Free Amazon AWS-DEA-C01 Exam Questions

Question 1

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 2

A company stores its processed data in an S3 bucket. The company has a strict data access policy. Thecompany uses IAM roles to grant teams within the company different levels of access to the S3 bucket.The company wants to receive notifications when a user violates the data access policy. Each notificationmust include the username of the user who violated the policy.Which solution will meet these requirements?

A : Use AWS Config rules to detect violations of the data access policy. Set up compliance alarms.

B : Use Amazon CloudWatch metrics to gather object-level metrics. Set up CloudWatch alarms.

C : Use AWS CloudTrail to track object-level events for the S3 bucket. Forward events to Amazon CloudWatch to set up CloudWatch alarms.

D : Use Amazon S3 server access logs to monitor access to the bucket. Forward the access logs to an Amazon CloudWatch log group. Use metric filters on the log group to set up CloudWatch alarms.

Answer: C

Question 3

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 4

A sales company uses AWS Glue ETL to collect, process, and ingest data into an Amazon S3 bucket. The AWS Glue pipeline creates a new file in the S3 bucket every hour. File sizes vary from 200 KB to 300 KB. The company wants to build a sales prediction model by using data from the previous 5 years. The historic data includes 44,000 files. The company builds a second AWS Glue ETL pipeline by using the smallest worker type. The second pipeline retrieves the historic files from the S3 bucket and processes the files for downstream analysis. The company notices significant performance issues with the second ETL pipeline. The company needs to improve the performance of the second pipeline. Which solution will meet this requirement MOST cost-effectively?

A : Use a larger worker type.

B : Increase the number of workers in the AWS Glue ETL jobs.

C : Use the AWS Glue DynamicFrame grouping option.

D : Enable AWS Glue auto scaling.

Answer: D

Question 5

An analyst is using Amazon Athena and needs to create a new table in the Parquet format with SNAPPY compression from an existing table named old_table. The task requires the correct syntax for a Create Table As Select (CTAS) statement in Athena. Which of the following SQL statements correctly achieves this objective?

A : CREATE NEW TABLE new_tableFROM old_tableWITH FORMAT AS 'Parquet' AND COMPRESSION AS 'SNAPPY';

B : CREATE TABLE new_tableSET FORMAT = 'Parquet', COMPRESSION = 'SNAPPY'AS SELECT *FROM old_table;

C : CREATE TABLE new_table ASSELECT *FROM old_tableUSING ('format' = 'Parquet', 'compression' = 'SNAPPY');

D : CREATE TABLE new_tableWITH ( format = 'Parquet', write_compression = 'SNAPPY')AS SELECT *FROM old_table;

Answer: D