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Snowflake DEA-C01 Exam Overview:

Certification Vendor:Snowflake
Exam Name:SnowPro Advanced: Data Engineer Certification Exam
Exam Number:DEA-C01
Available Languages:English
Related Certifications:SnowPro Core Certification
Certificate Validity Period:2 years
Exam Price:$375 USD
Exam Duration:115 minutes
Passing Score:Not publicly disclosed (Snowflake uses scaled scoring)
Real Exam Qty:Approximately 65 questions
Exam Format:Scenario-based questions, Multiple select, Multiple choice
Recommended Training:Snowflake University Training
SnowPro Advanced Data Engineer Exam Guide
Exam Registration:Snowflake Certification Portal
Sample Questions:Snowflake DEA-C01 Sample Questions
Exam Way:Online proctored or authorized testing center
Pre Condition:Recommended: SnowPro Core Certification or equivalent Snowflake experience
Official Syllabus URL:https://www.snowflake.com/certifications/snowpro-advanced-data-engineer/

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Snowflake DEA-C01 Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 2
  • 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 3
  • 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 4
  • 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 5
  • 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.

Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q249-Q254):

NEW QUESTION # 249
Which UDF programming language is not supported with Snowflake Secure Data Sharing feature?

Answer: B


NEW QUESTION # 250
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?

Answer: C

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 # 251
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]

Answer: B,D

Explanation:
Explanation
Deleting Staged Files After Snowpipe Loads the Data
Pipe objects do not support the PURGE copy option. Snowpipe cannot delete staged files automat-ically when the data is successfully loaded into tables.
To remove staged files that you no longer need, It is recommended to periodically executing the REMOVE command to delete the files.
Alternatively, configure any lifecycle management features provided by cloud storage service pro-vider.


NEW QUESTION # 252
A company needs to generate a one-time performance report by joining data that is stored in Amazon DynamoDB. Amazon RDS. Amazon Redshift. and Amazon S3. The company wants to avoid unnecessary data movement and to minimize query execution time. Which solution will meet these requirements?

Answer: D

Explanation:
Amazon Athena Federated Query can run SQL across data in Amazon S3 and external sources by using data source connectors, and AWS provides connectors for Amazon DynamoDB, Amazon RDS, and Amazon Redshift. That lets the company perform a one-time cross-source join and analysis without first centralizing or copying the data, which minimizes unnecessary data movement and setup effort.
Reference:
https://docs.aws.amazon.com/athena/latest/ug/federated-queries.html
https://docs.aws.amazon.com/athena/latest/ug/connectors-redshift.html
https://docs.aws.amazon.com/athena/latest/ug/connectors-dynamodb.html


NEW QUESTION # 253
A finance company receives data from third-party data providers and stores the data as objects in an Amazon S3 bucket.
The company ran an AWS Glue crawler on the objects to create a data catalog. The AWS Glue crawler created multiple tables. However, the company expected that the crawler would create only one table.
The company needs a solution that will ensure the AVS Glue crawler creates only one table.
Which combination of solutions will meet this requirement? (Choose two.)

Answer: A,D


NEW QUESTION # 254
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