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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	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	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 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.

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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q65-Q70):

NEW QUESTION # 65
A retail company stores point-of-sale transaction data in an Amazon RDS for MySQL database.
The company maintains historical sales analytics in Amazon Redshift. The company needs to create daily reports that combine the current day's transactions with historical sales patterns for trend analysis. The company requires a solution that provides near real-time insights while minimizing data transfer costs and maintenance overhead.
Which solution will meet these requirements?

A. Use AWS Glue to create an extract, transform, and load (ETL) pipeline that runs every hour to copy incremental data from RDS for MySQL to Amazon Redshift. Generate reports.
B. Configure AWS Database Migration Service (AWS DMS) to continuously replicate data from RDS for MySQL to Amazon Redshift. Use Redshift queries to create consolidated reports.
C. Export RDS for MySQL data to an Amazon S3 bucket on a regular schedule. Use the COPY command to load the data into Amazon Redshift staging tables. Join the data with historical data.
D. Implement Amazon Redshift federated queries to directly access RDS for MySQL data and join it with existing Redshift tables in a single query.

Answer: B

Explanation:
AWS DMS with ongoing replication (CDC) continuously streams new RDS for MySQL transactions into Amazon Redshift, providing near real-time freshness with minimal maintenance and transferring only changes instead of full batches. Reports can then run in Redshift joining current and historical data without hourly ETL jobs or S3 staging.

NEW QUESTION # 66
A data engineering team is using an Amazon Redshift data warehouse for operational reporting.
The team wants to prevent performance issues that might result from long- running queries. A data engineer must choose a system table in Amazon Redshift to record anomalies when a query optimizer identifies conditions that might indicate performance issues.
Which table views should the data engineer use to meet this requirement?

A. STL_ALERT_EVENT_LOG
B. STL_USAGE_CONTROL
C. STL_PLAN_INFO
D. STL_QUERY_METRICS

Answer: A

Explanation:
https://docs.aws.amazon.com/redshift/latest/dg/cm_chap_system-tables.html STL_ALERT_EVENT_LOG table view to meet this requirement. This system table in Amazon Redshift is designed to record anomalies when a query optimizer identifies conditions that might indicate performance issues.

NEW QUESTION # 67
A data engineer must orchestrate a series of Amazon Athena queries that will run every day.
Each query can run for more than 15 minutes.
Which combination of steps will meet these requirements MOST cost-effectively? (Choose two.)

A. Use an AWS Lambda function and the Athena Boto3 client start_query_execution API call to invoke the Athena queries programmatically.
B. Create an AWS Step Functions workflow and add two states. Add the first state before the Lambda function. Configure the second state as a Wait state to periodically check whether the Athena query has finished using the Athena Boto3 get_query_execution API call. Configure the workflow to invoke the next query when the current query has finished running.
C. Use Amazon Managed Workflows for Apache Airflow (Amazon MWAA) to orchestrate the Athena queries in AWS Batch.
D. Use an AWS Glue Python shell script to run a sleep timer that checks every 5 minutes to determine whether the current Athena query has finished running successfully. Configure the Python shell script to invoke the next query when the current query has finished running.
E. Use an AWS Glue Python shell job and the Athena Boto3 client start_query_execution API call to invoke the Athena queries programmatically.

Answer: A,B

NEW QUESTION # 68
A data engineer uses Amazon Managed Workflows for Apache Airflow (Amazon MWAA) to run data pipelines in an AWS account.
A workflow recently failed to run. The data engineer needs to use Apache Airflow logs to diagnose the failure of the workflow.
Which log type should the data engineer use to diagnose the cause of the failure?

A. YourEnvironmentName-Task
B. YourEnvironmentName-DAGProcessing
C. YourEnvironmentName-Scheduler
D. YourEnvironmentName-WebServer

Answer: A

Explanation:
When a workflow fails to run in Amazon MWAA, the task logs (YourEnvironmentName-Task) are the most relevant for diagnosing the issue. Task logs contain detailed information about the execution of individual tasks within the workflow, including any error messages or stack traces that can help pinpoint the cause of the failure.

NEW QUESTION # 69
To support Time Travel, Which of the following SQL extensions/parameters/commands have been implemented?

A. ONSET (time difference in seconds from the present time)
B. STATEMENT_ID (identifier for statement, e.g. query ID)
C. UNDROP command for tables, schemas, and databases.
D. AT | BEFORE clause which can be specified in the CREATE ... CLONE commands.
E. STATEMENT (identifier for statement, e.g. query ID)
F. OFFSET (time difference in seconds from the present time)

Answer: C,D,E,F

NEW QUESTION # 70
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