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	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	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	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.

Reference: https://learn.snowflake.com/en/certifications/snowpro-advanced-dataengineer/

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Snowflake SnowPro Advanced: Data Engineer Certification Sample Questions:

Question #1

A company needs to build a data pipeline to process a 1-TB file from an Amazon S3 bucket. The pipeline needs to create three DataFrames based on business logic. The pipeline must save all three DataFrames to a second S3 bucket in parallel. The company needs to set the pipeline to be the target of an Amazon EventBridge rule that matches file uploads to the source S3 bucket.
Which solution will meet these requirements with the LEAST maintenance overhead?

A. Configure an AWS Glue workflow to run three AWS Glue jobs in parallel to process the file.
B. Configure an AWS Step Functions state machine to initiate an AWS Glue workflow to run three AWS Glue jobs in parallel to process the file.
C. Configure three AWS Lambda functions to process the business logic and to save the DataFrames to the destination S3 bucket in parallel.
D. Configure an Apache Spark Streaming application on Amazon EMR to process data from the S3 source bucket in batches, create DataFrames, and save the output to the destination S3 bucket.

Reveal Solution Discussion 0

Correct Answer: A 🗳️