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Snowflake DEA-C01 Exam Syllabus Topics:

Topic	Details
Topic 1	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 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	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	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 (Q152-Q157):

NEW QUESTION # 152
A data engineer must ingest a source of structured data that is in .csv format into an Amazon S3 data lake. The .csv files contain 15 columns. Data analysts need to run Amazon Athena queries on one or two columns of the dataset. The data analysts rarely query the entire file.
Which solution will meet these requirements MOST cost-effectively?

A. Use an AWS Glue PySpark job to ingest the source data into the data lake in Apache Avro format.
B. Use an AWS Glue PySpark job to ingest the source data into the data lake in .csv format.
C. Create an AWS Glue extract, transform, and load (ETL) job to read from the .csv structured data source. Configure the job to write the data into the data lake in Apache Parquet format.
D. Create an AWS Glue extract, transform, and load (ETL) job to read from the .csv structured data source. Configure the job to ingest the data into the data lake in JSON format.

Answer: C

NEW QUESTION # 153
A company processes 500 GB of audience and advertising data daily, storing CSV files in Amazon S3 with schemas registered in AWS Glue Data Catalog. They need to convert these files to Apache Parquet format and store them in an S3 bucket.
The solution requires a long-running workflow with 15 GiB memory capacity to process the data concurrently, followed by a correlation process that begins only after the first two processes complete.
Which solution will meet these requirements with the LEAST operational overhead?

A. Use Amazon EMR to run each process in the workflow. Create an Amazon Simple Queue Service (Amazon SQS) queue to handle messages that indicate the completion of the first two processes. Configure an AWS Lambda function to process the SQS queue by running the third process.
B. Use Amazon Managed Workflows for Apache Airflow (Amazon MWAA) to orchestrate the workflow by using AWS Glue. Configure AWS Glue to begin the third process after the first two processes have finished.
C. Use AWS Glue workflows to run the first two processes in parallel. Ensure that the third process starts after the first two processes have finished.
D. Use AWS Step Functions to orchestrate a workflow that uses multiple AWS Lambda functions.Ensure that the third process starts after the first two processes have finished.

Answer: C

Explanation:
AWS Glue workflows natively orchestrate multiple Glue jobs in parallel and support dependency triggers so the third job starts only after the first two finish. Glue jobs provide the required ~15 GiB memory capacity and easily convert CSV to Parquet using the Glue Data Catalog, delivering a fully managed, low-operations solution.

NEW QUESTION # 154
A data engineer is using an Apache Iceberg framework to build a data lake that contains 100 TB of data. The data engineer wants to run AWS Glue Apache Spark jobs that use the Iceberg framework.
What combination of steps will meet these requirements? (Choose two.)

A. Create a key named --conf for an AWS Glue job. Set Iceberg as a value for the --datalake-formats job parameter.
B. Set Iceberg as a value for the --datalake-formats job parameter.
C. Set the --enable-auto-scaling parameter to true.
D. Specify the path to a specific version of Iceberg by using the -extra-jars job parameter. Set Iceberg as a value for the datalake-formats job parameter.
E. Add the --job-bookmark-option: job-bookmark-enable parameter to an AWS Glue job.

Answer: B,D

Explanation:
To run AWS Glue Apache Spark jobs with the Apache Iceberg framework, you must:
Set --datalake-formats to iceberg so Glue knows to use the Iceberg format.
Specify the Iceberg library by using the --extra-jars parameter to include the appropriate Iceberg JAR files for Glue to access the Iceberg table format.

NEW QUESTION # 155
A company stores analysis results for thousands of daily customer service call transcripts in an Amazon DynamoDB table. An application generates business reports by using only the previous
7 working days of data. To reduce costs, the company wants to implement a solution that automatically removes data that is older than 7 working days.
Which solution will meet this requirement with the LEAST operational overhead?

A. Create a new DynamoDB table. Enable TTL on the new table. Migrate the data to the new table.
Update the application to use the new table.
B. Enable TTL on the DynamoDB table. Add an expiration timestamp attribute to each item in the table.
C. Use Amazon EventBridge to invoke an AWS Lambda function on a schedule to delete old records every day.
D. Use AWS Glue jobs to implement a daily batch job to scan for and delete expired items from the DynamoDB table.

Answer: B

Explanation:
DynamoDB TTL is the simplest native feature for automatically removing old items with minimal administration. By storing an expiration timestamp on each item and enabling TTL on the table, the company can let DynamoDB handle deletion of expired data automatically without building and maintaining scheduled jobs, Lambda code, or migration steps.

NEW QUESTION # 156
A financial services company stores financial data in Amazon Redshift. A data engineer wants to run real-time queries on the financial data to support a web-based trading application. The data engineer wants to run the queries from within the trading application.
Which solution will meet these requirements with the LEAST operational overhead?

A. Use the Amazon Redshift Data API.
B. Establish WebSocket connections to Amazon Redshift.
C. Store frequently accessed data in Amazon S3. Use Amazon S3 Select to run the queries.
D. Set up Java Database Connectivity (JDBC) connections to Amazon Redshift.

Answer: A

Explanation:
https://docs.aws.amazon.com/redshift/latest/mgmt/data-api.html

NEW QUESTION # 157
......

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