Data-Engineer-Associate Amazon Web Services AWS Certified Data Engineer

AWS Certified Data Engineer - Associate (DEA-C01)

Last Update 18 hours ago Total Questions : 289

The AWS Certified Data Engineer - Associate (DEA-C01) content is now fully updated, with all current exam questions added 18 hours ago. Deciding to include Data-Engineer-Associate practice exam questions in your study plan goes far beyond basic test preparation.

You'll find that our Data-Engineer-Associate exam questions frequently feature detailed scenarios and practical problem-solving exercises that directly mirror industry challenges. Engaging with these Data-Engineer-Associate sample sets allows you to effectively manage your time and pace yourself, giving you the ability to finish any AWS Certified Data Engineer - Associate (DEA-C01) practice test comfortably within the allotted time.

Question # 61

A data engineer needs to onboard a new data producer into AWS. The data producer needs to migrate data products to AWS.

The data producer maintains many data pipelines that support a business application. Each pipeline must have service accounts and their corresponding credentials. The data engineer must establish a secure connection from the data producer ' s on-premises data center to AWS. The data engineer must not use the public internet to transfer data from an on-premises data center to AWS.

Which solution will meet these requirements?

Instruct the new data producer to create Amazon Machine Images (AMIs) on Amazon Elastic Container Service (Amazon ECS) to store the code base of the application. Create security groups in a public subnet that allow connections only to the on-premises data center.

Create an AWS Direct Connect connection to the on-premises data center. Store the service account credentials in AWS Secrets manager.

Create a security group in a public subnet. Configure the security group to allow only connections from the CIDR blocks that correspond to the data producer. Create Amazon S3 buckets than contain presigned URLS that have one-day expiration dates.

Create an AWS Direct Connect connection to the on-premises data center. Store the application keys in AWS Secrets Manager. Create Amazon S3 buckets that contain resigned URLS that have one-day expiration dates.

Question # 62

A company wants to use Apache Spark jobs that run on an Amazon EMR cluster to process streaming data. The Spark jobs will transform and store the data in an Amazon S3 bucket. The company will use Amazon Athena to perform analysis.

The company needs to optimize the data format for analytical queries.

Which solutions will meet these requirements with the SHORTEST query times? (Select TWO.)

Use Avro format. Use AWS Glue Data Catalog to track schema changes.

Use ORC format. Use AWS Glue Data Catalog to track schema changes.

Use Apache Parquet format. Use an external Amazon DynamoDB table to track schema changes.

Use Apache Parquet format. Use AWS Glue Data Catalog to track schema changes.

Use ORC format. Store schema definitions in separate files in Amazon S3.

Question # 63

A data engineer is building a data pipeline on AWS by using AWS Glue extract, transform, and load (ETL) jobs. The data engineer needs to process data from Amazon RDS and MongoDB, perform transformations, and load the transformed data into Amazon Redshift for analytics. The data updates must occur every hour.

Which combination of tasks will meet these requirements with the LEAST operational overhead? (Choose two.)

Configure AWS Glue triggers to run the ETL jobs even/ hour.

Use AWS Glue DataBrewto clean and prepare the data for analytics.

Use AWS Lambda functions to schedule and run the ETL jobs even/ hour.

Use AWS Glue connections to establish connectivity between the data sources and Amazon Redshift.

Use the Redshift Data API to load transformed data into Amazon Redshift.

Question # 64

A data engineer needs to deploy a complex pipeline. The stages of the pipeline must run scripts, but only fully managed and serverless services can be used.

Deploy AWS Glue jobs and workflows. Use AWS Glue to run the jobs and workflows on a schedule.

Use Amazon MWAA to build and schedule the pipeline.

Deploy the script to EC2. Use EventBridge to schedule it.

Use AWS Glue DataBrew and EventBridge to run on a schedule.

Question # 65

A data engineer is implementing model governance for machine learning (ML) workflows on AWS. The data engineer needs a solution that can track the complete lifecycle of the ML models, including data preparation, model training, and deployment stages. The solution must ensure reproducibility and audit compliance.

Use Amazon SageMaker Debugger to capture metrics. Create associations between datasets and training jobs by monitoring training jobs.

Use Amazon SageMaker ML Lineage Tracking to create associations between artifacts, training jobs, and datasets by recording metadata.

Use Amazon SageMaker Model Monitor to create associations between artifacts and training jobs by tracking model performance.

Use Amazon SageMaker Experiments to create associations between datasets and artifacts by tracking hyperparameters and metrics.

Question # 66

A data engineer is using an AWS Glue ETL job to remove outdated customer records from a table that contains customer account information. The data engineer is using the following SQL command:

MERGE INTO accounts t USING monthly_accounts_update s

ON t.customer = s.customer

WHEN MATCHED THEN DELETE

What will happen when the data engineer runs the SQL command?

All customer records that exist in both the customer accounts table and the monthly_accounts_update table will be deleted from the accounts table.

Only customer records that are present in both tables will be retained in the customer accounts table.

The monthly_accounts_update table will be deleted.

No records will be deleted because the command syntax is not valid in AWS Glue.

Question # 67

A company extracts approximately 1 TB of data every day from data sources such as SAP HANA, Microsoft SQL Server, MongoDB, Apache Kafka, and Amazon DynamoDB. Some of the data sources have undefined data schemas or data schemas that change.

A data engineer must implement a solution that can detect the schema for these data sources. The solution must extract, transform, and load the data to an Amazon S3 bucket. The company has a service level agreement (SLA) to load the data into the S3 bucket within 15 minutes of data creation.

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

Use Amazon EMR to detect the schema and to extract, transform, and load the data into the S3 bucket. Create a pipeline in Apache Spark.

Use AWS Glue to detect the schema and to extract, transform, and load the data into the S3 bucket. Create a pipeline in Apache Spark.

Create a PvSpark proqram in AWS Lambda to extract, transform, and load the data into the S3 bucket.

Create a stored procedure in Amazon Redshift to detect the schema and to extract, transform, and load the data into a Redshift Spectrum table. Access the table from Amazon S3.

Question # 68

A company stores sensitive data in an Amazon Redshift table. The company needs to give specific users the ability to access the sensitive data. The company must not create duplication in the data. Customer support users must be able to see the last four characters of the sensitive data. Audit users must be able to see the full value of the sensitive data. No other users can have the ability to access the sensitive information.

Which solution will meet these requirements?

Create a dynamic data masking policy to allow access based on each user role. Create IAM roles that have specific access permissions. Attach the masking policy to the column that contains sensitive data.

Enable metadata security on the Redshift cluster. Create IAM users and IAM roles for the customer support users and the audit users. Grant the IAM users and IAM roles permissions to view the metadata in the Redshift cluster.

Create a row-level security policy to allow access based on each user role. Create IAM roles that have specific access permissions. Attach the security policy to the table.

Create an AWS Glue job to redact the sensitive data and to load the data into a new Redshift table.

Question # 69

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.)

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

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

Configure an Amazon Made discovery job for the S3 bucket.

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

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

Question # 70

A data engineer needs to debug an AWS Glue job that reads from Amazon S3 and writes to Amazon Redshift. The data engineer enabled the bookmark feature for the AWS Glue job. The data engineer has set the maximum concurrency for the AWS Glue job to 1.

The AWS Glue job is successfully writing the output to Amazon Redshift. However, the Amazon S3 files that were loaded during previous runs of the AWS Glue job are being reprocessed by subsequent runs.

What is the likely reason the AWS Glue job is reprocessing the files?

The AWS Glue job does not have the s3:GetObjectAcl permission that is required for bookmarks to work correctly.

The maximum concurrency for the AWS Glue job is set to 1.

The data engineer incorrectly specified an older version of AWS Glue for the Glue job.

The AWS Glue job does not have a required commit statement.