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Amazon Data-Engineer-Associate Exam Syllabus Topics:

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

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Amazon AWS Certified Data Engineer - Associate (DEA-C01) Sample Questions (Q156-Q161):

NEW QUESTION # 156
A retail company uses an Amazon Redshift data warehouse and an Amazon S3 bucket. The company ingests retail order data into the S3 bucket every day.
The company stores all order data at a single path within the S3 bucket. The data has more than 100 columns.
The company ingests the order data from a third-party application that generates more than 30 files in CSV format every day. Each CSV file is between 50 and 70 MB in size.
The company uses Amazon Redshift Spectrum to run queries that select sets of columns. Users aggregate metrics based on daily orders. Recently, users have reported that the performance of the queries has degraded.
A data engineer must resolve the performance issues for the queries.
Which combination of steps will meet this requirement with LEAST developmental effort? (Select TWO.)

Answer: C,D

Explanation:
The performance issue in Amazon Redshift Spectrum queries arises due to the nature of CSV files, which are row-based storage formats. Spectrum is more optimized for columnar formats, which significantly improve performance by reducing the amount of data scanned. Also, partitioning data based on relevant columns like order date can further reduce the amount of data scanned, as queries can focus only on the necessary partitions.
* A. Configure the third-party application to create the files in a columnar format:
* Columnar formats (like Parquet or ORC) store data in a way that is optimized for analytical queries because they allow queries to scan only the columns required, rather than scanning all columns in a row-based format like CSV.
* Amazon Redshift Spectrum works much more efficiently with columnar formats, reducing the amount of data that needs to be scanned, which improves query performance.


NEW QUESTION # 157
A retail company has a customer data hub in an Amazon S3 bucket. Employees from many countries use the data hub to support company-wide analytics. A governance team must ensure that the company's data analysts can access data only for customers who are within the same country as the analysts.
Which solution will meet these requirements with the LEAST operational effort?

Answer: A

Explanation:
AWS Lake Formation is a service that allows you to easily set up, secure, and manage data lakes. One of the features of Lake Formation is row-level security, which enables you to control access to specific rows or columns of data based on the identity or role of the user. This feature is useful for scenarios where you need to restrict access to sensitive or regulated data, such as customer data from different countries. By registering the S3 bucket as a data lake location in Lake Formation, you can use the Lake Formation console or APIs to define and apply row-level security policies to the data in the bucket. You can also use Lake Formation blueprints to automate the ingestion and transformation of data from various sources into the data lake. This solution requires the least operational effort compared to the other options, as it does not involve creating or moving data, or managing multiple tables, views, or roles. References:
AWS Lake Formation
Row-Level Security
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide, Chapter 4: Data Lakes and Data Warehouses, Section 4.2: AWS Lake Formation


NEW QUESTION # 158
A company has several new datasets in CSV and JSON formats. A data engineer needs to make the data available to a team of data analysts who will analyze the data by using SQL queries.
Which solution will meet these requirements in the MOST cost-effective way?

Answer: D

Explanation:
Option C is the most cost-effective because it keeps the datasets in Amazon S3 and uses Amazon Athena to query them with SQL only when needed, avoiding the cost of running always-on database infrastructure. The study material states that "Amazon Athena is a serverless service that allows you to query data stored in Amazon S3 using standard SQL" . This directly matches the requirement that analysts will "analyze the data by using SQL queries," while remaining cost-efficient due to serverless, on-demand querying.
To make CSV and JSON in S3 easily queryable, metadata must be discoverable and managed. The material also highlights that AWS Glue automates cataloging data in S3 through the AWS Glue Data Catalog, which
"helps discover and manage metadata for data stored in AWS," enabling query engines to treat file data as tables .
Other options are less aligned: RDS MySQL adds continuous capacity and administration costs; DataBrew is primarily for visual preparation (clean/normalize) rather than serving as the SQL query layer ; and QuickSight
/SPICE targets BI dashboards, not general-purpose ad hoc SQL over raw files.


NEW QUESTION # 159
A company is using Amazon S3 to build a data lake. The company needs to replicate records from multiple source databases into Apache Parquet format.
Most of the source databases are hosted on Amazon RDS. However, one source database is an on-premises Microsoft SQL Server Enterprise instance. The company needs to implement a solution to replicate existing data from all source databases and all future changes to the target S3 data lake.
Which solution will meet these requirements MOST cost-effectively?

Answer: A

Explanation:
AWS Database Migration Service (AWS DMS)is purpose-built to migrate and continuously replicate data from both AWS-hosted and on-premises databases. It supports full-load (existing data) andchange data capture (CDC)for ongoing changes, making itthe most cost-effective and operationally simplesolution in this scenario.
"DMS supports both full-load and continuous replication via CDC. This enables replicating existing and future data from various sources to a data lake in Amazon S3."
-Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf AWS Glue is not suitable for real-time CDC replication across hybrid environments.


NEW QUESTION # 160
A company needs a solution that restricts access to Amazon S3 data and encrypts the data by using AWS managed keys. The solution must manage database credentials that an AWS Lambda function uses and must rotate the credentials automatically.
Which solution will meet these requirements?

Answer: B

Explanation:
Option B is correct because IAM policies are the standard AWS mechanism to control access to Amazon S3, and SSE-KMS encrypts S3 data with AWS KMS keys. AWS S3 documentation for SSE-KMS explains that it uses AWS Key Management Service keys for server-side encryption, and Secrets Manager documentation states that you can store secrets securely and configure automatic rotation. AWS further explains that rotation updates the credentials in both the secret and the target database or service, and that Secrets Manager uses a Lambda rotation function for supported rotation patterns.
Option A is weaker because Lambda environment variables are not the right service for managed secret storage and automatic rotation. Option C is not the best answer because S3 ACLs are not the preferred modern access-control model compared with IAM and bucket policies, and Parameter Store is not the main AWS service for built-in managed database credential rotation. Option D uses SSE-S3, not KMS-based encryption, and relies on a custom rotation approach instead of the native managed secret-rotation service.
The study guide also highlights AWS KMS for encryption-key management and Secrets Manager for rotating credentials.


NEW QUESTION # 161
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