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Amazon DEA-C01 Exam Questions

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Description

Exam Name: AWS Certified Data Engineer – Associate
Exam Code: Amazon-DEA-C01
Related Certification(s): Amazon AWS Certified Data Engineer Associate Certification
Certification Provider: Amazon
Number of Amazon-DEA-C01 practice questions in our database: 152 (updated: Jan. 15, 2025)
Expected Amazon-DEA-C01 Exam Topics, as suggested by Amazon :

  • Module 1: Data Ingestion and Transformation: This section assesses data engineers on their ability to design scalable data ingestion pipelines. It focuses on collecting and transforming data from various sources for analysis. Candidates should be skilled in using AWS data services to create secure, optimized ingestion processes that support data analysis.
  • Module 2: Data Store Management: This domain evaluates database administrators and data engineers who manage AWS data storage. It covers creating and optimizing relational databases, NoSQL databases, and data lakes. The focus is on performance, scalability, and data integrity, ensuring efficient and reliable storage solutions.
  • Module 3: Data Operations and Support: Targeted at database administrators and engineers, this section covers maintaining and monitoring AWS data workflows. It emphasizes automation, monitoring, troubleshooting, and pipeline optimization, ensuring smooth operations and resolving system issues effectively.
  • Module 4: Data Security and Governance: This section database cloud security engineers on securing AWS data and ensuring policy compliance. It focuses on access control, encryption, privacy, and auditing, requiring candidates to design governance frameworks that meet regulatory standards.

Description

Exam Name: AWS Certified Data Engineer – Associate
Exam Code: Amazon-DEA-C01
Related Certification(s): Amazon AWS Certified Data Engineer Associate Certification
Certification Provider: Amazon
Number of Amazon-DEA-C01 practice questions in our database: 152 (updated: Jan. 15, 2025)
Expected Amazon-DEA-C01 Exam Topics, as suggested by Amazon :

  • Module 1: Data Ingestion and Transformation: This section assesses data engineers on their ability to design scalable data ingestion pipelines. It focuses on collecting and transforming data from various sources for analysis. Candidates should be skilled in using AWS data services to create secure, optimized ingestion processes that support data analysis.
  • Module 2: Data Store Management: This domain evaluates database administrators and data engineers who manage AWS data storage. It covers creating and optimizing relational databases, NoSQL databases, and data lakes. The focus is on performance, scalability, and data integrity, ensuring efficient and reliable storage solutions.
  • Module 3: Data Operations and Support: Targeted at database administrators and engineers, this section covers maintaining and monitoring AWS data workflows. It emphasizes automation, monitoring, troubleshooting, and pipeline optimization, ensuring smooth operations and resolving system issues effectively.
  • Module 4: Data Security and Governance: This section database cloud security engineers on securing AWS data and ensuring policy compliance. It focuses on access control, encryption, privacy, and auditing, requiring candidates to design governance frameworks that meet regulatory standards.

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Q1. A company hosts its applications on Amazon EC2 instances. The company must use SSL/TLS connections that encrypt data in transit to communicate securely with AWS infrastructure that is managed by a customer. A data engineer needs to implement a solution to simplify the generation, distribution, and rotation of digital certificates. The solution must automatically renew and deploy SSL/TLS certificates. Which solution will meet these requirements with the LEAST operational overhead?

A.Store self-managed certificates on the EC2 instances.

B. Use AWS Certificate Manager (ACM).

C. Implement custom automation scripts in AWS Secrets Manager.

D. Use Amazon Elastic Container Service (Amazon ECS) Service Connect.

Correct Answer: B

Q2. A company saves customer data to an Amazon S3 bucket. The company uses server-side encryption with AWS KMS keys (SSE-KMS) to encrypt the bucket. The dataset includes personally identifiable information (PII) such as social security numbers and account details. Data that is tagged as PII must be masked before the company uses customer data for analysis. Some users must have secure access to the PII data during the preprocessing phase. The company needs a low-maintenance solution to mask and secure the PII data throughout the entire engineering pipeline. Which combination of solutions will meet these requirements? (Select TWO.)

A.Use AWS Glue DataBrew to perform extract, transform, and load (ETL) tasks that mask the PII data before analysis.

B. Use Amazon GuardDuty to monitor access patterns for the PII data that is used in the engineering pipeline.

C. Configure an Amazon Made discovery job for the S3 bucket.

D. Use AWS Identity and Access Management (IAM) to manage permissions and to control access to the PII data.

E. Write custom scripts in an application to mask the PII data and to control access.

Correct Answer: A, D

Q3. A company uses AWS Glue Data Catalog to index data that is uploaded to an Amazon S3 bucket every day. The company uses a daily batch processes in an extract, transform, and load (ETL) pipeline to upload data from external sources into the S3 bucket. The company runs a daily report on the S3 dat a. Some days, the company runs the report before all the daily data has been uploaded to the S3 bucket. A data engineer must be able to send a message that identifies any incomplete data to an existing Amazon Simple Notification Service (Amazon SNS) topic. Which solution will meet this requirement with the LEAST operational overhead?

A.Create data quality checks for the source datasets that the daily reports use. Create a new AWS managed Apache Airflow cluster. Run the data quality checks by using Airflow tasks that run data quality queries on the columns data type and the presence of null values. Configure Airflow Directed Acyclic Graphs (DAGs) to send an email notification that informs the data engineer about the incomplete datasets to the SNS topic.

B. Create data quality checks on the source datasets that the daily reports use. Create a new Amazon EMR cluster. Use Apache Spark SQL to create Apache Spark jobs in the EMR cluster that run data quality queries on the columns data type and the presence of null values. Orchestrate the ETL pipeline by using an AWS Step Functions workflow. Configure the workflow to send an email notification that informs the data engineer about the incomplete datasets to the SNS topic.

C. Create data quality checks on the source datasets that the daily reports use. Create data quality actions by using AWS Glue workflows to confirm the completeness and consistency of the datasets. Configure the data quality actions to create an event in Amazon EventBridge if a dataset is incomplete. Configure EventBridge to send the event that informs the data engineer about the incomplete datasets to the Amazon SNS topic.

D. Create AWS Lambda functions that run data quality queries on the columns data type and the presence of null values. Orchestrate the ETL pipeline by using an AWS Step Functions workflow that runs the Lambda functions. Configure the Step Functions workflow to send an email notification that informs the data engineer about the incomplete datasets to the SNS topic.

Correct Answer: C

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