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Snowflake DEA-C01 Exam Syllabus Topics:

Topic	Details
Topic 1	Security: The Security topic of the DEA-C01 test covers the principles of Snowflake security, including the management of system roles and data governance. It measures the ability to secure data and ensure compliance with policies, crucial for maintaining secure data environments for Snowflake Data Engineers and Software Engineers.
Topic 2	Performance Optimization: This topic assesses the ability to optimize and troubleshoot underperforming queries in Snowflake. Candidates must demonstrate knowledge in configuring optimal solutions, utilizing caching, and monitoring data pipelines. It focuses on ensuring engineers can enhance performance based on specific scenarios, crucial for Snowflake Data Engineers and Software Engineers.
Topic 3	Data Movement: Snowflake Data Engineers and Software Engineers are assessed on their proficiency to load, ingest, and troubleshoot data in Snowflake. It evaluates skills in building continuous data pipelines, configuring connectors, and designing data sharing solutions.
Topic 4	Data Transformation: The SnowPro Advanced: Data Engineer exam evaluates skills in using User-Defined Functions (UDFs), external functions, and stored procedures. It assesses the ability to handle semi-structured data and utilize Snowpark for transformations. This section ensures Snowflake engineers can effectively transform data within Snowflake environments, critical for data manipulation tasks.
Topic 5	Storage and Data Protection: The topic tests the implementation of data recovery features and the understanding of Snowflake's Time Travel and micro-partitions. Engineers are evaluated on their ability to create new environments through cloning and ensure data protection, highlighting essential skills for maintaining Snowflake data integrity and accessibility.

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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q208-Q213):

NEW QUESTION # 208
When using the CURRENT_ROLE and CURRENT_USER functions with secure UDFs that will be shared with Snowflake accounts, Snowflake returns a NULL value for these functions?

A. TRUE
B. FALSE

Answer: A

Explanation:
Explanation
When using the CURRENT_ROLE and CURRENT_USER functions with secure UDFs that will be shared with Snowflake accounts, Snowflake returns a NULL value for these functions. The rea-son is that the owner of the data being shared does not typically control the users or roles in the ac-count with which the UDF is being shared.

NEW QUESTION # 209
A data engineer creates an AWS Glue Data Catalog table by using an AWS Glue crawler that is named Orders. The data engineer wants to add the following new partitions:
s3://transactions/orders/order_date=2023-01-01
s3://transactions/orders/order_date=2023-01-02
The data engineer must edit the metadata to include the new partitions in the table without scanning all the folders and files in the location of the table.
Which data definition language (DDL) statement should the data engineer use in Amazon Athena?

A. REPAIR TABLE Orders;
B. ALTER TABLE Orders MODIFY PARTITION(order_date='2023-01-01') LOCATION
's3://transactions/orders/2023-01-01';
ALTER TABLE Orders MODIFY PARTITION(order_date='2023-01-02') LOCATION
's3://transactions/orders/2023-01-02';
C. ALTER TABLE Orders ADD PARTITION(order_date='2023-01-01') LOCATION
's3://transactions/orders/order_date=2023-01-01';
ALTER TABLE Orders ADD PARTITION(order_date='2023-01-02') LOCATION
's3://transactions/orders/order_date=2023-01-02';
D. MSCK REPAIR TABLE Orders;

Answer: C

Explanation:
https://docs.aws.amazon.com/athena/latest/ug/alter-table-add-partition.html

NEW QUESTION # 210
Which callback function is required within a JavaScript User-Defined Function (UDF) for it to execute successfully?

A. finalize ()
B. initialize ()
C. handler
D. processRow ()

Answer: D

Explanation:
Explanation
The processRow () callback function is required within a JavaScript UDF for it to execute successfully. This function defines how each row of input data is processed and what output is returned. The other callback functions are optional and can be used for initialization, finalization, or error handling.

NEW QUESTION # 211
Streams cannot be created to query change data on which of the following objects? [Select All that Apply]

A. Views, including secure views
B. Query Log Tables
C. Standard tables, including shared tables.
D. Directory tables
E. External tables

Answer: B

Explanation:
Explanation
Streams supports all the listed objects except Query Log tables.

NEW QUESTION # 212
Within a Snowflake account permissions have been defined with custom roles and role hierarchies.
To set up column-level masking using a role in the hierarchy of the current user, what command would be used?

A. IS_GRANTED_TO_INVOKER_ROLE
B. IS_RCLE_IN_SESSION
C. CORRECT_ROLE
D. IKVOKER_ROLE

Answer: B

Explanation:
Explanation
The IS_ROLE_IN_SESSION function is used to set up column-level masking using a role in the hierarchy of the current user. Column-level masking is a feature in Snowflake that allows users to apply dynamic data masking policies to specific columns based on the roles of the users who access them. The IS_ROLE_IN_SESSION function takes a role name as an argument and returns true if the role is in the current user's session, or false otherwise. The function can be used in a masking policy expression to determine whether to mask or unmask a column value based on the role of the user. For example:
CREATE OR REPLACE MASKING POLICY email_mask AS (val string) RETURNS string -> CASE WHEN IS_ROLE_IN_SESSION('HR') THEN val ELSE REGEXP_REPLACE(val, '(.).(.@.)', '\1****\2') END; In this example, the IS_ROLE_IN_SESSION function is used to create a masking policy for an email column.
The masking policy returns the original email value if the user has the HR role in their session, or returns a masked email value with asterisks if not.

NEW QUESTION # 213
......

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