Free Amazon AWS-DEA-C01 Exam Questions

Question 1

A company uses Amazon S3 as a data lake. The company sets up a data warehouse by using a multi-nodeAmazon Redshift cluster. The company organizes the data files in the data lake based on the data source ofeach data file.The company loads all the data files into one table in the Redshift cluster by using a separate COPY commandfor each data file location. This approach takes a long time to load all the data files into the table. Thecompany must increase the speed of the data ingestion. The company does not want to increase the cost of theprocess.Which solution will meet these requirements?

A : Use a provisioned Amazon EMR cluster to copy all the data files into one folder. Use a COPY command to load the data into Amazon Redshift.

B : Load all the data files in parallel into Amazon Aurora. Run an AWS Glue job to load the data into Amazon Redshift.

C : Use an AWS Glue job to copy all the data files into one folder. Use a COPY command to load the data into Amazon Redshift.

D : Create a manifest file that contains the data file locations. Use a COPY command to load the data into Amazon Redshift.

Answer: D

Question 2

A company wants to migrate a data warehouse from Teradata to Amazon Redshift. Which solution will meet this requirement with the LEAST operational effort?

A : Use AWS Database Migration Service (AWS DMS) Schema Conversion to migrate the schema. Use AWS DMS to migrate the data.

B : Use the AWS Schema Conversion Tool (AWS SCT) to migrate the schema. Use AWS Database Migration Service (AWS DMS) to migrate the data.

C : Use AWS Database Migration Service (AWS DMS) to migrate the data. Use automatic schema conversion.

D : Manually export the schema definition from Teradata. Apply the schema to the Amazon Redshift database. Use AWS Database Migration Service (AWS DMS) to migrate the data.

Answer: B

Question 3

A company is deploying a stateful application on Amazon EC2 that requires temporary storage for processing large datasets with maximum I/O. The application must also maintain a smaller subset of essential state data that needs to persist beyond the life of the instance. The company needs a storage solution that ensures the essential data is not lost if the EC2 instance is stopped or terminated.

Which combination of storage solutions should the company choose?

A : Attach Amazon EBS volumes for essential state data and use Amazon DynamoDB for temporary data.

B : Attach additional Amazon EBS volumes for temporary data processing and rely on the root device volume for essential state data.

C : Attach Amazon EC2 instance store volumes for temporary data processing and Amazon Elastic Block Store (EBS) for essential state data.

D : Implement a combination of Amazon EC2 instance store volumes for rapid I/O and EBS snapshots for daily backups of essential data.

Answer: C

Question 4

A Data Engineering Team is working on optimizing a complex query that involves numerous joins and aggregations on a large dataset. The dataset is stored in a graph data structure for which the relationships between entities are as crucial as the entities themselves.

The team is deciding on the most appropriate data structure or AWS service to use for this task to ensure both performance efficiency and query optimization. Which of the following should they consider?

A : Implement the data in an Amazon Neptune graph database, taking advantage of its support for highly connected data and complex traversal queries.

B : Store the data in an Amazon DynamoDB table, using its fast and flexible NoSQL database service to manage the relationships and queries efficiently.

C : Utilize Amazon Redshift, leveraging its columnar storage and massively parallel processing (MPP) architecture to handle complex queries and aggregations efficiently.

D : Opt for an Amazon Elastic MapReduce (EMR) cluster with Apache Spark, using Spark's GraphX library for graph processing and query optimizations on large datasets.

Answer: A

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