Pass Exam Questions Efficiently With DEA-C01 Questions (2023) [Q13-Q29]

QUESTION 13
For SQL UDFs, The invoker of the function need not have access to the objects referenced in the function definition, but only needs the privilege to use the function?

TRUE

FALSE

QUESTION 14
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?

ALTER TABLE customer MODIFY COLUMN full_name Set MASKING POLICY name_policy;

ALTER TABLE customer MODIFY COLUMN full_nam ADD MASKING POLICY name_poiicy;

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

ALTER TABLE customer MODIFY COLUMN first_name ADD MASKING POLICY name_policy,

QUESTION 15
Which are false statements about Star Schema?

Star schemas are denormalized.

The star schema separates business process data into facts, which hold the measurable, quantitative data about a business, and dimensions which are descriptive attributes re-lated to fact data.

Star schema is more flexible in terms of analytical needs compared to Data Vault Mod-elling.

The star schema is an important special case of the snowflake schema and is more effec-tive for handling simpler queries.

QUESTION 16
To view/monitor the clustering metadata for a table, Snowflake provides which of the following system functions?

SYSTEM$CLUSTERING_DEPTH_KEY

SYSTEM$CLUSTERING_KEY_INFORMATION (including clustering depth)

SYSTEM$CLUSTERING_DEPTH

SYSTEM$CLUSTERING_INFORMATION (including clustering depth)

QUESTION 17
What are Common Query Problems a Data Engineer can identified using Query Profiler?

“Exploding” Joins i.e Joins resulting due to a “Cartesian product”

Queries Too Large to Fit in Memory

Inefficient Pruning

Ineffective Data Sharing

QUESTION 18
Select the incorrect statement while working with warehouses?

Compute resources waiting to shut down are considered to be in “quiesce” mode.

Resizing a warehouse to a larger size is useful while loading and unloading significant amounts of data.

Resizing a warehouse will have any immediate impact on statements that are currently being executed by the warehouse.

Resizing a suspended warehouse does not provision any new compute resources for the warehouse.

QUESTION 19
A Data Engineer wants to centralize grant management to maximize security. A user needs ownership on a table m a new schema However, this user should not have the ability to make grant decisions What is the correct way to do this?

Grant ownership to the user on the table

Revoke grant decisions from the user on the table

Revoke grant decisions from the user on the schema.

Add the with managed access parameter on the schema

QUESTION 20
While working with Multi Cluster Warehouses, Select the incorrect understanding of Data Engineer about its usage?

Multi-cluster warehouses are designed specifically for handling queuing and perfor-mance issues related to large numbers of concurrent users and/or queries.

Unless you have a specific requirement for running in Maximized mode, multi-cluster warehouses should be configured to run in Auto-scale mode, which enables Snowflake to automatically start and stop clusters as needed.

When choosing the minimum number of clusters for a multi-cluster warehouse keep the default value as
1.

Multi-cluster warehouses generally improve query performance, particularly for larger, more complex queries.

When choosing the maximum number of clusters for a multi-cluster warehouse set its value as large as possible.

QUESTION 21
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?

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

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

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

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

QUESTION 22
A Data Engineer enables a result cache at the session level with the following command:
ALTER SESSION SET USE CACHED RESULT = TRUE;
The Engineer then runs the following select query twice without delay:

The underlying table does not change between executions
What are the results of both runs?

The first and second run returned the same results because sample is deterministic

The first and second run returned the same results, because the specific SEEDvalue was provided.

The first and second run returned different results because the query is evaluated each time it is run.

The first and second run returned differentresults because the query uses *instead of an explicit column list

QUESTION 23
Mark the Correct Statements for the VALIDATION_MODE option used by Data Engineer for Da-ta loading operations in his/her COPY INTO <table> command:

VALIDATION_MODE instructs the COPY command to validate the data files instead of loading them into the specified table; i.e., the COPY command tests the files for er-rors but does not load them.

VALIDATION_MODE option supported these values:
RETURN_n_ROWS,
RETURN_ERRORS,
RETURN_ALL_ERRORS

VALIDATION_MODE does not support COPY statements that transform data during a load. If the parameter is specified, the COPY statement returns an error.

VALIDATION_MODE only support Data loading operation i.e., do not work while da-ta unloading.

QUESTION 24
The smaller the average depth, the better clustered the table is with regards to the specified column?

TRUE

FALSE

QUESTION 25
A Data Engineer would like to define a file structure for loading and unloading data Where can the file structure be defined? (Select THREE)

copy command

MERGE command

FILE FORMAT Object

pipe object

stage object

INSERT command

QUESTION 26
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?

Creating a DataFrame from a table will start a virtual warehouse

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

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

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

QUESTION 27
If using a JavaScript UDF in a masking policy, Data Engineer needs to ensure the data type of the column, UDF, and masking policy match irrespective of case-sensitivity?

TRUE

FALSE

QUESTION 28
Which Snowflake feature facilitates access to external API services such as geocoders. data transformation, machine Learning models and other custom code?

Security integration

External tables

External functions

Java User-Defined Functions (UDFs)

QUESTION 29
Data Engineer is looking out to delete staged files automatically/periodically when the data is suc-cessfully loaded into tables by the Snowpipe. For achieving the same, which options/command is best suited: [Select 2]

PURGE option can be set as True in the COPY INTO Command embedded in PIPE objects definition.

To remove staged files that no longer needed, periodically REMOVE command can be executed to delete the files.

To remove staged files that no longer needed, periodically DELETE command can be executed to delete the files.

REMOVE_STAGE_FILES option can be set as True in the COPY INTO Command embedded in PIPE objects definition.

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Pass Exam Questions Efficiently With DEA-C01 Questions (2023) [Q13-Q29]

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