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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	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	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 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 (Q252-Q257):

NEW QUESTION # 252
A data engineer is building a new data pipeline that stores metadata in an Amazon DynamoDB table. The data engineer must ensure that all items that are older than a specified age are removed from the DynamoDB table daily.
Which solution will meet this requirement with the LEAST configuration effort?

A. Create an Amazon EventBridge rule that uses a daily cron expression to trigger an AWS Lambda function to delete items that are older than the specified age.
B. Enable DynamoDB TTL on the DynamoDB table. Adjust the application source code to set the TTL attribute appropriately.
C. Create a DynamoDB stream that has an AWS Lambda function that reacts to data modifications.Configure the Lambda function to delete items that are older than the specified age.
D. Add a lifecycle configuration to the DynamoDB table that deletes items that are older than the specified age.

Answer: B

Explanation:
DynamoDB Time to Live (TTL) is a fully managed feature that automatically expires and deletes items based on a timestamp attribute you define. By enabling TTL and setting the TTL attribute in the application code, items older than the specified age are removed without the need for custom scheduling or Lambda functions, offering the least configuration effort.

NEW QUESTION # 253
A data engineer is building a data orchestration workflow. The data engineer plans to use a hybrid model that includes some on-premises resources and some resources that are in the cloud. The data engineer wants to prioritize portability and open source resources.
Which service should the data engineer use in both the on-premises environment and the cloud- based environment?

A. AWS Data Exchange
B. AWS Glue
C. Amazon Managed Workflows for Apache Airflow (Amazon MWAA)
D. Amazon Simple Workflow Service (Amazon SWF)

Answer: C

Explanation:
Amazon MWAA is a managed service for Apache Airflow, which is an open-source workflow automation tool. Apache Airflow can be used both on-premises and in the cloud, making it ideal for hybrid environments. Using Amazon MWAA allows the data engineer to leverage the managed service in the cloud while maintaining the ability to use the same open-source Airflow setup on-premises, ensuring portability and consistency across environments.

NEW QUESTION # 254
A company adds new data to a large CSV file in an Amazon S3 bucket every day. The file contains company sales data from the previous 5 years. The file currently includes more than
5,000 rows. The CSV file structure is shown below with sample data:

The company needs to use Amazon Athena to run queries on the CSV file to fetch data from a specific time period.
Which solution will meet this requirement MOST cost-effectively?

A. Launch an Amazon EMR cluster. Specify AWS Glue Data Catalog as the default Apache Hive metastore. Use Presto in Trino to run queries on the data.
B. Create an Amazon RDS database. Create a table named SALES that matches the schema of the CSV file. Create an index on the SALE_DATE column. Create an AWS Lambda function to load the CSV data into the RDS database. Use S3 Event Notifications to invoke the Lambda function.
C. Write an Apache Spark script to convert the CSV data to JSON format. Create an AWS Glue job to run the script every day. Catalog the JSON data in AWS Glue. Run the Athena queries on the JSON data.
D. Use prefixes to partition the data in the S3 bucket. Use the SALE_DATE column to create a partition for each day. Catalog the data in AWS Glue and ensure that the partitions are added.
Update the Athena queries to use the new partitions.

Answer: D

Explanation:
Partitioning the S3 data by SALE_DATE (daily prefixes) lets Athena prune non-matching partitions so it scans only the requested time range, minimizing bytes scanned and cost while working directly on the existing CSVs. Cataloging the partitions in AWS Glue enables efficient querying with minimal operational overhead.

NEW QUESTION # 255
Two data engineering teams use separate AWS accounts. Both teams request access to the same datashare in an Amazon Redshift cluster that is in a third AWS account. The datashare is named salesshare.
A data engineer must use the Amazon Redshift SQL interface to grant both data engineering teams' access to the datashare.
Which command or commands will meet this requirement?

A. GRANT USAGE ON DATASHARE salesshare TO NAMESPACE '<account ID 1>';GRANT USAGE ON DATASHARE salesshare TO NAMESPACE '<account ID 2>';
B. GRANT USAGE ON DATASHARE salesshare TO NAMESPACES '<account ID 1>' AND
'<account ID 2>';
C. GRANT USAGE ON DATASHARE salesshare TO ACCOUNT '<account ID 1>';
GRANT USAGE ON DATASHARE salesshare TO ACCOUNT '<account ID 2>';
D. GRANT USAGE ON DATASHARE salesshare TO ACCOUNTS '<account ID 1>' AND '<account ID 2>';

Answer: A

Explanation:
In Amazon Redshift data sharing, cross-account permissions for a datashare are granted to consumer cluster/serverless namespaces. Granting USAGE on the datashare to each consumer namespace enables both teams' Redshift environments in their respective accounts to create and query databases from the shared data.

NEW QUESTION # 256
A company uses Amazon RDS to store transactional data. The company runs an RDS DB instance in a private subnet. A developer wrote an AWS Lambda function with default settings to insert, update, or delete data in the DB instance.
The developer needs to give the Lambda function the ability to connect to the DB instance privately without using the public internet.
Which combination of steps will meet this requirement with the LEAST operational overhead?
(Choose two.)

A. Update the network ACL of the private subnet to include a self-referencing rule that allows access through the database port.
B. Configure the Lambda function to run in the same subnet that the DB instance uses.
C. Attach the same security group to the Lambda function and the DB instance. Include a self- referencing rule that allows access through the database port.
D. Update the security group of the DB instance to allow only Lambda function invocations on the database port.
E. Turn on the public access setting for the DB instance.

Answer: B,D

NEW QUESTION # 257
......

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