Amazon Data-Engineer-Associate

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Amazon AWS Certified Data Engineer - Associate (DEA-C01) Sample Questions:

1. A company has five offices in different AWS Regions. Each office has its own human resources (HR) department that uses a unique IAM role. The company stores employee records in a data lake that is based on Amazon S3 storage.
A data engineering team needs to limit access to the records. Each HR department should be able to access records for only employees who are within the HR department ' s Region.
Which combination of steps should the data engineering team take to meet this requirement with the LEAST operational overhead? (Choose two.)

A) Register the S3 path as an AWS Lake Formation location.
B) Enable fine-grained access control in AWS Lake Formation. Add a data filter for each Region.
C) Use data filters for each Region to register the S3 paths as data locations.
D) Create a separate S3 bucket for each Region. Configure an IAM policy to allow S3 access. Restrict access based on Region.
E) Modify the IAM roles of the HR departments to add a data filter for each department ' s Region.

2. A company is setting up a data pipeline in AWS. The pipeline extracts client data from Amazon S3 buckets, performs quality checks, and transforms the data. The pipeline stores the processed data in a relational database. The company will use the processed data for future queries.
Which solution will meet these requirements MOST cost-effectively?

A) Use AWS Glue ETL to extract the data from the S3 buckets and perform the transformations. Use AWS Glue DataBrew to perform quality checks. Load the processed data and the quality check results into a new S3 bucket.
B) Use AWS Glue Studio to extract the data from the S3 buckets. Use AWS Glue DataBrew to perform the transformations and quality checks. Load the processed data into an Amazon RDS for MySQL instance. Load the quality check results into a new S3 bucket.
C) Use AWS Glue Studio to extract the data from the S3 buckets. Use AWS Glue DataBrew to perform the transformations and quality checks. Load the processed data and quality check results into an Amazon RDS for MySQL instance.
D) Use AWS Glue ETL to extract the data from the S3 buckets and perform the transformations. Use AWS Glue Data Quality to enforce suggested quality rules. Load the data and the quality check results into an Amazon RDS for MySQL instance.

3. A data engineer needs to maintain a central metadata repository that users access through Amazon EMR and Amazon Athena queries. The repository needs to provide the schema and properties of many tables. Some of the metadata is stored in Apache Hive. The data engineer needs to import the metadata from Hive into the central metadata repository.
Which solution will meet these requirements with the LEAST development effort?

A) Use the AWS Glue Data Catalog.
B) Use a metastore on an Amazon RDS for MySQL DB instance.
C) Use a Hive metastore on an EMR cluster.
D) Use Amazon EMR and Apache Ranger.

4. A company ' s data engineer needs to optimize the performance of table SQL queries. The company stores data in an Amazon Redshift cluster. The data engineer cannot increase the size of the cluster because of budget constraints.
The company stores the data in multiple tables and loads the data by using the EVEN distribution style. Some tables are hundreds of gigabytes in size. Other tables are less than 10 MB in size.
Which solution will meet these requirements?

A) Keep using the EVEN distribution style for all tables. Specify primary and foreign keys for all tables.
B) Specify a combination of distribution, sort, and partition keys for all tables.
C) Use the ALL distribution style for large tables. Specify primary and foreign keys for all tables.
D) Use the ALL distribution style for rarely updated small tables. Specify primary and foreign keys for all tables.

5. A data engineer is building a serverless, multi-step extract, transform, and load (ETL) pipeline. The pipeline extracts data from an Amazon S3 data lake and transforms the data by using AWS Glue ETL jobs. The pipeline then loads the results into an Amazon Redshift database. The data engineer needs to orchestrate the serverless ETL workflow.
Which solutions will meet these requirements? (Select TWO.)

A) Provision an always-on Amazon EC2 instance. Create a cron job that invokes the AWS Glue ETL jobs in sequence based on a predefined schedule.
B) Implement the workflow by using AWS Step Functions. Configure Step Functions to coordinate the AWS Glue ETL jobs and handle error conditions with automatic retries.
C) Build an orchestration solution by using AWS CodePipeline to coordinate the ETL pipeline and infrastructure changes based on the dependencies.
D) Use Amazon EventBridge rules to invoke the AWS Glue ETL jobs based on S3 object creation events.
Configure the rules to chain the AWS Glue ETL jobs in sequence and handle complex job dependencies.
E) Use AWS Glue workflows to create a graph of the ETL tasks that visually represents the dependencies between jobs and the job triggers.

Solutions: