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Snowflake DEA-C01 Exam Syllabus Topics:

Topic	Details
Topic 1	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 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	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 4	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 5	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.

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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q34-Q39):

NEW QUESTION # 34
A company created an extract, transform, and load (ETL) data pipeline in AWS Glue. A data engineer must crawl a table that is in Microsoft SQL Server. The data engineer needs to extract, transform, and load the output of the crawl to an Amazon S3 bucket. The data engineer also must orchestrate the data pipeline.
Which AWS service or feature will meet these requirements MOST cost-effectively?

A. Amazon Managed Workflows for Apache Airflow (Amazon MWAA)
B. AWS Step Functions
C. AWS Glue Studio
D. AWS Glue workflows

Answer: D

Explanation:
https://aws.amazon.com/blogs/big-data/orchestrate-an-etl-pipeline-using-aws-glue-workflows- triggers-and-crawlers-with-custom-classifiers/
https://aws.amazon.com/blogs/big-data/extracting-multidimensional-data-from-microsoft-sql- server-analysis-services-using-aws-glue/

NEW QUESTION # 35
A company uses Amazon SageMaker AI for its machine learning (ML) workflows. The company is organized into several project groups that use sensitive data. The company needs to give the project groups the ability to discover available datasets across different AWS accounts. The solution must maintain access controls and track all data access for compliance purposes.
Which solution will meet these requirements with the LEAST operational overhead?

A. Create separate Amazon SageMaker Studio domains for each project group with isolated environments and no ability to share data between domains.
B. Use Amazon SageMaker Assets to publish, discover, and request access to datasets through the asset catalog with approval workflows that track data access.
C. Set up Amazon SageMaker Feature Store with cross-account access policies to automatically share data between AWS accounts without requiring approval workflows.
D. Set up IAM roles for each project group with permissions to access all datasets across all AWS accounts. Use AWS CloudTrail to record data access activity.

Answer: B

Explanation:
Amazon SageMaker Assets (via SageMaker Catalog) provides a centralized way to publish and discover datasets across accounts with built-in governance. It supports approval workflows to control access and automatically tracks dataset usage for compliance, meeting the requirements with minimal operational overhead while maintaining proper access controls.

NEW QUESTION # 36
A company stores information about its subscribers in an Amazon S3 bucket. The company runs an analysis every time a subscriber ends their subscription. The company uses AWS Lambda functions to respond to events from the S3 bucket by performing analyses.
The Lambda functions clean data from the S3 bucket and initiate an AWS Glue workflow. The Lambda functions have 128 MB of memory and 512 MB of ephemeral storage. The Lambda functions have a timeout of 15 seconds.
All three functions successfully finish running. However, CPU usage is often near 100%, which causes slow performance. The company wants to improve the performance of the functions and reduce the total runtime of the pipeline.
Which solution will meet these requirements?

A. Increase the timeout value for the Lambda functions from 15 seconds to 30 seconds.
B. Increase the memory of the Lambda functions to 512 MB.
C. Configure the Lambda functions to run in the company's VPC.
D. Increase the number of retries by using the Maximum Retry Attempts setting.

Answer: B

Explanation:
In AWS Lambda, increasing memory also proportionally increases the allocated CPU. By raising the Lambda functions' memory from 128 MB to 512 MB, the functions receive more CPU power, which reduces execution time and improves performance without changing logic or timeouts.

NEW QUESTION # 37
A data engineer needs to use Amazon Neptune to develop graph applications.
Which programming languages should the engineer use to develop the graph applications?
(Choose two.)

A. SPARQL
B. SQL
C. Spark SQL
D. Gremlin
E. ANSI SQL

Answer: A,D

NEW QUESTION # 38
How Data Engineer can do Monitoring of Files which are Staged Internally during Continuous data pipelines loading process? [Select all that apply]

A. Snowflake retains historical data for COPY INTO commands executed within the pre-vious 14 days.
B. She can Monitor the status of each COPY INTO <table> command on the History tab page of the classic web interface.
C. She can use the DATA_LOAD_HISTORY Information Schema view to retrieve the history of data loaded into tables using the COPY INTO command.
D. She can use the DATA_VALIDATE function to validate the data files She have loaded and can retrieve any errors encountered during the load.
E. She Can Monitor the files using Metadata maintained by Snowflake i.e. file-name,last_modified date etc.

Answer: A,B,E

Explanation:
Explanation
Monitoring Files Staged Internally
Snowflake maintains detailed metadata for each file uploaded into internal stage (for users, tables, and stages), including:
File name
File size (compressed, if compression was specified during upload)
LAST_MODIFIED date, i.e. the timestamp when the data file was initially staged or when it was last modified, whichever is later In addition, Snowflake retains historical data for COPY INTO commands executed within the pre-vious 14 days. The metadata can be used to monitor and manage the loading process, including de-leting files after upload completes:
Use the LIST command to view the status of data files that have been staged.
Monitor the status of each COPY INTO <table> command on the History tab page of the classic web interface.
Use the VALIDATE function to validate the data files you've loaded and retrieve any errors en-countered during the load.
Use the LOAD_HISTORY Information Schema view to retrieve the history of data loaded into tables using the COPY INTO command.

NEW QUESTION # 39
......

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