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Amazon Data-Engineer-Associate Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Ingestion and Transformation	34%	- Ingest data from various sources 1. Batch and streaming data ingestion 2. Use services like Kinesis, DMS, Glue, S3 - Transform and enrich data 1. Apply data processing logic 2. Orchestrate data pipelines 3. Use Spark, EMR, Step Functions - Implement data quality and validation
Topic 2: Data Operations and Support	22%	- Monitor and troubleshoot data pipelines 1. CloudWatch, X-Ray, logging and metrics - Backup, restore, and disaster recovery - Automate operational tasks - Ensure reliability and scalability
Topic 3: Data Security and Governance	18%	- Implement access control and authentication 1. IAM, Lake Formation, resource policies - Encrypt data at rest and in transit - Protect sensitive data - Enforce compliance and data governance 1. Data lineage, audit logging, regulatory requirements
Topic 4: Data Store Management	26%	- Manage data lifecycle and storage tiers - Optimize storage performance and cost - Design and implement data storage solutions 1. S3, Redshift, DynamoDB, RDS, Lake Formation 2. Data lakes, data warehouses, databases

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Amazon AWS Certified Data Engineer - Associate (DEA-C01) Sample Questions (Q42-Q47):

NEW QUESTION # 42
A company that operates globally must follow regulations that require data from an AWS Region to be accessible only within that Region.
A data engineer is creating a data pipeline that will create resources in the Region where the data engineer works. The data pipeline should have access to data only from the Region where the data engineer works. The pipeline uses Active Directory as an identity and authentication system. The pipeline uses a custom identity broker application to verify that employees are signed in to Active Directory and to obtain temporary credentials by using the AssumeRole API operation.
Which solution will meet the locality requirements with the LEAST administrative effort?

A. Create individual IAM policies that allow users to create resources in a specific Region. Assign the policies to each data engineer. Allow users to assume the individually assigned role when the users log in to AWS.
B. Create an IAM role that has permissions to create resources. Create a policy for each Region that ensures users can create resources only in that Region. Pass the policy as the session policy when employees obtain the temporary credentials.
C. Create an IAM role for data engineers in each Region separately. Instruct each data engineer to obtain temporary credentials by assuming the appropriate Region-specific IAM role.
D. Create an IAM group for each Region. Include the required IAM policies for each IAM group. Add users to each IAM group so that when users log in by obtaining the temporary credentials, the users will receive the appropriate access based on the IAM group.

Answer: B

Explanation:
Option A minimizes administration because it keeps one base IAM role for the pipeline and enforces regional locality at session time by attaching a session policy when the broker calls AssumeRole. This aligns with the security best practice of applying only what is necessary, because permissions should be limited to the exact scope required for the session. The material explicitly emphasizes the principle of least privilege, stating that permissions "should apply only to what is necessary," and the most appropriate approach is to grant only the minimum actions needed for the job.
With session policies, the organization avoids creating and maintaining separate per-Region roles (Option B), avoids managing users and group memberships for federated sessions (Option C), and avoids individually attaching policies per engineer (Option D). Instead, the broker can dynamically apply a Region-specific restriction for each login, ensuring the temporary credentials are valid only for the engineer's working Region. This design reduces ongoing operational burden while still enforcing strict locality controls through temporary, scoped credentials and least-privilege boundaries.

NEW QUESTION # 43
A company uses an Amazon QuickSight dashboard to monitor usage of one of the company's applications.
The company uses AWS Glue jobs to process data for the dashboard. The company stores the data in a single Amazon S3 bucket. The company adds new data every day.
A data engineer discovers that dashboard queries are becoming slower over time. The data engineer determines that the root cause of the slowing queries is long-running AWS Glue jobs.
Which actions should the data engineer take to improve the performance of the AWS Glue jobs? (Choose two.)

A. Increase the AWS Glue instance size by scaling up the worker type.
B. Modify the 1AM role that grants access to AWS glue to grant access to all S3 features.
C. Adjust AWS Glue job scheduling frequency so the jobs run half as many times each day.
D. Partition the data that is in the S3 bucket. Organize the data by year, month, and day.
E. Convert the AWS Glue schema to the DynamicFrame schema class.

Answer: A,D

Explanation:
Partitioning the data in the S3 bucket can improve the performance of AWS Glue jobs by reducing the amount of data that needs to be scanned and processed. By organizing the data by year, month, and day, the AWS Glue job can use partition pruning to filter out irrelevant data and only read the data that matches the query criteria. This can speed up the data processing and reduce the cost of running the AWS Glue job.
Increasing the AWS Glue instance size by scaling up the worker type can also improve the performance of AWS Glue jobs by providing more memory and CPU resources for the Spark execution engine. This can help the AWS Glue job handle larger data sets and complex transformations more efficiently. The other options are either incorrect or irrelevant, as they do not affect the performance of the AWS Glue jobs. Converting the AWS Glue schema to the DynamicFrame schema class does not improve the performance, but rather provides additional functionality and flexibility for data manipulation. Adjusting the AWS Glue job scheduling frequency does not improve the performance, but rather reduces the frequency of data updates. Modifying the IAM role that grants access to AWS Glue does not improve the performance, but rather affects the security and permissions of the AWS Glue service. References:
Optimising Glue Scripts for Efficient Data Processing: Part 1 (Section: Partitioning Data in S3) Best practices to optimize cost and performance for AWS Glue streaming ETL jobs (Section: Development tools) Monitoring with AWS Glue job run insights (Section: Requirements) AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide (Chapter 5, page 133)

NEW QUESTION # 44
A company uses Amazon DataZone as a data governance and business catalog solution. The company stores data in an Amazon S3 data lake. The company uses AWS Glue with an AWS Glue Data Catalog.
A data engineer needs to publish AWS Glue Data Quality scores to the Amazon DataZone portal.
Which solution will meet this requirement?

A. Configure AWS Glue ETL jobs to use an Evaluate Data Quality transform. Define a data quality ruleset inside the jobs. Configure the Amazon DataZone project to have an AWS Glue data source. Enable the data quality configuration for the data source.
B. Create a data quality ruleset with Data Quality Definition Language (DQDL) rules that apply to a specific AWS Glue table. Schedule the ruleset to run daily. Configure the Amazon DataZone project to have an Amazon Redshift data source. Enable the data quality configuration for the data source.
C. Create a data quality ruleset with Data Quality Definition Language (DQDL) rules that apply to a specific AWS Glue table. Schedule the ruleset to run daily. Configure the Amazon DataZone project to have an AWS Glue data source. Enable the data quality configuration for the data source.
D. Configure AWS Glue ETL jobs to use an Evaluate Data Quality transform. Define a data quality ruleset inside the jobs. Configure the Amazon DataZone project to have an Amazon Redshift data source.
Enable the data quality configuration for the data source.

Answer: C

Explanation:
Publishing AWS Glue data quality scores to Amazon DataZone requires creating a DQDL ruleset, scheduling it to run regularly, and then linking the corresponding AWS Glue table as a data source in the DataZone project. The setup ensures that data quality scores from Glue are correctly published and accessible within Amazon DataZone:
"You can define DQDL rulesets for Glue tables and publish the data quality results to DataZone when the project is configured with an AWS Glue data source and the rulesets are scheduled."
- Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf Option C follows the expected flow without unnecessary complexity and aligns perfectly with the integration flow supported by AWS.

NEW QUESTION # 45
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?

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

Answer: B

Explanation:
For secure migration of data from an on-premises data center to AWS without using the public internet, AWS Direct Connect is the most secure and reliable method. Using Secrets Manager to store service account credentials ensures that the credentials are managed securely with automatic rotation.
* AWS Direct Connect:
* Direct Connect establishes a dedicated, private connection between the on-premises data center and AWS, avoiding the public internet. This is ideal for secure, high-speed data transfers.

NEW QUESTION # 46
A company stores sales data in an Amazon RDS for MySQL database. The company needs to start a reporting process between 6:00 A.M. and 6:10 A.M. every Monday. The reporting process must generate a CSV file and store the file in an Amazon S3 bucket.
Which combination of steps will meet these requirements with the LEAST operational overhead? (Select TWO.)

A. Create and invoke an Amazon EMR Serverless job to generate the report and to save it to the S3 bucket.
B. Create an Amazon EventBridge rule to run every Monday at 6:00 A.M.
C. Create and invoke an AWS Batch job that runs a script in an Amazon Elastic Container Service (Amazon ECS) container. Configure the script to generate the report and to save it to the S3 bucket.
D. Create and invoke an AWS Glue ETL job to generate the report and to save it to the S3 bucket.
E. Create an Amazon EventBridge Scheduler to run every Monday at 6:00 A.M.

Answer: D,E

Explanation:
The Amazon EventBridge Scheduler offers a simple, serverless cron-based execution mechanism. It can trigger an AWS Glue ETL job that extracts data from Amazon RDS, formats it as CSV, and writes it to Amazon S3 - all without manual orchestration or servers.
"For scheduled data extraction and transformation, use AWS Glue jobs triggered by EventBridge Scheduler for fully managed, low-maintenance workflows."
- Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf Glue natively integrates with RDS and S3, avoiding the need to manage Batch or EMR infrastructure.

NEW QUESTION # 47
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