Free Amazon AWS-DEA-C01 Exam Questions

Question 1

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 2

An analytics team frequently runs complex Amazon Athena queries on a dataset stored in an Amazon S3 bucket, with AWS Glue Data Catalog serving as the metadata repository. They've identified a slowdown in Athena query plans and attribute it to the excessive number of S3 partitions. The team needs to enhance query performance and reduce the time Athena takes in query planning.

Which two measures should the team take to meet these performance goals? (Select TWO.)

A : Implement AWS Glue Elastic Views to automate partition management.

B : Employ Athena's partition projection for dynamic partitioning based on common query patterns.

C : Merge small S3 objects into larger ones using AWS Glue's data transformation jobs.

D : Use Athena DML statements to reorganize data into fewer, more meaningful partitions.

E : Transform the data that is in the S3 bucket to Apache Parquet format.

Answer: B,E

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

A company has three subsidiaries. Each subsidiary uses a different data warehousing solution. The firstsubsidiary hosts its data warehouse in Amazon Redshift. The second subsidiary uses Teradata Vantage onAWS. The third subsidiary uses Google BigQuery.The company wants to aggregate all the data into a central Amazon S3 data lake. The company wants to useApache Iceberg as the table format.A data engineer needs to build a new pipeline to connect to all the data sources, run transformations by usingeach source engine, join the data, and write the data to Iceberg.Which solution will meet these requirements with the LEAST operational effort?

A : Use native Amazon Redshift, Teradata, and BigQuery connectors to build the pipeline in AWS Glue. Use native AWS Glue transforms to join the data. Run a Merge operation on the data lake Iceberg table.

B : Use the Amazon Athena federated query connectors for Amazon Redshift, Teradata, and BigQuery to build the pipeline in Athena. Write a SQL query to read from all the data sources, join the data, and run a Merge operation on the data lake Iceberg table

C : Use the native Amazon Redshift connector, the Java Database Connectivity (JDBC) connector for Teradata, and the open source Apache Spark BigQuery connector to build the pipeline in Amazon EMR. Write code in PySpark to join the data. Run a Merge operation on the data lake Iceberg table.

D : Use the native Amazon Redshift, Teradata, and BigQuery connectors in Amazon Appflow to write data to Amazon S3 and AWS Glue Data Catalog. Use Amazon Athena to join the data. Run a Merge operation on the data lake Iceberg table.

Answer: B

Question 5

A data engineer has two datasets that contain sales information for multiple cities and states. One dataset is named reference, and the other dataset is named primary. The data engineer needs a solution to determine whether a specific set of values in the city and state columns of the primary dataset exactly match the same specific values in the reference dataset. The data engineer wants to use Data Quality Definition Language (DQDL) rules in an AWS Glue Data Quality job. Which rule will meet these requirements?

A : DatasetMatch "reference" "city->ref_city, state->ref_state" = 1.0

B : ReferentialIntegrity "city,state" "reference.{ref_city,ref_state}" = 1.0

C : DatasetMatch "reference" "city->ref_city, state->ref_state" = 100

D : ReferentialIntegrity "city,state" "reference.{ref_city,ref_state}" = 100

Answer: A