Free Amazon AWS-DEA-C01 Exam Questions

Question 1

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

Question 2

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 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 data engineer needs to reformat newly acquired .csv data in an S3 bucket for more efficient analytics. The data includes timestamps, and older data is removed daily. What is the most cost-effective AWS solution to transform and optimize this data for query performance?

A : Set up a daily Lambda function to convert and partition the .csv data into Parquet format within S3.

B : Use AWS Glue to convert the .csv data to Apache Parquet and partition it by timestamp.

C : Configure an EMR Spark job to transform the .csv files into Parquet, partitioned by the creation timestamp.

D : Run an Athena CTAS query to convert the data to Parquet format with Snappy compression, partitioned by timestamp.

Answer: D

Question 5

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