Free Amazon AWS-DEA-C01 Exam Questions

Question 1

A company needs to load customer data that comes from a third party into an Amazon Redshift datawarehouse. The company stores order data and product data in the same data warehouse. The company wantsto use the combined dataset to identify potential new customers.A data engineer notices that one of the fields in the source data includes values that are in JSON format.How should the data engineer load the JSON data into the data warehouse with the LEAST effort?

A : Use the SUPER data type to store the data in the Amazon Redshift table.

B : Use AWS Glue to flatten the JSON data and ingest it into the Amazon Redshift table.

C : Use Amazon S3 to store the JSON data. Use Amazon Athena to query the data.

D : Use an AWS Lambda function to flatten the JSON data. Store the data in Amazon S3.

Answer: A

Question 2

A company receives test results from testing facilities that are located around the world. The company storesthe test results in millions of 1 KB JSON files in an Amazon S3 bucket. A data engineer needs to process thefiles, convert them into Apache Parquet format, and load them into Amazon Redshift tables. The dataengineer uses AWS Glue to process the files, AWS Step Functions to orchestrate the processes, and AmazonEventBridge to schedule jobs.The company recently added more testing facilities. The time required to process files is increasing. The dataengineer must reduce the data processing time.Which solution will MOST reduce the data processing time?

A : Use AWS Lambda to group the raw input files into larger files. Write the larger files back to Amazon S3. Use AWS Glue to process the files. Load the files into the Amazon Redshift tables.

B : Use the AWS Glue dynamic frame file-grouping option to ingest the raw input files. Process the files. Load the files into the Amazon Redshift tables.

C : Use the Amazon Redshift COPY command to move the raw input files from Amazon S3 directly into the Amazon Redshift tables. Process the files in Amazon Redshift.

D : Use Amazon EMR instead of AWS Glue to group the raw input files. Process the files in Amazon EMR. Load the files into the Amazon Redshift tables.

Answer: B

Question 3

A retail company receives a daily .xls file with customer data, which is uploaded to Amazon S3. The file size is about 2 GB. A data engineer is tasked with combining the customer first name and last name fields and then identifying the total number of unique customer entries in the file.

Which AWS service or feature should the data engineer use to determine the count of distinct customers with minimal operational effort?

A : Employ Amazon S3 Select to perform the concatenation and distinct count directly on the S3 file.

B : Utilize AWS Lambda with Amazon S3 trigger to process the file and calculate the number of distinct customer names.

C : Use Amazon Athena to run a SQL query that concatenates the names and counts distinct entries after converting the file to CSV format.

D : Set up an AWS Glue DataBrew job to concatenate the fields and use the COUNT_DISTINCT function to find the number of unique customers.

Answer: D

Question 4

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 5

Your organization has deployed a series of IoT devices across its facilities to monitor environmental conditions. These devices send telemetry data every few seconds. As part of the data pipeline, you have been tasked with architecting a solution that ingests this streaming data, provides the ability to perform real-time analytics, and subsequently batches the data for storage in Amazon Redshift for further analysis. The solution should be scalable, manage large bursts of data effectively, and ensure that analytics can be performed promptly.

As a Cloud Data Engineering Consultant, which combination of AWS services would you employ to meet these requirements? (Select THREE)

A : Utilize Amazon DynamoDB to store and quickly retrieve data, which provides low latency access and flexibility to handle varying workloads.

B : Utilize Amazon Kinesis Data Firehose to batch and load the processed streaming data directly into Amazon Redshift.

C : Implement AWS Lambda functions to transform the data on the fly as it arrives on Kinesis Data Streams before sending it to Kinesis Data Firehose.

D : Deploy an Amazon Kinesis Data Streams application to collect streaming telematics data, enabling real-time analytics and quick response to data.

E : Configure Amazon QuickSight to connect to the Kinesis Data Stream for real-time analytics dashboards and visualizations.

F : Set up a scheduled Amazon Elastic MapReduce (EMR) job to process data, offering robust data transformation capabilities for complex analytics tasks.

Answer: B,C,D