore the average error is computed and used to evaluate the model. The evaluation given by leave-one-out cross validation is very expensive to compute at first pass.

Correct Answer: D

Explanation: Data frames cannot be grouped by index values. Hence it results in Error.

Correct Answer: D