Pre-Summer Sale Limited Time 65% Discount Offer - Ends in 0d 00h 00m 00s - Coupon code: 65pass65

Data-Engineer-Associate AWS Certified Data Engineer - Associate (DEA-C01) is now Stable and With Pass Result | Test Your Knowledge for Free

Exams4sure Dumps

Data-Engineer-Associate Practice Questions

AWS Certified Data Engineer - Associate (DEA-C01)

Last Update 4 days ago
Total Questions : 289

Dive into our fully updated and stable Data-Engineer-Associate practice test platform, featuring all the latest AWS Certified Data Engineer exam questions added this week. Our preparation tool is more than just a Amazon Web Services study aid; it's a strategic advantage.

Our free AWS Certified Data Engineer practice questions crafted to reflect the domains and difficulty of the actual exam. The detailed rationales explain the 'why' behind each answer, reinforcing key concepts about Data-Engineer-Associate. Use this test to pinpoint which areas you need to focus your study on.

Data-Engineer-Associate PDF

Data-Engineer-Associate PDF (Printable)
$43.75
$124.99

Data-Engineer-Associate Testing Engine

Data-Engineer-Associate PDF (Printable)
$50.75
$144.99

Data-Engineer-Associate PDF + Testing Engine

Data-Engineer-Associate PDF (Printable)
$63.7
$181.99
Question # 81

A retail company is using an Amazon Redshift cluster to support real-time inventory management. The company has deployed an ML model on a real-time endpoint in Amazon SageMaker.

The company wants to make real-time inventory recommendations. The company also wants to make predictions about future inventory needs.

Which solutions will meet these requirements? (Select TWO.)

Options:

A.  

Use Amazon Redshift ML to generate inventory recommendations.

B.  

Use SQL to invoke a remote SageMaker endpoint for prediction.

C.  

Use Amazon Redshift ML to schedule regular data exports for offline model training.

D.  

Use SageMaker Autopilot to create inventory management dashboards in Amazon Redshift.

E.  

Use Amazon Redshift as a file storage system to archive old inventory management reports.

Discussion 0
Question # 82

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?

Options:

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.

Discussion 0
Question # 83

A company wants to migrate an application and an on-premises Apache Kafka server to AWS. The application processes incremental updates that an on-premises Oracle database sends to the Kafka server. The company wants to use the replatform migration strategy instead of the refactor strategy.

Which solution will meet these requirements with the LEAST management overhead?

Options:

A.  

Amazon Kinesis Data Streams

B.  

Amazon Managed Streaming for Apache Kafka (Amazon MSK) provisioned cluster

C.  

Amazon Data Firehose

D.  

Amazon Managed Streaming for Apache Kafka (Amazon MSK) Serverless

Discussion 0
Question # 84

A data engineer needs to create an Amazon Athena table based on a subset of data from an existing Athena table named cities_world. The cities_world table contains cities that are located around the world. The data engineer must create a new table named cities_us to contain only the cities from cities_world that are located in the US.

Which SQL statement should the data engineer use to meet this requirement?

Question # 84

Options:

A.  

Option A

B.  

Option B

C.  

Option C

D.  

Option D

Discussion 0
Question # 85

A retail company stores order information in an Amazon Aurora table named Orders. The company needs to create operational reports from the Orders table with minimal latency. The Orders table contains billions of rows, and over 100,000 transactions can occur each second.

A marketing team needs to join the Orders data with an Amazon Redshift table named Campaigns in the marketing team ' s data warehouse. The operational Aurora database must not be affected.

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

Options:

A.  

Use AW5 Database Migration Service (AWS DMS) Serverless to replicate the Orders table to Amazon Redshift. Create a materialized view in Amazon Redshift to join with the Campaigns table.

B.  

Use the Aurora zero-ETL integration with Amazon Redshift to replicate the Orders table. Create a materialized view in Amazon Redshift to join with the Campaigns table.

C.  

Use AWS Glue to replicate the Orders table to Amazon Redshift. Create a materialized view in Amazon Redshift to join with the Campaigns table.

D.  

Use federated queries to query the Orders table directly from Aurora. Create a materialized view in Amazon Redshift to join with the Campaigns table.

Discussion 0
Question # 86

A data engineer is using AWS Glue to build an extract, transform, and load (ETL) pipeline that processes streaming data from sensors. The pipeline sends the data to an Amazon S3 bucket in near real-time. The data engineer also needs to perform transformations and join the incoming data with metadata that is stored in an Amazon RDS for PostgreSQL database. The data engineer must write the results back to a second S3 bucket in Apache Parquet format.

Which solution will meet these requirements?

Options:

A.  

Use an AWS Glue streaming job and AWS Glue Studio to perform the transformations and to write the data in Parquet format.

B.  

Use AWS Glue jobs and AWS Glue Data Catalog to catalog the data from Amazon S3 and Amazon RDS. Configure the jobs to perform the transformations and joins and to write the output in Parquet format.

C.  

Use an AWS Glue interactive session to process the streaming data and to join the data with the RDS database.

D.  

Use an AWS Glue Python shell job to run a Python script that processes the data in batches. Keep track of processed files by using AWS Glue bookmarks.

Discussion 0
Get Data-Engineer-Associate dumps and pass your exam in 24 hours!

Free Exams Sample Questions