Snowflake SnowPro Advanced: Data Engineer Certification : DEA-C01

A company needs to build a data lake in AWS. The company must provide row-level data access and column-level data access to specific teams. The teams will access the data by using Amazon Athena, Amazon Redshift Spectrum, and Apache Hive from Amazon EMR.
Which solution will meet these requirements with the LEAST operational overhead?

• A. Use Amazon S3 for data lake storage. Use AWS Lake Formation to restrict data access by rows and columns. Provide data access through AWS Lake Formation.
• B. Use Amazon Redshift for data lake storage. Use Redshift security policies to restrict data access by rows and columns. Provide data access by using Apache Spark and Amazon Athena federated queries.
• C. Use Amazon S3 for data lake storage. Use S3 access policies to restrict data access by rows and columns. Provide data access through Amazon S3.
• D. Use Amazon S3 for data lake storage. Use Apache Ranger through Amazon EMR to restrict data access by rows and columns. Provide data access by using Apache Pig.

A company stores employee data in Amazon Resdshift. A table names Employee uses columns named Region ID, Department ID, and Role ID as a compound sort key.
Which queries will MOST increase the speed of query by using a compound sort key of the table?
(Choose two.)

• A. Select *from Employee where Region ID='North America';
• B. Select *from Employee where Region ID='North America' and Department ID=20;
• C. Select *from Employee where Department ID=20 and Region ID='North America';
• D. Select *from Employee where Role ID=50;
• E. Select *from Employee where Region ID='North America' and Role ID=50;

A legal company is building a data pipeline to power an application that will handle peak traffic during business hours. The application will provide information about relevant laws and available lawyers. The legal document database will be updated one time each day.
The application must display up-to-date lawyer availability from a calendar database and provide complex full-text search of legal documents. The company wants to use AWS Glue for extract, transform, and load (ETL) processes. Lawyer availability information must be current within 5 minutes of any schedule changes.
Which solution will meet these requirements with the LEAST operational overhead?

• A. Use AWS Glue workflows with time-based triggers and event-based triggers. Store processed data in Amazon DynamoDB. Create a custom search solution by using AWS Lambda functions.
• B. Use AWS Step Functions to orchestrate AWS Glue jobs with time-based triggers and event-based triggers. Store processed data in Amazon S3. Use Amazon OpenSearch Service for full-text search capabilities.
• C. Use AWS Step Functions to orchestrate AWS Glue jobs with time-based triggers and event-based triggers. Store processed data in Amazon S3. Use Amazon RDS for the search functionality.
• D. Use Amazon EventBridge to schedule all AWS Glue jobs. Store processed data in Amazon RDS.Use Amazon Kendra for full-text search capabilities.

A data engineer is configuring an AWS Glue Apache Spark extract, transform, and load (ETL) job. The job contains a sort-merge join of two large and equally sized DataFrames.
The job is failing with the following error: No space left on device.
Which solution will resolve the error?

• A. Convert the sort-merge join in the job to be a broadcast join.
• B. Use the AWS Glue Spark shuffle manager.
• C. Convert the DataFrames to DynamicFrames, and perform a DynamicFrame join in the job.
• D. Deploy are Amazon Elastic Block Store (Amazon EBS) volume for the job to use.

A data engineer is using an AWS Glue ETL job to remove outdated customer records from a table that contains customer account information. The data engineer is using the following SQL command to remove customers that exist in a table named monthly_accounts_update table from the customer accounts table:
MERGE INTO accounts t USING monthly_accounts_update s
ON t.customer = s.customer
WHEN MATCHED
THEN DELETE
What will happen when the data engineer runs the SQL command?

• A. The monthly_accounts_update table will be deleted.
• B. All customer records that exist in both the customer accounts table and the monthly_accounts_update table will be deleted from the accounts table.
• C. No records will be deleted because the command syntax is not valid in AWS Glue.
• D. Only customer records that are present in both tables will be retained in the customer accounts table.