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Amazon DEA-C01 Exam

AWS Certified Data Engineer – Associate Amazon (DEA-C01) Exam Questions

Certification Exams

Number Of Questions

65 Questions Answers with Explanation

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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: 65 Questions Answers with Explanation

Amazon DEA-C01 Exam Syllabus & Study Guide

At Certs4Success, we provide the most accurate and up-to-date preparation materials for the Amazon DEA-C01 Exam. Our content is professionally designed to help you master data engineering concepts on AWS and pass the Amazon DEA-C01 Exam with confidence.

If you are planning to clear the Amazon AWS Certified Data Engineer – Associate (DEA-C01) Exam, this detailed syllabus will guide you through all the essential domains you need to focus on.

All Exam Topics of Amazon DEA-C01 Exam

Topic 1: Data Ingestion & Collection

Data Sources: Understanding structured, semi-structured, and unstructured data.
Ingestion Methods: Batch and real-time data ingestion techniques for the Amazon DEA-C01 Exam.
AWS Services: Using services like Kinesis and Data Pipeline.

Topic 2: Data Storage Solutions

Amazon S3: Scalable object storage for data lakes.
Data Warehousing: Using Amazon Redshift for analytics.
Database Services: RDS, DynamoDB, and Aurora.

Topic 3: Data Processing & Transformation

ETL Pipelines: Extract, transform, and load processes.
AWS Glue: Serverless data integration service.
Big Data Processing: Using EMR and Spark frameworks.

Topic 4: Data Modeling & Design

Schema Design: Designing efficient and scalable data models.
Partitioning: Improving query performance.
Data Formats: Working with Parquet, ORC, and JSON.

Topic 5: Data Security & Governance

Encryption: Securing data at rest and in transit.
Access Control: Managing permissions with IAM roles.
Compliance: Meeting regulatory requirements.

Topic 6: Data Analysis & Visualization

Querying Data: Using Amazon Athena for analysis.
Visualization Tools: Amazon QuickSight dashboards.
Insights Generation: Turning raw data into actionable insights.

Topic 7: Monitoring & Troubleshooting

Amazon CloudWatch: Monitoring pipelines and resources.
Logging: Tracking system performance and errors.
Troubleshooting: Identifying and resolving data workflow issues.

Topic 8: Streaming Data Solutions

Amazon Kinesis: Real-time data streaming.
Stream Processing: Handling continuous data flows.
Use Cases: Real-time analytics and event processing.

Topic 9: Cost Optimization & Performance

Cost Management: Reducing data processing costs.
Performance Tuning: Optimizing queries and pipelines.
Resource Management: Efficient use of AWS services.

Topic 10: Best Practices & Architecture

Data Lake Architecture: Designing modern data platforms.
Scalability: Building highly scalable systems.
Best Practices: Following AWS-recommended strategies for the Amazon DEA-C01 Exam.

Why Trust Certs4Success for Amazon DEA-C01 Exam?

Updated Content: Our materials are regularly updated to match the latest Amazon DEA-C01 Exam objectives.

Expert Guidance: We provide practical insights and real-world examples to help you succeed in the Amazon DEA-C01 Exam.

High Success Rate: Our content is created by certified professionals to help you pass the Amazon DEA-C01 Exam on your first attempt.

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: 65 Questions Answers with Explanation

Amazon DEA-C01 Exam Syllabus & Study Guide

At Certs4Success, we provide the most accurate and up-to-date preparation materials for the Amazon DEA-C01 Exam. Our content is professionally designed to help you master data engineering concepts on AWS and pass the Amazon DEA-C01 Exam with confidence.

If you are planning to clear the Amazon AWS Certified Data Engineer – Associate (DEA-C01) Exam, this detailed syllabus will guide you through all the essential domains you need to focus on.

All Exam Topics of Amazon DEA-C01 Exam

Topic 1: Data Ingestion & Collection

Data Sources: Understanding structured, semi-structured, and unstructured data.
Ingestion Methods: Batch and real-time data ingestion techniques for the Amazon DEA-C01 Exam.
AWS Services: Using services like Kinesis and Data Pipeline.

Topic 2: Data Storage Solutions

Amazon S3: Scalable object storage for data lakes.
Data Warehousing: Using Amazon Redshift for analytics.
Database Services: RDS, DynamoDB, and Aurora.

Topic 3: Data Processing & Transformation

ETL Pipelines: Extract, transform, and load processes.
AWS Glue: Serverless data integration service.
Big Data Processing: Using EMR and Spark frameworks.

Topic 4: Data Modeling & Design

Schema Design: Designing efficient and scalable data models.
Partitioning: Improving query performance.
Data Formats: Working with Parquet, ORC, and JSON.

Topic 5: Data Security & Governance

Encryption: Securing data at rest and in transit.
Access Control: Managing permissions with IAM roles.
Compliance: Meeting regulatory requirements.

Topic 6: Data Analysis & Visualization

Querying Data: Using Amazon Athena for analysis.
Visualization Tools: Amazon QuickSight dashboards.
Insights Generation: Turning raw data into actionable insights.

Topic 7: Monitoring & Troubleshooting

Amazon CloudWatch: Monitoring pipelines and resources.
Logging: Tracking system performance and errors.
Troubleshooting: Identifying and resolving data workflow issues.

Topic 8: Streaming Data Solutions

Amazon Kinesis: Real-time data streaming.
Stream Processing: Handling continuous data flows.
Use Cases: Real-time analytics and event processing.

Topic 9: Cost Optimization & Performance

Cost Management: Reducing data processing costs.
Performance Tuning: Optimizing queries and pipelines.
Resource Management: Efficient use of AWS services.

Topic 10: Best Practices & Architecture

Data Lake Architecture: Designing modern data platforms.
Scalability: Building highly scalable systems.
Best Practices: Following AWS-recommended strategies for the Amazon DEA-C01 Exam.

Why Trust Certs4Success for Amazon DEA-C01 Exam?

Updated Content: Our materials are regularly updated to match the latest Amazon DEA-C01 Exam objectives.

Expert Guidance: We provide practical insights and real-world examples to help you succeed in the Amazon DEA-C01 Exam.

High Success Rate: Our content is created by certified professionals to help you pass the Amazon DEA-C01 Exam on your first attempt.

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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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