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Snowflake DEA-C01 Exam Syllabus Topics:

Topic	Details
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	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 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	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 (Q96-Q101):

NEW QUESTION # 96
A company stores customer records in Amazon S3. The company must not delete or modify the customer record data for 7 years after each record is created. The root user also must not have the ability to delete or modify the data.
A data engineer wants to use S3 Object Lock to secure the data.
Which solution will meet these requirements?

A. Set the retention period for individual objects in the S3 bucket to 7 years.
B. Place a legal hold on individual objects in the S3 bucket. Set the retention period to 7 years.
C. Enable compliance mode on the S3 bucket. Use a default retention period of 7 years.
D. Enable governance mode on the S3 bucket. Use a default retention period of 7 years.

Answer: C

Explanation:
Compliance Mode is the stricter mode of S3 Object Lock. When enabled, it ensures that no one, not even the root user, can modify or delete the object until the retention period expires. In this case, to meet the requirement of preventing any deletion or modification of the data for 7 years, compliance mode is the correct choice.
Governance Mode allows some users with special permissions (such as the root user) to override the lock and delete or modify the data, which does not meet the strict requirement that even the root user should not have this ability.
Legal Hold is used to prevent deletion of objects without specifying a time-based retention period, but it can be removed by users with sufficient permissions, and the root user might still be able to delete the data.
Retention Period for Individual Objects is possible, but enabling compliance mode provides a more robust solution by applying the same 7-year retention policy bucket-wide, ensuring that no modifications or deletions can occur during that period.

NEW QUESTION # 97
A data engineer needs a fully automated solution to check for new data in multiple databases and process data that the solution finds. The solution must run every hour. The solution must be compatible with Amazon RDS, Amazon DynamoDB, and Amazon OpenSearch Service. The solution must be able to process up to 10 MB of data at one time. The solution must be optimized for costs and operational overhead. The solution must have robust error handling capabilities.
Which solution will meet these requirements?

A. Configure an Apache Spark application to run on Amazon EMR to check for data. Implement error handling in the application. Use Amazon EventBridge to invoke the application every hour.
B. Use Amazon EventBridge to invoke an AWS Lambda function every hour to check for data.
Configure the function to send a message to an Amazon Simple Queue Service (Amazon SQS) queue when the function finds new data. Use a second Lambda function to read the queue and perform the processing.
C. Use Amazon Managed Workflows for Apache Airflow (Amazon MWAA) to create a workflow that runs a directed acyclic graph (DAG) every hour to check for data. Configure the DAG to process identified data. Implement error handling in a Python operator.
D. Use Amazon EventBridge to invoke AWS Step Functions every hour to deploy an AWS Lambda function to check for data. Configure Step Functions steps to process data that the Lambda function finds. Implement error handling in each state.

Answer: D

Explanation:
Amazon EventBridge can invoke AWS Step Functions on an hourly schedule, and Step Functions is a serverless orchestration service that is well suited for coordinating checks across multiple data sources while providing built-in retry and catch mechanisms for robust error handling. AWS Lambda can connect to Amazon RDS, Amazon DynamoDB, and Amazon OpenSearch Service through AWS SDKs, and Lambda can handle workloads well beyond 10 MB in memory, so this design meets the processing-size requirement with low cost and minimal operational overhead compared with EMR or MWAA.
Reference:
https://docs.aws.amazon.com/step-functions/latest/dg/tutorial-handling-error-conditions.html
https://docs.aws.amazon.com/opensearch-service/latest/developerguide/configuration- samples.html
https://docs.aws.amazon.com/lambda/latest/dg/configuration-memory.html
https://docs.aws.amazon.com/step-functions/latest/dg/workflow-studio-process-error.html

NEW QUESTION # 98
A company wants to use Apache Spark jobs that run on an Amazon EMR cluster to process streaming data. The Spark jobs will transform and store the data in an Amazon S3 bucket. The company will use Amazon Athena to perform analysis.
The company needs to optimize the data format for analytical queries.
Which solutions will meet these requirements with the SHORTEST query times? (Choose two.)

A. Use ORC format. Use AWS Glue Data Catalog to track schema changes.
B. Use Apache Parquet format. Use AWS Glue Data Catalog to track schema changes.
C. Use ORC format. Store schema definitions in separate files in Amazon S3.
D. Use Apache Parquet format. Use an external Amazon DynamoDB table to track schema changes.
E. Use Avro format. Use AWS Glue Data Catalog to track schema changes.

Answer: A,B

Explanation:
ORC is a columnar format optimized for Athena and provides high compression and predicate pushdown for faster queries; Glue Data Catalog manages schema evolution.
Parquet is a columnar format optimized for Athena with efficient compression and predicate pushdown; Glue Data Catalog tracks schema changes.

NEW QUESTION # 99
During a security review, a company identified a vulnerability in an AWS Glue job. The company discovered that credentials to access an Amazon Redshift cluster were hard coded in the job script.
A data engineer must remediate the security vulnerability in the AWS Glue job. The solution must securely store the credentials.
Which combination of steps should the data engineer take to meet these requirements? (Choose two.)

A. Grant the AWS Glue job IAM role access to the stored credentials.
B. Store the credentials in the AWS Glue job parameters.
C. Store the credentials in a configuration file that is in an Amazon S3 bucket.
D. Store the credentials in AWS Secrets Manager.
E. Access the credentials from a configuration file that is in an Amazon S3 bucket by using the AWS Glue job.

Answer: A,D

NEW QUESTION # 100
Robert, A Data Engineer, found that Pipe become stale as it was paused for longer than the limited retention period for event messages received for the pipe (14 days by default) & also the previous pipe owner transfers the ownership of this pipe to Robert role while the pipe was paused. How Robert in this case, Resume this stale pipe?

A. Robert can use SYSTEM$PIPE_FORCE_RESUME function to resume this stale pipe.
B. select sys-tem$pipe_force_resume('mydb.myschema.stalepipe','staleness_check_override, ownership_transfer_check_override');
C. He can apply System function SYSTEM$PIPE_STALE_RESUME with ALTER PIPE statement.
D. PIPE needs to recreate in this scenario, as pipe already past 14 days of period & stale.
E. ALTER PIPES ... RESUME statement will resume the pipe.

Answer: B

Explanation:
Explanation
When a pipe is paused, event messages received for the pipe enter a limited retention period. The period is 14 days by default. If a pipe is paused for longer than 14 days, it is considered stale.
To resume a stale pipe, a qualified role must call the SYSTEM$PIPE_FORCE_RESUME function and input the STALENESS_CHECK_OVERRIDE argument. This argument indicates an under-standing that the role is resuming a stale pipe.
For example, resume the stale stalepipe1 pipe in the mydb.myschema database and schema:
SELECT SYS-TEM$PIPE_FORCE_RESUME('mydb.myschema.stalepipe1','staleness_check_override'); While the stale pipe was paused, if ownership of the pipe was transferred to another role, then re-suming the pipe requires the additional OWNERSHIP_TRANSFER_CHECK_OVERRIDE argu-ment. For example, resume the stale stalepipe2 pipe in the mydb.myschema database and schema, which transferred to a new role:
SELECT SYS-TEM$PIPE_FORCE_RESUME('mydb.myschema.stalepipe1','staleness_check_override, own-ership_transfer_check_override');

NEW QUESTION # 101
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