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Data-Engineer-Associate Exam Dumps : AWS Certified Data Engineer - Associate (DEA-C01)

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Amazon Web Services Data-Engineer-Associate Exam Dumps FAQs

Q. # 1: What is the Amazon Web Services Certified Data-Engineer-Associate Exam?

The Amazon Web Services Certified Data-Engineer-Associate Exam validates your skills and knowledge in core data-related AWS services, including data modeling, data lifecycle management, and ensuring data quality.

Q. # 2: Who should take the Amazon Web Services Data-Engineer-Associate Exam?

The Amazon Web Services Data-Engineer-Associate exam is ideal for data engineers with 2-3 years of experience and at least 1-2 years working with AWS services. It assesses your ability to design, develop, secure, and maintain data pipelines on AWS.

Q. # 3: What topics does the Amazon Web Services Data-Engineer-Associate Exam cover?

The Amazon Web Services Data-Engineer-Associate exam focuses on five core domains:

  • Data Ingestion & Transformation (25%)
  • Data Store Management (25%)
  • Data Operations & Support (25%)
  • Data Security and Governance (20%)

Q. # 4: How many questions are on the Amazon Web Services Certified Data-Engineer-Associate Exam?

The Amazon Web Services Certified Data-Engineer-Associate exam consists of 65 questions, which can be either multiple choice or multiple response.

Q. # 5: What is the exam duration for the Amazon Web Services Certified Data Engineer - Associate Exam?

The Amazon Web Services Data-Engineer-Associate exam duration is 130 minutes.

Q. # 6: What is the passing score for the AWS Certified Data-Engineer-Associate Exam?

The passing score for the Amazon Web Services Data-Engineer-Associate exam is 720.

Q. # 7: What is the difference between Amazon Web Services Data-Engineer-Associate and SOA-C02 Exams?

The AWS Certified Data-Engineer-Associate (DAS-C01) and the AWS Certified SysOps Administrator - Associate (SOA-C02) exams are designed for different roles and focus on different areas of AWS expertise:

  • AWS Certified Data-Engineer-Associate Exam (DAS-C01): The AWS Certified Data-Engineer-Associate (DAS-C01) Exam validates your ability to design data models, manage data life cycles, and ensure data quality using AWS services.

Q. # 8: How can CertsTopics help me prepare for the Amazon Web Services Data-Engineer-Associate Exam?

CertsTopics offers top-quality Data-Engineer-Associate Exam Dumps, Practice Tests, and PDF Questions and Answers specifically for the Amazon Web Services Data-Engineer-Associate Exam. Our Data-Engineer-Associate stduy materials are crafted by experts and regularly updated to reflect current exam content, ensuring a high success rate.

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AWS Certified Data Engineer - Associate (DEA-C01) Questions and Answers

Question 1

A company has three subsidiaries. Each subsidiary uses a different data warehousing solution. The first subsidiary hosts its data warehouse in Amazon Redshift. The second subsidiary uses Teradata Vantage on AWS. 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 use Apache Iceberg as the table format.

A data engineer needs to build a new pipeline to connect to all the data sources, run transformations by using each source engine, join the data, and write the data to Iceberg.

Which solution will meet these requirements with the LEAST operational effort?

Options:

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.

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Question 2

A retail company is expanding its operations globally. The company needs to use Amazon QuickSight to accurately calculate currency exchange rates for financial reports. The company has an existing dashboard that includes a visual that is based on an analysis of a dataset that contains global currency values and exchange rates.

A data engineer needs to ensure that exchange rates are calculated with a precision of four decimal places. The calculations must be precomputed. The data engineer must materialize results in QuickSight super-fast, parallel, in-memory calculation engine (SPICE).

Which solution will meet these requirements?

Options:

A.

Define and create the calculated field in the dataset.

B.

Define and create the calculated field in the analysis.

C.

Define and create the calculated field in the visual.

D.

Define and create the calculated field in the dashboard.

Question 3

A retail company stores data from a product lifecycle management (PLM) application in an on-premises MySQL database. The PLM application frequently updates the database when transactions occur.

The company wants to gather insights from the PLM application in near real time. The company wants to integrate the insights with other business datasets and to analyze the combined dataset by using an Amazon Redshift data warehouse.

The company has already established an AWS Direct Connect connection between the on-premises infrastructure and AWS.

Which solution will meet these requirements with the LEAST development effort?

Options:

A.

Run a scheduled AWS Glue extract, transform, and load (ETL) job to get the MySQL database updates by using a Java Database Connectivity (JDBC) connection. Set Amazon Redshift as the destination for the ETL job.

B.

Run a full load plus CDC task in AWS Database Migration Service (AWS DMS) to continuously replicate the MySQL database changes. Set Amazon Redshift as the destination for the task.

C.

Use the Amazon AppFlow SDK to build a custom connector for the MySQL database to continuously replicate the database changes. Set Amazon Redshift as the destination for the connector.

D.

Run scheduled AWS DataSync tasks to synchronize data from the MySQL database. Set Amazon Redshift as the destination for the tasks.