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

As a Data Engineer, you are tasked with setting up a batch data ingestion pipeline for a substantial financial dataset. This data is currently housed in an Amazon RDS database, stemming from various financial services and trading platforms. The dataset is complex and heterogeneous, predominantly in JSON format, and is updated nightly, typically ranging between 100-200 GB.

Your primary objective is to efficiently transfer this data from the RDS database to a data lake in AWS, ensuring reliable storage and preparing the dataset for analytical processing with minimal yet capable transformation steps due to its complexity.

Which combination of AWS services would be most appropriate?

A : Amazon AppFlow to facilitate the data transfer from Amazon RDS to Amazon S3. Afterward, utilize AWS Glue ETL for the data transformation processes.

B : Use AWS Database Migration Service (AWS DMS) to continuously replicate data from the Amazon RDS database to Amazon S3. Then, employ AWS Lambda for performing the necessary data transformations.

C : Configure AWS DataSync to transfer the JSON data nightly from the RDS database to Amazon S3. Subsequently, use AWS Glue ETL service to perform the necessary data transformations.

D : Use AWS Database Migration Service (AWS DMS) to continuously replicate data to Amazon S3. Configure an AWS Glue Crawler to update the schema in the Glue Data Catalog, and use Amazon Glue ETL for transformation jobs.

Answer: D

Question 3

The Cloud Data Engineering team at an e-commerce company is tasked with analyzing historical sales data stored in CSV files in Amazon S3. They require a solution that allows them to directly query this data for their daily sales report, which will include product names, quantities sold, and total sales amounts. The solution should enable them to perform this task without the overhead of cluster management or data loading processes.

Which two steps are essential to set up this process (Select TWO)

A : Create an Amazon Redshift Spectrum external schema for the CSV files and run the query from an Amazon Redshift cluster.

B : Implement an AWS Lambda function to convert the CSV files into JSON format, as Athena cannot query CSV files directly.

C : Use AWS Data Pipeline to transfer the CSV files into an Amazon RDS instance and then query the data with Athena.

D : Write an Amazon Athena SQL query to select the necessary fields, aggregate the quantity and sales amount, and group the results by product name.

E : Use the AWS Glue Data Catalog to catalog the CSV files and define a schema that maps to the fields in the sales data.

Answer: D,E

Question 4

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 5

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