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Snowflake DEA-C01 SnowPro Advanced: Data Engineer Certification Exam Exam Practice Test

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Total 65 questions

SnowPro Advanced: Data Engineer Certification Exam Questions and Answers

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Question 1

A large table with 200 columns contains two years of historical data. When queried. the table is filtered on a single day Below is the Query Profile:

Using a size 2XL virtual warehouse, this query look over an hour to complete

What will improve the query performance the MOST?

Options:

A.

increase the size of the virtual warehouse.

B.

Increase the number of clusters in the virtual warehouse

C.

Implement the search optimization service on the table

D.

Add a date column as a cluster key on the table

Question 2

What are characteristics of Snowpark Python packages? (Select THREE).

Third-party packages can be registered as a dependency to the Snowpark session using the session, import () method.

Options:

A.

Python packages can access any external endpoints

B.

Python packages can only be loaded in a local environment

C.

Third-party supported Python packages are locked down to prevent hitting

D.

The SQL command DESCRIBE FUNCTION will list the imported Python packages of the Python User-Defined Function (UDF).

E.

Querying information__schema .packages will provide a list of supported Python packages and versions

Question 3

Which functions will compute a 'fingerprint' over an entire table, query result, or window to quickly detect changes to table contents or query results? (Select TWO).

Options:

A.

HASH (*)

B.

HASH_AGG(*)

C.

HASH_AGG(, )

D.

HASH_AGG_COMPARE (*)

E.

HASH COMPARE(*)

Question 4

A new customer table is created by a data pipeline in a Snowflake schema where MANAGED ACCESSenabled.

…. Can gran access to the CUSTOMER table? (Select THREE.)

Options:

A.

The role that owns the schema

B.

The role that owns the database

C.

The role that owns the customer table

D.

The SYSADMIN role

E.

The SECURITYADMIN role

F.

The USERADMIN role with the manage grants privilege

Question 5

A Data Engineer is working on a continuous data pipeline which receives data from Amazon Kinesis Firehose and loads the data into a staging table which will later be used in the data transformation process The average file size is 300-500 MB.

The Engineer needs to ensure that Snowpipe is performant while minimizing costs.

How can this be achieved?

Options:

A.

Increase the size of the virtual warehouse used by Snowpipe.

B.

Split the files before loading them andset the SIZE_LIMIT option to 250 MB.

C.

Change the file compression size and increase the frequency of the Snowpipe loads

D.

Decrease the buffer size to trigger delivery of files sized between 100 to 250 MB in Kinesis Firehose

Question 6

A Data Engineer has written a stored procedure that will run with caller's rights. The Engineer has granted ROLEA right to use this stored procedure.

What is a characteristic of the stored procedure being called using ROLEA?

Options:

A.

The stored procedure must run with caller's rights it cannot be converted later to run with owner's rights

B.

If the stored procedure accesses an object that ROLEA does not have access to the stored procedure will fail

C.

The stored procedure will run in the context (database and schema) where the owner created the stored procedure

D.

ROLEA will not be able to see the source code for the stored procedure even though the role has usage privileges on the stored procedure

Question 7

A Data Engineer needs to ingest invoice data in PDF format into Snowflake so that the data can be queried and used in a forecasting solution.

..... recommended way to ingest this data?

Options:

A.

Use Snowpipe to ingest the files that land in an external stage into a Snowflake table

B.

Use a COPY INTO command to ingest the PDF files in an external stage into a Snowflake table with a VARIANT column.

C.

Create an external table on the PDF files that are stored in a stage and parse the data nto structured data

D.

Create a Java User-Defined Function (UDF) that leverages Java-based PDF parser libraries to parse PDF data into structured data

Question 8

Given the table sales which has a clustering key of column CLOSED_DATE which table function will return the average clustering depth for the SALES_REPRESENTATIVEcolumn for the North American region?

A)

B)

C)

D)

Options:

A.

Option A

B.

Option B

C.

Option C

D.

Option D

Question 9

Company A and Company B both have Snowflake accounts. Company A's account is hosted on a different cloud provider and region than Company B's account Companies A and B are not in the same Snowflake organization.

How can Company A share data with Company B? (Select TWO).

Options:

A.

Create a share within Company A's account and add Company B's account as a recipient of that share

B.

Create a share within Company A's account, and create a reader account that is a recipient of the share Grant Company B access to the reader account

C.

Use database replication to replicate Company A's data into Company B's account Create a share within Company B's account and grant users within Company B's

account access to the share

D.

Create a new account within Company A's organization in the same cloud provider and region as Company B's account Use database replication to replicate Company

A's data to the new account Create a share within the new account and add Company B's account as a recipient of that share

E.

Create a separate database within Company A's account to contain only those data sets they wish to share with Company B Create a share within Company A's account

and add all the objects within this separate database to the share Add Company B's account as a recipient of the share

Question 10

A Data Engineer ran a stored procedure containing various transactions During the execution, the session abruptly disconnected preventing one transactionfrom committing or rolling hark.The transaction was left in a detached state and created a lock on resources

...must the Engineer take to immediately run a new transaction?

Options:

A.

Call the system function SYSTEM$ABORT_TRANSACTION.

B.

Call the system function SYSTEM$CANCEL_TRANSACTION.

C.

Set the LOCK_TIMEOUTto FALSE in the stored procedure

D.

Set the transaction abort on error to true in the stored procedure.

Question 11

A stream called TRANSACTIONS_STM is created on top of a transactions table in a continuous pipeline running in Snowflake. After a couple of months, the TRANSACTIONS table is renamed transactiok3_raw to comply with new naming standards

What will happen to the TRANSACTIONS _STM object?

Options:

A.

TRANSACTIONS _STMwill keep working as expected

B.

TRANSACTIONS _STMwill be stale and will need to be re-created

C.

TRANSACTIONS _STMwill be automatically renamedTRANSACTIONS _RAW_STM.

D.

Reading from the traksactioks_3T>: stream will succeed for some time after the expected STALE_TIME.

Question 12

Assuming that the session parameter USE_CACHED_RESULT is set to false, what are characteristics of Snowflake virtual warehouses in terms of the use of Snowpark?

Options:

A.

Creating a DataFrame from a table will start a virtual warehouse

B.

Creating a DataFrame from a staged file with the read () method will start a virtual warehouse

C.

Transforming a DataFrame with methods like replace () will start a virtual warehouse -

D.

Calling a Snowpark stored procedure to query the database with session, call () will start a virtual warehouse

Question 13

A company is using Snowpipe to bring in millions of rows every day of Change Data Capture (CDC) into a Snowflake staging table on a real-time basis The CDC needs to get processedand combined with other data in Snowflake and land in a final table as part of the full data pipeline.

How can a Data engineer MOST efficiently process the incoming CDC on an ongoing basis?

Options:

A.

Create a stream on the staging table and schedule a task that transforms data from the stream only when the stream has data.

B.

Transform the data during the data load with Snowpipe by modifying the related copy into statement to include transformation steps such as case statements andJOIN'S.

C.

Schedule a task that dynamically retrieves the last time the task was run from information_schema-rask_hiSwOry and use that timestamp to process the delta of the new rows since the last time the task was run.

D.

Use a create ok replace table as statement that references the staging table and includes all the transformation SQL. Use a task to run the full create or replace table as statement on a scheduled basis

Question 14

A Data Engineer executes a complex query and wants to make use of Snowflake s query results caching capabilities to reuse the results.

Which conditions must be met? (Select THREE).

Options:

A.

The results must be reused within 72 hours.

B.

The query must be executed using the same virtual warehouse.

C.

The USED_CACHED_RESULT parameter must be included in the query.

D.

The table structure contributing to the query result cannot have changed

E.

The new query must have the same syntax as the previously executed query.

F.

The micro-partitions cannot have changed due to changes to other data in the table

Question 15

A Data Engineer is building a pipeline to transform a 1 TD tab e by joining it with supplemental tables The Engineer is applying filters and several aggregations leveraging Common TableExpressions (CTEs) using a size Medium virtual warehouse in a single query in Snowflake.

After checking the Query Profile, what is the recommended approach to MAXIMIZE performance of this query if the Profile shows data spillage?

Options:

A.

Enable clustering on the table

B.

Increase the warehouse size

C.

Rewrite the query to remove the CTEs.

D.

Switch to a multi-cluster virtual warehouse

Question 16

A company built a sales reporting system with Python, connecting to Snowflake using the Python Connector. Based on the user's selections, the system generates the SQL queries needed to fetch the data for the report First it gets the customers that meet the given query parameters (on average 1000 customer records for each report run) and then it loops the customer records sequentially Inside that loop it runs the generated SQL clause for the current customer to get the detailed data for that customer number from the sales data table

When the Data Engineer tested the individual SQL clauses they were fast enough (1 second to get the customers 0 5 second to get the sales data for one customer) but the total runtime of the report is too long

How can this situation be improved?

Options:

A.

Increase the size of the virtual warehouse

B.

Increase the number of maximum clusters of the virtual warehouse

C.

Define a clustering key for the sales data table

D.

Rewrite the report to eliminate the use of the loop construct

Question 17

Which methods will trigger an action that will evaluate a DataFrame? (Select TWO)

Options:

A.

DataFrame.random_split ( )

B.

DataFrame.collect ()

C.

DateFrame.select ()

D.

DataFrame.col ( )

E.

DataFrame.show ()

Question 18

A Data Engineer needs to load JSON output from some software into Snowflake using Snowpipe.

Which recommendations apply to this scenario? (Select THREE)

Options:

A.

Load large files (1 GB or larger)

B.

Ensure that data files are 100-250 MB (or larger) in size compressed

C.

Load a single huge array containing multiple records into a single table row

D.

Verify each value of each unique element stores a single native data type (string or number)

E.

Extract semi-structured data elements containing null values into relational columns before loading

F.

Create data files that are less than 100 MB and stage them in cloud storage at a sequence greater than once each minute

Question 19

A Data Engineer defines the following masking policy:

….

must be applied to the full_name column in the customer table:

Which query will apply the masking policy on the full_name column?

Options:

A.

ALTER TABLE customer MODIFY COLUMN full_name Set MASKING POLICY name_policy;

B.

ALTER TABLE customer MODIFY COLUMN full_nam ADD MASKING POLICY name_poiicy;

C.

ALTER TABLE customer MODIFY COLUMN first_nane SET MASKING POLICY name_policy; lasT_name SET MASKING POLICY name_pclicy;

D.

ALTER TABLE customer MODIFY COLUMN first_name ADD MASKING POLICY name_policy,

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Total 65 questions