Free Amazon AWS-DEA-C01 Exam Questions

Question 1

A company needs to partition the Amazon S3 storage that the company uses for a data lake. The partitioning will use a path of the S3 object keys in the following format:

s3://bucket/prefix/year=2023/month=01/day=01

A data engineer must ensure that the AWS Glue Data Catalog synchronizes with the S3 storage when the company adds new partitions to the bucket.

Which solution will meet these requirements with the LEAST latency?

A : Manually run the AWS Glue CreatePartition API twice each day.

B : Run the MSCK REPAIR TABLE command from the AWS Glue console.

C : Schedule an AWS Glue crawler to run every morning.

D : Use code that writes data to Amazon S3 to invoke the Boto3 AWS Glue create_partition API call.

Answer: B

Question 2

A Cloud Data Engineer is designing a serverless data processing pipeline on AWS. The pipeline uses AWS Lambda for data transformation, AWS Step Functions for workflow management, and Amazon DynamoDB for storing intermediate data. The engineer decides to use the AWS Serverless Application Model (AWS SAM) to manage the deployment of these components. The primary requirement is to ensure that the deployment process is repeatable and easily modifiable.

What should the engineer include in the SAM template to meet these requirements efficiently?

A : Include the Lambda functions and DynamoDB tables in the SAM template, but exclude the Step Functions, as they are not supported by AWS SAM.

B : Specify only the Lambda function in the SAM template, manually configuring the Step Functions and DynamoDB tables through the AWS Console.

C : Use separate SAM templates for each component (Lambda, Step Functions, DynamoDB) to manage deployments individually for better control.

D : Define all the components (Lambda functions, Step Functions, DynamoDB tables) in the SAM template, and use AWS CloudFormation to deploy the entire pipeline

Answer: D

Question 3

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 4

Files from multiple data sources arrive in an Amazon S3 bucket on a regular basis. A data engineer wants to ingest new files into Amazon Redshift in near real time when the new files arrive in the S3 bucket. Which solution will meet these requirements?

A : Use the query editor v2 to schedule a COPY command to load new files into Amazon Redshift.

B : Use the zero-ETL integration between Amazon Aurora and Amazon Redshift to load new files into Amazon Redshift.

C : Use AWS Glue job bookmarks to extract, transform, and load (ETL) load new files into Amazon Redshift.

D : Use S3 Event Notifications to invoke an AWS Lambda function that loads new files into Amazon Redshift.

Answer: D

Question 5

As a Data Engineer, you are tasked with setting up a batch data ingestion pipeline for a substantial financial dataset. This data is currently housed in an Amazon RDS database, stemming from various financial services and trading platforms. The dataset is complex and heterogeneous, predominantly in JSON format, and is updated nightly, typically ranging between 100-200 GB.

Your primary objective is to efficiently transfer this data from the RDS database to a data lake in AWS, ensuring reliable storage and preparing the dataset for analytical processing with minimal yet capable transformation steps due to its complexity.

Which combination of AWS services would be most appropriate?

A : Amazon AppFlow to facilitate the data transfer from Amazon RDS to Amazon S3. Afterward, utilize AWS Glue ETL for the data transformation processes.

B : Use AWS Database Migration Service (AWS DMS) to continuously replicate data from the Amazon RDS database to Amazon S3. Then, employ AWS Lambda for performing the necessary data transformations.

C : Configure AWS DataSync to transfer the JSON data nightly from the RDS database to Amazon S3. Subsequently, use AWS Glue ETL service to perform the necessary data transformations.

D : Use AWS Database Migration Service (AWS DMS) to continuously replicate data to Amazon S3. Configure an AWS Glue Crawler to update the schema in the Glue Data Catalog, and use Amazon Glue ETL for transformation jobs.

Answer: D