Free Amazon AWS-DEA-C01 Exam Questions

Question 1

A Cloud Data Engineering Consultant has been tasked with organizing access control for a team of analysts using an Amazon Redshift data warehouse. The team requires varying levels of access to sensitive financial data for reporting and analysis. The consultants need to define a strategy that provides the necessary access for the analysts to perform their duties while ensuring that principles of least privilege and data governance are upheld.

Which of the following actions should the consultant take to appropriately manage user access and permissions within the Amazon Redshift database? (Select TWO)

A : Implement row-level security within Redshift tables to control access to rows of data based on user roles, ensuring analysts only see data relevant to their analysis.

B : Grant superuser privileges to all analysts to ensure they have the ability to query all necessary tables within the Redshift cluster, then audit queries periodically for compliance.

C : Enable Amazon Redshift Spectrum to manage external tables and use IAM policies to grant access to these tables.

D : Set up database groups within Redshift and assign users to groups based on their access requirements, then grant group-specific permissions to the relevant schemas and tables.

E : Create individual IAM users for each analyst with attached policies that define the level of access to Redshift resources, and use those IAM users for database authentication.

Answer: A,D

Question 2

A company receives test results from testing facilities that are located around the world. The company storesthe test results in millions of 1 KB JSON files in an Amazon S3 bucket. A data engineer needs to process thefiles, convert them into Apache Parquet format, and load them into Amazon Redshift tables. The dataengineer uses AWS Glue to process the files, AWS Step Functions to orchestrate the processes, and AmazonEventBridge to schedule jobs.The company recently added more testing facilities. The time required to process files is increasing. The dataengineer must reduce the data processing time.Which solution will MOST reduce the data processing time?

A : Use AWS Lambda to group the raw input files into larger files. Write the larger files back to Amazon S3. Use AWS Glue to process the files. Load the files into the Amazon Redshift tables.

B : Use the AWS Glue dynamic frame file-grouping option to ingest the raw input files. Process the files. Load the files into the Amazon Redshift tables.

C : Use the Amazon Redshift COPY command to move the raw input files from Amazon S3 directly into the Amazon Redshift tables. Process the files in Amazon Redshift.

D : Use Amazon EMR instead of AWS Glue to group the raw input files. Process the files in Amazon EMR. Load the files into the Amazon Redshift tables.

Answer: B

Question 3

A Data Engineering team is planning to migrate a complex legacy Microsoft SQL Server database to Amazon Aurora MySQL. The existing SQL Server database contains several stored procedures, triggers, and complex joins. The team is concerned about potential compatibility issues and the effort needed to rewrite substantial amounts of SQL code manually. They aim to use AWS services to facilitate an efficient and accurate schema conversion, ensuring minimal downtime and data consistency during the migration process.

What sequence of steps should the team follow using AWS services to achieve this migration?

A : Use AWS Database Migration Service (AWS DMS) for an initial assessment of the schema conversion, apply the AWS Schema Conversion Tool (AWS SCT) for the actual schema conversion, then use AWS DMS again to migrate the data to Amazon Aurora MySQL.

B : First, apply the AWS Schema Conversion Tool (AWS SCT) to assess and convert the SQL Server schema and SQL code to a format compatible with Aurora MySQL. Following this, use AWS DMS to migrate the data from SQL Server to Aurora MySQL.

C : Directly utilize AWS DMS to migrate the SQL Server database to Amazon Aurora MySQL, relying on the DMS’s built-in schema conversion feature to handle any compatibility issues during the migration.

D : Start with the AWS Schema Conversion Tool (AWS SCT) for converting the SQL Server schema and code to Aurora MySQL, utilize Amazon RDS to temporarily host the converted schema, and then use AWS DMS for the final migration to Aurora MySQL.

Answer: B

Question 4

A company has as JSON file that contains personally identifiable information (PIT) data and non-PII data. The company needs to make the data available for querying and analysis. The non-PII data must be available to everyone in the company. The PII data must be available only to a limited group of employees. Which solution will meet these requirements with the LEAST operational overhead?

A : Store the JSON file in an Amazon S3 bucket. Configure AWS Glue to split the file into one file that contains the PII data and one file that contains the non-PII data. Store the output files in separate S3 buckets. Grant the required access to the buckets based on the type of user.

B : Store the JSON file in an Amazon S3 bucket. Use Amazon Macie to identify PII data and to grant access based on the type of user.

C : Store the JSON file in an Amazon S3 bucket. Catalog the file schema in AWS Lake Formation. Use Lake Formation permissions to provide access to the required data based on the type of user.

D : Create two Amazon RDS PostgreSQL databases. Load the PII data and the non-PII data into the separate databases. Grant access to the databases based on the type of user.

Answer: C

Question 5