Free Amazon AWS-DEA-C01 Exam Questions

Question 1

A data engineer must orchestrate a data pipeline that consists of one AWS Lambda function and one AWS Glue job. The solution must integrate with AWS services. Which solution will meet these requirements with the LEAST management overhead?

A : Use an AWS Step Functions workflow that includes a state machine. Configure the state machine to run the Lambda function and then the AWS Glue job.

B : Use an Apache Airflow workflow that is deployed on an Amazon EC2 instance. Define a directed acyclic graph (DAG) in which the first task is to call the Lambda function and the second task is to call the AWS Glue job.

C : Use an AWS Glue workflow to run the Lambda function and then the AWS Glue job.

D : Use an Apache Airflow workflow that is deployed on Amazon Elastic Kubernetes Service (Amazon EKS). Define a directed acyclic graph (DAG) in which the first task is to call the Lambda function and the second task is to call the AWS Glue job.

Answer: A

Question 2

A company wants to migrate an application and an on-premises Apache Kafka server to AWS. Theapplication processes incremental updates that an on-premises Oracle database sends to the Kafka server. Thecompany wants to use the replatform migration strategy instead of the refactor strategy.Which solution will meet these requirements with the LEAST management overhead?

A : Amazon Kinesis Data Streams

B : Amazon Managed Streaming for Apache Kafka (Amazon MSK) provisioned cluster

C : Amazon Data Firehose

D : Amazon Managed Streaming for Apache Kafka (Amazon MSK) Serverless

Answer: D

Question 3

A Cloud Data Engineering Team is working on optimizing the query performance of an Amazon Redshift cluster, serving as the primary data warehouse for their organization. The warehouse contains a large-scale dataset, including extensive historical sales data.

The team has identified that the query performance issues are mainly due to inefficient data scanning and retrieval processes, especially in tables with complex query patterns. They need a solution that specifically optimizes the way data is stored and accessed in these tables to reduce execution times for complex queries.

Which of the following techniques should they implement to improve the query performance on these large tables?

A : Increase the number of nodes in the Redshift cluster to boost the overall processing power and disk I/O, enhancing the query performance on large datasets.

B : Enable automatic vacuum delete on the Redshift cluster to purge the deleted tuples, thus improving query performance on large tables.

C : Use Amazon Redshift Spectrum to offload some of the older, less frequently accessed data to Amazon S3, and query directly from S3 when needed.

D : Implement columnar storage and define SORTKEYs on frequently queried columns in these large tables to optimize data scanning.

Answer: D

Question 4

A research institution is looking to transition their on-premises data analytics platform, which includes Apache Hadoop clusters and a comprehensive data catalog managed in an Apache Hive metastore, to the AWS cloud. They are adopting Amazon EMR for their Hadoop workloads and require a serverless, cost-effective solution to migrate and manage their existing data catalog.

Which solution should the institution implement to migrate their Hive metastore to AWS while ensuring a serverless and cost-effective data catalog management?

A : Migrate the Hive metastore to Amazon RDS with AWS Database Migration Service (DMS), then connect the RDS instance to Amazon EMR clusters.

B : Use AWS Glue Data Catalog to import the existing Hive metastore, making it the centralized metadata repository, and integrate it with Amazon EMR.

C : Set up an Amazon EMR cluster with a configured Hive metastore, migrate the on-premises metastore to EMR, and utilize AWS Glue Data Catalog for catalog synchronization.

D : Implement Amazon Athena to reconstruct the data catalog from the Hive metastore data stored in Amazon S3, integrating it with EMR for analytics.

Answer: B

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