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Amazon Data-Engineer-Associate Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Data Ingestion and Transformation34%- Implement data quality and validation
- Transform and enrich data
  • 1. Orchestrate data pipelines
  • 2. Apply data processing logic
  • 3. Use Spark, EMR, Step Functions
- Ingest data from various sources
  • 1. Batch and streaming data ingestion
  • 2. Use services like Kinesis, DMS, Glue, S3
Topic 2: Data Store Management26%- Optimize storage performance and cost
- Manage data lifecycle and storage tiers
- Design and implement data storage solutions
  • 1. S3, Redshift, DynamoDB, RDS, Lake Formation
  • 2. Data lakes, data warehouses, databases
Topic 3: Data Operations and Support22%- Monitor and troubleshoot data pipelines
  • 1. CloudWatch, X-Ray, logging and metrics
- Backup, restore, and disaster recovery
- Ensure reliability and scalability
- Automate operational tasks
Topic 4: Data Security and Governance18%- Protect sensitive data
- Implement access control and authentication
  • 1. IAM, Lake Formation, resource policies
- Encrypt data at rest and in transit
- Enforce compliance and data governance
  • 1. Data lineage, audit logging, regulatory requirements

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Amazon AWS Certified Data Engineer - Associate (DEA-C01) Sample Questions (Q247-Q252):

NEW QUESTION # 247
A company uses a variety of AWS and third-party data stores. The company wants to consolidate all the data into a central data warehouse to perform analytics. Users need fast response times for analytics queries.
The company uses Amazon QuickSight in direct query mode to visualize the data. Users normally run queries during a few hours each day with unpredictable spikes.
Which solution will meet these requirements with the LEAST operational overhead?

Answer: D

Explanation:
Problem Analysis:
The company requires a centralized data warehouse for consolidating data from various sources.
They use Amazon QuickSight in direct query mode, necessitating fast response times for analytical queries.
Users query the data intermittently, with unpredictable spikes during the day.
Operational overhead should be minimal.
Key Considerations:
The solution must support fast, SQL-based analytics.
It must handle unpredictable spikes efficiently.
Must integrate seamlessly with QuickSight for direct querying.
Minimize operational complexity and scaling concerns.
Solution Analysis:
Option A: Amazon Redshift Serverless
Redshift Serverless eliminates the need for provisioning and managing clusters.
Automatically scales compute capacity up or down based on query demand.
Reduces operational overhead by handling performance optimization.
Fully integrates with Amazon QuickSight, ensuring low-latency analytics.
Reduces costs as it charges only for usage, making it ideal for workloads with intermittent spikes.
Option B: Amazon Athena with S3 (Apache Parquet)
Athena supports querying data directly from S3 in Parquet format.
While it's cost-effective, performance depends on the size and complexity of the data.
It is not optimized for high-speed analytics needed by QuickSight in direct query mode.
Option C: Amazon Redshift Provisioned Clusters
Requires manual cluster provisioning, scaling, and maintenance.
Higher operational overhead compared to Redshift Serverless.
Option D: Amazon Aurora PostgreSQL
Aurora is optimized for transactional databases, not data warehousing or analytics.
Does not meet the requirement for fast analytics queries.
Final Recommendation:
Amazon Redshift Serverless is the best choice for this use case because it provides fast analytics, integrates natively with QuickSight, and minimizes operational complexity while efficiently handling unpredictable spikes.
Reference:
Amazon Redshift Serverless Overview
Amazon QuickSight and Redshift Integration


NEW QUESTION # 248
A company is setting up a data pipeline in AWS. The pipeline extracts client data from Amazon S3 buckets, performs quality checks, and transforms the data. The pipeline stores the processed data in a relational database. The company will use the processed data for future queries.
Which solution will meet these requirements MOST cost-effectively?

Answer: B


NEW QUESTION # 249
A data engineer needs to create an AWS Lambda function that converts the format of data from .csv to Apache Parquet. The Lambda function must run only if a user uploads a .csv file to an Amazon S3 bucket.
Which solution will meet these requirements with the LEAST operational overhead?

Answer: B

Explanation:
Option A is the correct answer because it meets the requirements with the least operational overhead. Creating an S3 event notification that has an event type of s3:ObjectCreated:* will trigger the Lambda function whenever a new object is created in the S3 bucket. Using a filter rule to generate notifications only when the suffix includes .csv will ensure that the Lambda function only runs for .csv files. Setting the ARN of the Lambda function as the destination for the event notification will directly invoke the Lambda function without any additional steps.
Option B is incorrect because it requires the user to tag the objects with .csv, which adds an extra step and increases the operational overhead.
Option C is incorrect because it uses an event type of s3:*, which will trigger the Lambda function for any S3 event, not just object creation. This could result in unnecessary invocations and increased costs.
Option D is incorrect because it involves creating and subscribing to an SNS topic, which adds an extra layer of complexity and operational overhead.
:
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide, Chapter 3: Data Ingestion and Transformation, Section 3.2: S3 Event Notifications and Lambda Functions, Pages 67-69 Building Batch Data Analytics Solutions on AWS, Module 4: Data Transformation, Lesson 4.2: AWS Lambda, Pages 4-8 AWS Documentation Overview, AWS Lambda Developer Guide, Working with AWS Lambda Functions, Configuring Function Triggers, Using AWS Lambda with Amazon S3, Pages 1-5


NEW QUESTION # 250
A company needs to implement a data mesh architecture for trading, risk, and compliance teams. Each team has its own data but needs to share views. They have 1,000+ tables in 50 Glue databases. All teams use Athena and Redshift, and compliance requires full auditing and PII access control.

Answer: D

Explanation:
A data mesh approach in AWS typically uses Lake Formation for domain-level access control and Athena for cross-domain querying through federated views. CloudTrail ensures auditing.
"For data mesh architectures, use AWS Lake Formation for fine-grained access control and Athena views for cross-domain analysis. Enable CloudTrail to audit access."
- Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf This ensures scalability, security, and compliance across domains.


NEW QUESTION # 251
A company receives .csv files that contain physical address data. The data is in columns that have the following names: Door_No, Street_Name, City, and Zip_Code. The company wants to create a single column to store these values in the following format:

Which solution will meet this requirement with the LEAST coding effort?

Answer: B

Explanation:
The NEST TO MAP transformation allows you to combine multiple columns into a single column that contains a JSON object with key-value pairs. This is the easiest way to achieve the desired format for the physical address data, as you can simply select the columns to nest and specify the keys for each column. The NEST TO ARRAY transformation creates a single column that contains an array of values, which is not the same as the JSON object format. The PIVOT transformation reshapes the data by creating new columns from unique values in a selected column, which is not applicable for this use case. Writing a Lambda function in Python requires more coding effort than using AWS Glue DataBrew, which provides a visual and interactive interface for data transformations. References:
* 7 most common data preparation transformations in AWS Glue DataBrew (Section: Nesting and unnesting columns)
* NEST TO MAP - AWS Glue DataBrew (Section: Syntax)


NEW QUESTION # 252
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