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Amazon Data-Engineer-Associate Exam Syllabus Topics:

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

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Amazon AWS Certified Data Engineer - Associate (DEA-C01) Sample Questions (Q291-Q296):

NEW QUESTION # 291
A company uses AWS Glue jobs to implement several data pipelines. The pipelines are critical to the company.
The company needs to implement a monitoring mechanism that will alert stakeholders if the pipelines fail.
Which solution will meet these requirements with the LEAST operational overhead?

Answer: A

Explanation:
Creating an EventBridge rule that triggers a Lambda function on AWS Glue job failure events and then sends notifications via Amazon SNS is the most direct and operationally efficient method:
"Practice Quiz 10: A data engineer must monitor the data pipeline... Which solution will meet these requirements?
A: Inspect the job run monitoring section of the AWS Glue console.
-Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf Although this reference directly supports using AWS Glue's monitoring features via EventBridge, it implies that solutions likeA-which directly use EventBridge failure events for automation-are more optimal and less complex than constructing custom logs and metrics.


NEW QUESTION # 292
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: A

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.
Reference:
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 # 293
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: A

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 # 294
A food delivery company manages thousands of deliveries simultaneously. Each delivery vehicle transmits real-time telemetry data as JSON events. The company wants to accelerate downstream analytics and simplify data processing. The company needs to flatten the telemetry data and then store the data in an Amazon S3 bucket.
Which solution will meet these requirements with the LEAST latency?

Answer: D

Explanation:
The correct answer is B because AWS Glue streaming ETL jobs are built to continuously consume data from streaming sources such as Amazon Kinesis Data Streams, transform the data, and load the results into Amazon S3 data lakes. The requirement is not just delivery to S3; the telemetry JSON must be flattened before storage, and this needs to happen with low latency. Amazon Data Firehose can deliver streaming data, but the option incorrectly assumes automatic flattening without a transformation layer.
SQS plus scheduled batch Glue introduces batch delay and does not meet the lowest-latency requirement. Athena is a query engine, not a real-time transformation and ingestion service for writing flattened streaming output to S3. AWS Glue streaming ETL is the correct serverless streaming transformation layer.


NEW QUESTION # 295
A data engineer is building a data pipeline. A large data file is uploaded to an Amazon S3 bucket once each day at unpredictable times. An AWS Glue workflow uses hundreds of workers to process the file and load the data into Amazon Redshift. The company wants to process the file as quickly as possible.
Which solution will meet these requirements?

Answer: C

Explanation:
The best solution for fast, event-driven processing of unpredictable file uploads is to use S3 event notifications, CloudTrail, and EventBridge to automatically trigger the AWS Glue workflow:
"You can configure S3 PutObject events to be captured by CloudTrail and forwarded through EventBridge to trigger an AWS Glue job or workflow. This allows Glue to begin processing as soon as the file arrives, with minimal latency."
- Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf This option provides the lowest latency and least manual overhead compared to polling or scheduling solutions.


NEW QUESTION # 296
......

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