Question 1

While reviewing the data security policies of their company, a Cloud Data Engineering Consultant identifies a need to enhance the protection of sensitive data stored in various AWS services such as Amazon S3, RDS, and DynamoDB.

Which method would be most effective in securing the data across these services to prevent unauthorized access?

Options :

A : Encrypt the data at rest using AWS KMS-managed keys and ensure that encryption in transit is enabled for all communications.

B : Rely solely on AWS IAM policies to govern access, granting necessary permissions to each service based on the role of the user or service accessing the data.

C : Implement network access control lists (NACLs) and security groups to restrict access to the resources based on IP addresses.

D : Place all data in a private VPC and disable internet access, requiring all access to occur through a VPN or Direct Connect.

Answer: A

Question 2

A data analytics team is working with time-sensitive financial data. Their primary dataset resides in an Amazon Redshift cluster, but crucial, frequently updated transactional data is stored in a PostgreSQL database on Amazon RDS. They need a solution to analyze combined data sets in real-time, ensuring that the most current transactional information is included in their analysis without the overhead of continually transferring data between the two systems.

What approach should they consider to efficiently perform these real-time, integrated queries?

Options :

A : Utilize Athena Federated Queries to perform real-time querying across both the Redshift data and the RDS PostgreSQL database.

B : Implement Redshift Spectrum for querying external data, creating external tables that reflect the RDS PostgreSQL data.

C : Regularly export transactional data from RDS PostgreSQL to S3 and use Redshift Spectrum for querying alongside Redshift data.

D : Use Redshift Federated Query to directly integrate and query data from the Redshift cluster and the RDS PostgreSQL database in real-time.

Answer: D

Question 3

A data engineer has a one-time task to read data from objects that are in Apache Parquet format in an Amazon S3 bucket. The data engineer needs to query only one column of the data. Which solution will meet these requirements with the LEAST operational overhead?

Options :

A :

Confiqure an AWS Lambda function to load data from the S3 bucket into a pandas dataframe- Write a SQL SELECT statement on the dataframe to query the required column.

B : Use S3 Select to write a SQL SELECT statement to retrieve the required column from the S3 objects.

C : Prepare an AWS Glue DataBrew project to consume the S3 objects and to query the required column.

D :

Run an AWS Glue crawler on the S3 objects. Use a SQL SELECT statement in Amazon Athena to query the required column.

Answer: B,D

Question 4

A financial services firm is experiencing rising costs associated with monthly data migrations from their on-premises Oracle database to AWS. They need a more economical method to transfer their extensive transaction records to an Amazon RDS for Oracle instance with minimal disruption to their ongoing operations.

What AWS service should the firm utilize to achieve a cost-efficient and low-downtime migration?

Options :

A : Configure an AWS Snowball job to handle the end-of-month data transfers.

B : Establish AWS Direct Connect for a dedicated network connection to AWS.

C : Use AWS DataSync to schedule and automate the data transfer process.

D : Implement AWS Database Migration Service (AWS DMS) to manage ongoing data migrations.

Answer: D

Question 5

A large e-commerce company is looking to improve the search and recommendation capabilities on its platform. The company's data engineering team has recently built a data lake on Amazon S3, consisting of user interaction logs, product catalog information, and transactional data. The data is ingested from various sources, including RDBMS exports, streamed clickstream data, and batch processed log files, resulting in diverse data formats such as JSON, CSV, and Parquet.

The team wants to leverage this data for advanced analytics and machine learning but is facing challenges in consistently cataloging and querying this data efficiently. Additionally, they need to manage frequently evolving schemas as new product attributes and user interaction types are introduced.

How should the team use AWS Glue to address these challenges?

Options :

A : Implement a combination of AWS Glue crawlers and AWS Glue DataBrew. Use the crawlers for schema discovery and DataBrew for data cleansing and preparation, ensuring consistent cataloging. Manage schema evolution through manual updates in the AWS Glue Data Catalog.

B : Deploy multiple AWS Glue crawlers with a focus on each data source. Utilize Glue Studio to design and run ETL jobs that harmonize the data formats into a unified model, and leverage the Glue Schema Registry for evolving schema management.

C : Set up individual AWS Glue crawlers for each data category and format. Enable schema versioning in the Glue Data Catalog to handle evolving schemas, and use Glue Elastic Views to combine and transform data for advanced analytics.

D : Use a single AWS Glue crawler to scan the entire data lake. Apply custom classifiers to handle the diverse data formats, and employ AWS Glue Schema Registry for managing schema changes over time.

Answer: B

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