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Snowflake DEA-C01 Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	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 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	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 5	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.

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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q205-Q210):

NEW QUESTION # 205
Which system role is recommended for a custom role hierarchy to be ultimately assigned to?

A. USERADMIN
B. ACCOUNTADMIN
C. SECURITYADMIN
D. SYSTEMADMIN

Answer: C

Explanation:
Explanation
The system role that is recommended for a custom role hierarchy to be ultimately assigned to is SECURITYADMIN. This role has the manage grants privilege on all objects in an account, which allows it to grant access privileges to other roles or revoke them as needed. This role can also create or modify custom roles and assign them to users or other roles. By assigning custom roles to SECURITYADMIN, the role hierarchy can be managed centrally and securely. The other options are not recommended system roles for a custom role hierarchy to be ultimately assigned to. Option A is incorrect because ACCOUNTADMIN is the most powerful role in an account, which has full access to all objects and operations. Assigning custom roles to ACCOUNTADMIN can pose a security risk and should be avoided. Option C is incorrect because SYSTEMADMIN is a role that has full access to all objects in the public schema of the account, but not to other schemas or databases. Assigning custom roles to SYSTEMADMIN can limit the scope and flexibility of the role hierarchy. Option D is incorrect because USERADMIN is a role that can manage users and roles in an account, but not grant access privileges to other objects. Assigning custom roles to USERADMIN can prevent the role hierarchy from controlling access to data and resources.

NEW QUESTION # 206
Snowflake does not treat the inner transaction as nested; instead, the inner transaction is a separate transaction.
What is term used to call these Transaction?

A. Scoped transactions
B. Atomic Transaction
C. Nested Scope Transaction
D. Enclosed Transaction
E. Inner Transaction

Answer: A

NEW QUESTION # 207
A company stores Apache Parquet files in an Amazon S3 data lake. The data lake receives thousands of files from multiple sources every hour. The files range in size from 50 KB to 100 KB.
The company is evaluating the implementation of Apache Iceberg tables for the data lake. The company is using AWS Glue Data Catalog as part of the evaluation. The company needs a solution to optimize query performance in Iceberg. The solution must ensure that Iceberg table performance does not degrade when more files are added over time.
Which solution will meet these requirements?

A. Configure Iceberg table properties to enable automatic compaction based on thresholds for file size and the number of files.
B. Implement a partition strategy in Amazon S3. Run an AWS Glue crawler to update the Data Catalog every 5 minutes.
C. Configure the Data Catalog to automatically compact the files every minute.
D. Use an AWS Glue job to compact the files into a standard size of 512 MB at the end of each day.
Run an AWS Glue crawler to update the Data Catalog.

Answer: A

Explanation:
Apache Iceberg is designed to manage table performance over time by controlling file layout through compaction. Enabling Iceberg's automatic compaction via table properties allows the table to rewrite many small files into fewer larger files based on configured thresholds, preventing the "small files problem" and keeping query planning and scan performance from degrading as new files continue to arrive.

NEW QUESTION # 208
A company is using Amazon Redshift as a data warehousing service. It is running analytical queries on large amounts of data using ODBC connections. The operations team is observing that some queries are getting completed while others are getting hung. They are seeking your suggestions to resolve this issue.
What setting can be done?

A. Increase the size of MTU (Maximum Transmission Unit) to jumbo size.
B. Reduce the size of MTU (Maximum Transmission Unit).
C. Set a lower fetch size per query.
D. Set a higher fetch size per query.

Answer: B

Explanation:
Maximum Transmission Unit (MTU) determines the maximum size in packet bytes that can be transferred in a single Ethernet frame between two hosts.
Customers may face query hang issues when the MTU size in a network path between two IP hosts is mismatched. To resolve the query hang issue for Redshift ODBC connections, the MTU size can be reduced.
Reference:
https://docs.aws.amazon.com/redshift/latest/dg/queries-troubleshooting.html#queries-troubleshooting-query-hangs
https://docs.aws.amazon.com/redshift/latest/mgmt/connecting-drop-issues.html

NEW QUESTION # 209
A company loads transaction data for each day into Amazon Redshift tables at the end of each day. The company wants to have the ability to track which tables have been loaded and which tables still need to be loaded.
A data engineer wants to store the load statuses of Redshift tables in an Amazon DynamoDB table. The data engineer creates an AWS Lambda function to publish the details of the load statuses to DynamoDB.
How should the data engineer invoke the Lambda function to write load statuses to the DynamoDB table?

A. Use the Amazon Redshift Data API to publish a message to an Amazon Simple Queue Service (Amazon SQS) queue. Configure the SQS queue to invoke the Lambda function.
B. Use a second Lambda function to invoke the first Lambda function based on AWS CloudTrail events.
C. Use the Amazon Redshift Data API to publish an event to Amazon EventBridge. Configure an EventBridge rule to invoke the Lambda function.
D. Use a second Lambda function to invoke the first Lambda function based on Amazon CloudWatch events.

Answer: C

Explanation:
https://docs.aws.amazon.com/redshift/latest/mgmt/data-api-monitoring-events.html

NEW QUESTION # 210
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