Snowflake DSA-C02 Online Practice Questions and Exam Preparation

Snowflake DSA-C02 Online Questions & Answers

• Question 1:

  Which of the following is a useful tool for gaining insights into the relationship between features and predictions?

  A. numpy plots
  B. sklearn plots
  C. Partial dependence plots(PDP)
  D. FULL dependence plots (FDP)
  C. Partial dependence plots(PDP)

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  Explanation/Reference:

  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.

• Question 2:

  Mark the incorrect statement regarding usage of Snowflake Stream and Tasks?

  A. Snowflake automatically resizes and scales the compute resources for serverless tasks.
  B. Snowflake ensures only one instance of a task with a schedule (i.e. a standalone task or the root task in a DAG) is executed at a given time. If a task is still running when the next scheduled execution time occurs, then that scheduled time is skipped.
  C. Streams support repeatable read isolation.
  D. An standard-only stream tracks row inserts only.
  D. An standard-only stream tracks row inserts only.

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  Explanation/Reference:

  All are correct except a standard-only stream tracks row inserts only. A standard (i.e. delta) stream tracks all DML changes to the source object, including inserts, up-dates, and deletes (including table truncates).

• Question 3:

  Which of the following Functions do Support Windowing?

  A. HASH_AGG
  B. ENCRYPT
  C. EXTRACT
  D. LISTAGG
  D. LISTAGG

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  Explanation/Reference:

  What is a Window?

  A window is a group of related rows. For example, a window might be defined based on timestamps, with all rows in the same month grouped in the same window. Or a window might be defined based on location, with all rows from a

  particular city grouped in the same window.

  A window can consist of zero, one, or multiple rows. For simplicity, Snowflake documentation usually says that a window contains multiple rows.

  What is a Window Function?

  A window function is any function that operates over a window of rows. A window function is generally passed two parameters:

  A row. More precisely, a window function is passed 0 or more expressions. In almost all cases, at least one of those expressions references a column in that row. (Most window functions require at least one column or expression, but a few

  window functions, such as some rank-related functions, do not required an explicit column or expression.) A window of related rows that includes that row. The window can be the entire table, or a subset of the rows in the table.

  For non-window functions, all arguments are usually passed explicitly to the function, for example:

  MY_FUNCTION(argument1, argument2, ...)

  Window functions behave differently; although the current row is passed as an argument the normal way, the window is passed through a separate clause, called an OVER clause. The syntax of the OVER clause is documented later.

  LISTAGG

  Returns the concatenated input values, separated by the delimiter string.

  Window function

  1.LISTAGG( [ DISTINCT ] [, ] ) 2.[ WITHIN GROUP ( ) ]

  3.OVER ( [ PARTITION BY ] )

  HASH_AGG

  Returns an aggregate signed 64-bit hash value over the (unordered) set of input rows. HASH_AGG never returns NULL, even if no input is provided. Empty input "hashes" to 0.

  Window function

  HASH_AGG( [ DISTINCT ] [ , ... ] ) OVER ( [ PARTITION BY ] ) HASH_AGG(*) OVER ( [ PARTITION BY ] )

• Question 4:

  As Data Scientist looking out to use Reader account, Which ones are the correct considerations about Reader Accounts for Third-Party Access?

  A. 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.
  B. Each reader account belongs to the provider account that created it.
  C. Users in a reader account can query data that has been shared with the reader account, but cannot perform any of the DML tasks that are allowed in a full account, such as data loading, insert, update, and similar data manipulation operations.
  D. Data sharing is only possible between Snowflake accounts.
  D. Data sharing is only possible between Snowflake accounts.

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  Explanation/Reference:

  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 and Non-snowflake accounts via Reader Account.

• Question 5:

  What is the formula for measuring skewness in a dataset?

  A. MEAN - MEDIAN
  B. MODE - MEDIAN
  C. (3(MEAN - MEDIAN))/ STANDARD DEVIATION
  D. (MEAN - MODE)/ STANDARD DEVIATION
  C. (3(MEAN - MEDIAN))/ STANDARD DEVIATION

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  Explanation/Reference:

  Since the normal curve is symmetric about its mean, its skewness is zero. This is a theoretical expla-nation for mathematical proofs, you can refer to books or websites that speak on the same in detail.

• Question 6:

  Which one is incorrect understanding about Providers of Direct share?

  A. A data provider is any Snowflake account that creates shares and makes them available to other Snowflake accounts to consume.
  B. As a data provider, you share a database with one or more Snowflake accounts.
  C. You can create as many shares as you want, and add as many accounts to a share as you want.
  D. If you want to provide a share to many accounts, you can do the same via Direct Share.
  D. If you want to provide a share to many accounts, you can do the same via Direct Share.

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  Explanation/Reference:

  If you want to provide a share to many accounts, you might want to use a listing or a data ex-change.

• Question 7:

  Which one of the following is not the key component while designing External functions within Snowflake?

  A. Remote Service
  B. API Integration
  C. UDF Service
  D. Proxy Service
  C. UDF Service

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  Explanation/Reference:

  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.

• Question 8:

  Mark the Incorrect understanding of Data Scientist about Streams? Choose 2.

  A. Streams on views support both local views and views shared using Snowflake Secure Data Sharing, including secure views.
  B. Streams can track changes in materialized views.
  C. Streams itself does not contain any table data.
  D. Streams do not support repeatable read isolation.
  B. Streams can track changes in materialized views.
  D. Streams do not support repeatable read isolation.

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  Explanation/Reference:

  Streams on views support both local views and views shared using Snowflake Secure Data Sharing, including secure views. Currently, streams cannot track changes in materialized views. stream itself does not contain any table data. A stream only stores an offset for the source object and returns CDC records by leveraging the versioning history for the source object. When the first stream for a table is created, several hidden columns are added to the source table and begin storing change tracking metadata. These columns consume a small amount of storage. The CDC records returned when querying a stream rely on a combination of the offset stored in the stream and the change tracking metadata stored in the table. Note that for streams on views, change tracking must be enabled explicitly for the view and underlying tables to add the hidden columns to these tables. Streams support repeatable read isolation. In repeatable read mode, multiple SQL statements within a transaction see the same set of records in a stream. This differs from the read committed mode supported for tables, in which statements see any changes made by previous statements executed within the same transaction, even though those changes are not yet committed. The delta records returned by streams in a transaction is the range from the current position of the stream until the transaction start time. The stream position advances to the transaction start time if the transaction commits; otherwise it stays at the same position.

• Question 9:

  To return the contents of a DataFrame as a Pandas DataFrame, Which of the following method can be used in SnowPark API?

  A. REPLACE_TO_PANDAS
  B. SNOWPARK_TO_PANDAS
  C. CONVERT_TO_PANDAS
  D. TO_PANDAS
  D. TO_PANDAS

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  Explanation/Reference:

  To return the contents of a DataFrame as a Pandas DataFrame, use the to_pandas method.

  For example:

  1.>>> python_df = session.create_dataframe(["a", "b", "c"]) 2.>>> pandas_df = python_df.to_pandas()

• Question 10:

  Consider a data frame df with 10 rows and index [ 'r1', 'r2', 'r3', 'row4', 'row5', 'row6', 'r7', 'r8', 'r9', 'row10']. What does the expression g = df.groupby(df.index.str.len()) do?

  A. Groups df based on index values
  B. Groups df based on length of each index value
  C. Groups df based on index strings
  D. Data frames cannot be grouped by index values. Hence it results in Error.
  D. Data frames cannot be grouped by index values. Hence it results in Error.

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  Explanation/Reference:

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