Free Amazon AWS-DEA-C01 Exam Questions

Question 1

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 2

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 3

A Data Engineering team is planning to migrate a complex legacy Microsoft SQL Server database to Amazon Aurora MySQL. The existing SQL Server database contains several stored procedures, triggers, and complex joins. The team is concerned about potential compatibility issues and the effort needed to rewrite substantial amounts of SQL code manually. They aim to use AWS services to facilitate an efficient and accurate schema conversion, ensuring minimal downtime and data consistency during the migration process.

What sequence of steps should the team follow using AWS services to achieve this migration?

A : Use AWS Database Migration Service (AWS DMS) for an initial assessment of the schema conversion, apply the AWS Schema Conversion Tool (AWS SCT) for the actual schema conversion, then use AWS DMS again to migrate the data to Amazon Aurora MySQL.

B : First, apply the AWS Schema Conversion Tool (AWS SCT) to assess and convert the SQL Server schema and SQL code to a format compatible with Aurora MySQL. Following this, use AWS DMS to migrate the data from SQL Server to Aurora MySQL.

C : Directly utilize AWS DMS to migrate the SQL Server database to Amazon Aurora MySQL, relying on the DMS’s built-in schema conversion feature to handle any compatibility issues during the migration.

D : Start with the AWS Schema Conversion Tool (AWS SCT) for converting the SQL Server schema and code to Aurora MySQL, utilize Amazon RDS to temporarily host the converted schema, and then use AWS DMS for the final migration to Aurora MySQL.

Answer: B

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

As a Cloud Data Engineering Consultant, you are assisting a client in setting up a data warehouse using Amazon Redshift. The client's data warehouse stores sensitive financial information and is used by various departments within the organization, each requiring different levels of access. The client emphasizes the need for secure data sharing practices that would allow specific teams to access only the data relevant to their work. They seek your advice on implementing a solution within Amazon Redshift to grant permissions effectively and securely for sharing data across different teams.

To meet the client's need for secure, role-specific data access in their Amazon Redshift data warehouse, which of the following approaches would you recommend?

A : Utilize Redshift Spectrum to create external schemas and tables, granting access to these external tables to specific IAM roles assigned to different teams.

B : Configure Amazon Redshift Security Groups to control which teams can access the data warehouse, assigning specific IP address ranges for each team.

C : Leverage Amazon Redshift's Row-Level Security (RLS) feature to control access to rows of data based on user attributes such as team or role, enabling fine-grained access control within shared tables.

D : Implement Redshift Data Sharing to share live data across different Redshift clusters, using Redshift's ALTER DATASHARE command to add and manage consumer clusters' access to the data.

Answer: C