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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	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 3	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 4	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.
Topic 5	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.

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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q161-Q166):

NEW QUESTION # 161
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. IKVOKER_ROLE
D. CORRECT_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 # 162
A company has developed an application in which Amazon Managed Service for Apache Flink reads streaming data from Amazon Kinesis Data Streams and performs an analysis. A parallelism of 10 is defined for subtasks in the Apache Flink application, while 2 numbers of shards are defined in the Amazon Kinesis Data Streams. The developer team is observing idle subtasks due to this configuration. They are looking to distribute the load across all subtasks.
What configuration settings can be done to resolve this issue?

A. Increase the number of shards in the Amazon Kinesis Data Stream to match the count of subtasks.
B. Enable serialization at the source and rebalance the data to distribute across all the tasks.Serialize data at the task level.
C. Decrease the number of subtasks in the Apache Flink application.
D. Disable deserialization at the source and rebalance the data to distribute it across all the tasks.
Then, deserialize the data at the task level.

Answer: A

Explanation:
Shards and Parallelism: In Amazon Kinesis Data Streams, each shard provides a unit of parallelism. With only 2 shards and 10 subtasks, there are not enough shards to distribute the load evenly across all subtasks. Increasing the number of shards to match the number of subtasks (10 in this case) will ensure that each subtask has data to process, thereby eliminating idle subtasks

NEW QUESTION # 163
A company needs a solution to store and query product data that has variable attributes. The solution must support unpredictable and high-volume queries with single-digit millisecond latency, even during sudden traffic spikes. The solution must retrieve items by a primary identifier named Product ID. The solution must allow flexible queries by secondary attributes named Category and Brand. Which solution will meet these requirements?

A. Use Amazon Aurora with a Multi-AZ deployment to store product data. Use read replicas. Create indexes for primary and secondary attributes.
B. Use an Amazon DynamoDB table with on-demand capacity to store product data. Store products by primary key. Use global secondary indexes (GSIs) to store secondary attributes.
C. Use Amazon ElastiCache (Redis OSS) and Amazon S3 to store product data. Use Amazon Athena to run flexible secondary attribute queries.
D. Use an Amazon OpenSearch Serverless cluster with dynamic scaling to store product data. Index product data by primary and secondary attributes.

Answer: B

Explanation:
Amazon DynamoDB with on-demand capacity is designed to handle unpredictable, high-volume request patterns and sudden traffic spikes while maintaining single-digit millisecond latency.
Using Product ID as the table's primary key enables efficient item retrieval by the primary identifier, and global secondary indexes on Category and Brand provide flexible, low-latency queries on those secondary attributes.

NEW QUESTION # 164
A data engineer needs to create an empty copy of an existing table in Amazon Athena to perform data processing tasks. The existing table in Athena contains 1,000 rows.
Which query will meet this requirement?

A. CREATE TABLE new_table
LIKE old_table;
B. CREATE TABLE new_table
AS SELECT *
FROM old_table
WITH NO DATA;
C. CREATE TABLE new_table
as SELECT *
FROM old_cable
WHERE 1=1;
D. CREATE TABLE new_table
AS SELECT *
FROM old_table;

Answer: B

NEW QUESTION # 165
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. FALSE
B. TRUE

Answer: B

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 # 166
......

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