Free Amazon AWS-DEA-C01 Exam Questions

Question 1

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 2

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 3

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 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

A data engineering team at an online retail company is optimizing the performance of their Amazon Redshift data warehouse. The warehouse contains a large sales table with millions of rows and a smaller products table. Queries often join these two tables, and the team wants to optimize the query performance, especially for these join operations.

Which Redshift distribution style should the team use for the sales and products tables to enhance query performance?

A : Configure the sales table to ALL distribution and the products table to EVEN distribution.

B : Configure the sales table to AUTO distribution and the products table to ALL distribution.

C : Configure the sales table to EVEN distribution and the products table to KEY distribution.

D : Configure KEY distribution for both the sales and products tables based on the join column.

Answer: B