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Amazon Data-Engineer-Associate Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Data Security and Governance18%- Implement data quality checks
  • 1. AWS Glue DataBrew
  • 2. Data validation
- Manage data privacy and compliance
  • 1. PII data handling
  • 2. AWS Lake Formation permissions
  • 3. Data masking and tokenization
- Apply authentication and authorization
  • 1. AWS IAM policies and roles
  • 2. Amazon S3 bucket policies
  • 3. Service control policies (SCPs)
- Ensure data encryption
  • 1. Encryption at rest and in transit
  • 2. AWS KMS
Topic 2: Data Ingestion and Transformation34%- Orchestrate data pipelines
  • 1. AWS Glue Workflows
  • 2. Amazon Managed Workflows for Apache Airflow (MWAA)
  • 3. Event-driven architectures
  • 4. AWS Step Functions
- Apply programming concepts
  • 1. SQL, Python, Scala
  • 2. Version control
  • 3. Infrastructure as Code (IaC)
- Transform and process data
  • 1. Data transformation services (AWS Glue, Amazon EMR, AWS Lambda)
  • 2. ETL/ELT patterns
  • 3. Batch and stream processing
  • 4. Data partitioning and compression
- Perform data ingestion
  • 1. Throughput and latency characteristics for AWS services
  • 2. Replayability of data
  • 3. Streaming data ingestion
  • 4. Data ingestion patterns (frequency and data history)
  • 5. Batch data ingestion (scheduled ingestion, event-driven ingestion)
Topic 3: Data Operations and Support22%- Automate data pipelines
  • 1. Event-driven triggers
  • 2. Scheduling jobs
  • 3. AWS Lambda triggers
- Monitor data pipelines
  • 1. Amazon CloudWatch
  • 2. AWS CloudTrail
  • 3. Logging and metrics
- Manage and troubleshoot data processes
  • 1. Cost optimization
  • 2. Debugging failed jobs
  • 3. Performance tuning
Topic 4: Data Store Management26%- Choose a data store
  • 1. Access and storage patterns
  • 2. Data lakes vs. data warehouses
  • 3. Amazon S3, Amazon RDS, Amazon DynamoDB, Amazon Redshift
  • 4. Data characteristics (structured, semi-structured, unstructured)
- Design data models
  • 1. Schema design
  • 2. Partitioning and indexing strategies
  • 3. Normalization and denormalization
- Manage data lifecycle
  • 1. Amazon S3 storage classes
  • 2. Data retention policies
  • 3. Data archiving
- Understand data cataloging
  • 1. Data discovery and classification
  • 2. Schema evolution
  • 3. AWS Glue Data Catalog

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Amazon AWS Certified Data Engineer - Associate (DEA-C01) Sample Questions (Q171-Q176):

NEW QUESTION # 171
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?

Answer: A

Explanation:
Amazon Athena provides federated query connectors that allow querying multiple data sources, such as Amazon Redshift, Teradata, and Google BigQuery, without needing to extract the data from the original source. This solution is optimal because it offers the least operational effort by avoiding complex data movement and transformation processes.
* Amazon Athena Federated Queries:
* Athena's federated queries allow direct querying of data stored across multiple sources, including Amazon Redshift, Teradata, and BigQuery. With Athena's support for Apache Iceberg, the company can easily run a Merge operation on the Iceberg table.
* The solution reduces complexity by centralizing the query execution and transformation process in Athena using SQL queries.


NEW QUESTION # 172
Two developers are working on separate application releases. The developers have created feature branches named Branch A and Branch B by using a GitHub repository's master branch as the source.
The developer for Branch A deployed code to the production system. The code for Branch B will merge into a master branch in the following week's scheduled application release.
Which command should the developer for Branch B run before the developer raises a pull request to the master branch?

Answer: D

Explanation:
To ensure that Branch B is up to date with the latest changes in the master branch before submitting a pull request, the correct approach is to perform a git rebase. This command rewrites the commit history so that Branch B will be based on the latest changes in the master branch.
* git rebase master:
* This command moves the commits of Branch B to be based on top of the latest state of the master branch. It allows the developer to resolve any conflicts and create a clean history.


NEW QUESTION # 173
A company receives call logs as Amazon S3 objects that contain sensitive customer information. The company must protect the S3 objects by using encryption. The company must also use encryption keys that only specific employees can use.
Which solution will meet these requirements with the LEAST effort?

Answer: A

Explanation:
Option C is correct because SSE-KMS encrypts Amazon S3 objects by using AWS KMS keys, and access to those keys can be controlled through IAM policies and KMS key policies so that only specific employees can use them. This directly satisfies both requirements: encryption of the S3 objects and restriction of key usage to a limited group of authorized users. AWS-managed S3 encryption with KMS also requires far less effort than building or operating a dedicated HSM-based solution. This is the standard AWS answer when the question asks for encrypted S3 data with fine-grained control over who can use the encryption keys.
Option A is far more operationally complex because CloudHSM requires managing dedicated HSM infrastructure. Option B is less desirable operationally because SSE-C requires the customer to provide and manage the keys for every request. Option D is incorrect because SSE-S3 uses Amazon S3 managed keys, and customers do not get the same granular control over key usage by specific employees. Therefore, the least- effort and best-governed solution is SSE-KMS with restricted KMS key access. This aligns with the study guide's security-and-governance focus on using AWS KMS when controlled access to encryption keys is required.


NEW QUESTION # 174
A data engineer needs to make tabular data available in an Amazon S3-based data lake. Users must be able to query the data by using SQL queries in Amazon Redshift, Amazon Athena, and Amazon EMR. The data is updated daily. The data engineer must ensure that updates and deletions are reflected in the data lake.
Which solution will meet these requirements with the LEAST operational overhead?

Answer: A

Explanation:
Comprehensive and Detailed Explanation (150-250 words)
Apache Iceberg is a table format designed for large-scale data lakes that supports ACID transactions, schema evolution, time travel, and row-level updates and deletes. Using S3 Tables with Apache Iceberg provides a fully managed experience that integrates natively with Amazon Athena, Amazon Redshift, and Amazon EMR.
By using AWS Glue with the Iceberg catalog, the data engineer can perform daily updates and deletions without managing Spark clusters, compaction scheduling, or metadata cleanup manually. Iceberg handles snapshots, file pruning, and unreferenced file removal automatically, significantly reducing operational overhead.
Apache Hudi requires Amazon EMR clusters, Spark jobs, and manual compaction orchestration, increasing complexity. The Parquet-only approaches in options C and D do not support updates or deletes efficiently and would require full rewrites of datasets, which is not scalable.
Therefore, using S3 Tables with Apache Iceberg provides the most efficient, scalable, and low-maintenance solution that satisfies all query and update requirements.


NEW QUESTION # 175
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?

Answer: B

Explanation:
Amazon Redshift supportsdynamic data masking, which enables you to limit sensitive data visibility to specific users and roles without duplicating the data. This approach supports showing only parts of a column's values (e.g., last four digits) and full visibility for authorized roles (e.g., auditors).
"With dynamic data masking, you can control how much sensitive data a user sees in query results without changing the data in the table."
-Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf IAM roles are used to associate users with the appropriate masking rules, keeping security tight and avoiding the creation of duplicate data views or tables.


NEW QUESTION # 176
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