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Amazon Data-Engineer-Associate Exam Syllabus Topics:

Section	Weight	Objectives
Data Store Management	26%	- Manage data lifecycle 1. Data archiving 2. Data retention policies 3. Amazon S3 storage classes - Choose a data store 1. Data lakes vs. data warehouses 2. Access and storage patterns 3. Data characteristics (structured, semi-structured, unstructured) 4. Amazon S3, Amazon RDS, Amazon DynamoDB, Amazon Redshift - Design data models 1. Schema design 2. Normalization and denormalization 3. Partitioning and indexing strategies - Understand data cataloging 1. Data discovery and classification 2. AWS Glue Data Catalog 3. Schema evolution
Data Security and Governance	18%	- Implement data quality checks 1. AWS Glue DataBrew 2. Data validation - Manage data privacy and compliance 1. PII data handling 2. Data masking and tokenization 3. AWS Lake Formation permissions - Apply authentication and authorization 1. Service control policies (SCPs) 2. Amazon S3 bucket policies 3. AWS IAM policies and roles - Ensure data encryption 1. Encryption at rest and in transit 2. AWS KMS
Data Ingestion and Transformation	34%	- Transform and process data 1. ETL/ELT patterns 2. Data partitioning and compression 3. Batch and stream processing 4. Data transformation services (AWS Glue, Amazon EMR, AWS Lambda) - Apply programming concepts 1. SQL, Python, Scala 2. Infrastructure as Code (IaC) 3. Version control - Perform data ingestion 1. Replayability of data 2. Batch data ingestion (scheduled ingestion, event-driven ingestion) 3. Data ingestion patterns (frequency and data history) 4. Throughput and latency characteristics for AWS services 5. Streaming data ingestion - Orchestrate data pipelines 1. AWS Step Functions 2. AWS Glue Workflows 3. Event-driven architectures 4. Amazon Managed Workflows for Apache Airflow (MWAA)
Data Operations and Support	22%	- Automate data pipelines 1. Scheduling jobs 2. AWS Lambda triggers 3. Event-driven triggers - Manage and troubleshoot data processes 1. Cost optimization 2. Performance tuning 3. Debugging failed jobs - Monitor data pipelines 1. AWS CloudTrail 2. Logging and metrics 3. Amazon CloudWatch

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Amazon AWS Certified Data Engineer - Associate (DEA-C01) Sample Questions (Q237-Q242):

NEW QUESTION # 237
A data engineer must orchestrate a series of Amazon Athena queries that will run every day. Each query can run for more than 15 minutes.
Which combination of steps will meet these requirements MOST cost-effectively? (Choose two.)

A. Use an AWS Lambda function and the Athena Boto3 client start_query_execution API call to invoke the Athena queries programmatically.
B. Use an AWS Glue Python shell script to run a sleep timer that checks every 5 minutes to determine whether the current Athena query has finished running successfully. Configure the Python shell script to invoke the next query when the current query has finished running.
C. Use an AWS Glue Python shell job and the Athena Boto3 client start_query_execution API call to invoke the Athena queries programmatically.
D. Use Amazon Managed Workflows for Apache Airflow (Amazon MWAA) to orchestrate the Athena queries in AWS Batch.
E. Create an AWS Step Functions workflow and add two states. Add the first state before the Lambda function. Configure the second state as a Wait state to periodically check whether the Athena query has finished using the Athena Boto3 get_query_execution API call. Configure the workflow to invoke the next query when the current query has finished running.

Answer: A,E

Explanation:
Option A and B are the correct answers because they meet the requirements most cost-effectively. Using an AWS Lambda function and the Athena Boto3 client start_query_execution API call to invoke the Athena queries programmatically is a simple and scalable way to orchestrate the queries. Creating an AWS Step Functions workflow and adding two states to check the query status and invoke the next query is a reliable and efficient way to handle the long-running queries.
Option C is incorrect because using an AWS Glue Python shell job to invoke the Athena queries programmatically is more expensive than using a Lambda function, as it requires provisioning and running a Glue job for each query.
Option D is incorrect because using an AWS Glue Python shell script to run a sleep timer that checks every 5 minutes to determine whether the current Athena query has finished running successfully is not a cost- effective or reliable way to orchestrate the queries, as it wastes resources and time.
Option E is incorrect because using Amazon Managed Workflows for Apache Airflow (Amazon MWAA) to orchestrate the Athena queries in AWS Batch is an overkill solution that introduces unnecessary complexity and cost, as it requires setting up and managing an Airflow environment and an AWS Batch compute environment.
References:
* AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide, Chapter 5: Data Orchestration, Section 5.2: AWS Lambda, Section 5.3: AWS Step Functions, Pages 125-135
* Building Batch Data Analytics Solutions on AWS, Module 5: Data Orchestration, Lesson 5.1: AWS Lambda, Lesson 5.2: AWS Step Functions, Pages 1-15
* AWS Documentation Overview, AWS Lambda Developer Guide, Working with AWS Lambda Functions, Configuring Function Triggers, Using AWS Lambda with Amazon Athena, Pages 1-4
* AWS Documentation Overview, AWS Step Functions Developer Guide, Getting Started, Tutorial:
Create a Hello World Workflow, Pages 1-8

NEW QUESTION # 238
A data engineer is launching an Amazon EMR duster. The data that the data engineer needs to load into the new cluster is currently in an Amazon S3 bucket. The data engineer needs to ensure that data is encrypted both at rest and in transit.
The data that is in the S3 bucket is encrypted by an AWS Key Management Service (AWS KMS) key. The data engineer has an Amazon S3 path that has a Privacy Enhanced Mail (PEM) file.
Which solution will meet these requirements?

A. Create an Amazon EMR security configuration. Specify the appropriate AWS KMS key for at-rest encryption for the S3 bucket. Create a second security configuration. Specify the Amazon S3 path of the PEM file for in-transit encryption. Create the EMR cluster, and attach both security configurations to the cluster.
B. Create an Amazon EMR security configuration. Specify the appropriate AWS KMS key for local disk encryption for the S3 bucket. Specify the Amazon S3 path of the PEM file for in-transit encryption. Use the security configuration during EMR cluster creation.
C. Create an Amazon EMR security configuration. Specify the appropriate AWS KMS key for at-rest encryption for the S3 bucket. Specify the Amazon S3 path of the PEM file for in-transit encryption. Create the EMR cluster, and attach the security configuration to the cluster.
D. Create an Amazon EMR security configuration. Specify the appropriate AWS KMS key for at-rest encryption for the S3 bucket. Specify the Amazon S3 path of the PEM file for in-transit encryption. Use the security configuration during EMR cluster creation.

Answer: D

Explanation:
The data engineer needs to ensure that the data in an Amazon EMR cluster is encrypted both at rest and in transit. The data in Amazon S3 is already encrypted using an AWS KMS key. To meet the requirements, the most suitable solution is to create an EMR security configuration that specifies the correct KMS key for at-rest encryption and use the PEM file for in-transit encryption.
Option C: Create an Amazon EMR security configuration. Specify the appropriate AWS KMS key for at-rest encryption for the S3 bucket. Specify the Amazon S3 path of the PEM file for in-transit encryption. Use the security configuration during EMR cluster creation.
This option configures encryption for both data at rest (using KMS keys) and data in transit (using the PEM file for SSL/TLS encryption). This approach ensures that data is fully protected during storage and transfer.
Options A, B, and D either involve creating unnecessary additional security configurations or make inaccurate assumptions about the way encryption configurations are attached.
Reference:
Amazon EMR Security Configuration
Amazon S3 Encryption

NEW QUESTION # 239
A technology company currently uses Amazon Kinesis Data Streams to collect log data in real time. The company wants to use Amazon Redshift for downstream real-time queries and to enrich the log data.
Which solution will ingest data into Amazon Redshift with the LEAST operational overhead?

A. Set up an Amazon Data Firehose delivery stream to send data to Amazon S3. Configure a Redshift provisioned cluster to load data every minute.
B. Use Amazon Redshift streaming ingestion from Kinesis Data Streams and to present data as a materialized view.
C. Configure Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) to send data directly to a Redshift provisioned cluster table.
D. Set up an Amazon Data Firehose delivery stream to send data to a Redshift provisioned cluster table.

Answer: B

Explanation:
The most efficient and low-operational-overhead solution for ingesting data into Amazon Redshift from Amazon Kinesis Data Streams is to use Amazon Redshift streaming ingestion. This feature allows Redshift to directly ingest streaming data from Kinesis Data Streams and process it in real-time.
Amazon Redshift Streaming Ingestion:
Redshift supports native streaming ingestion from Kinesis Data Streams, allowing real-time data to be queried using materialized views.
This solution reduces operational complexity because you don't need intermediary services like Amazon Kinesis Data Firehose or S3 for batch loading.
Reference:
Alternatives Considered:
A (Data Firehose to Redshift): This option is more suitable for batch processing but incurs additional operational overhead with the Firehose setup.
B (Firehose to S3): This involves an intermediate step, which adds complexity and delays the real-time requirement.
C (Managed Service for Apache Flink): This would work but introduces unnecessary complexity compared to Redshift's native streaming ingestion.
Amazon Redshift Streaming Ingestion from Kinesis
Materialized Views in Redshift

NEW QUESTION # 240
A company is building an inventory management system and an inventory reordering system to automatically reorder products. Both systems use Amazon Kinesis Data Streams. The inventory management system uses the Amazon Kinesis Producer Library (KPL) to publish data to a stream. The inventory reordering system uses the Amazon Kinesis Client Library (KCL) to consume data from the stream. The company configures the stream to scale up and down as needed.
Before the company deploys the systems to production, the company discovers that the inventory reordering system received duplicated data.
Which factors could have caused the reordering system to receive duplicated data? (Select TWO.)

A. There was a change in the number of shards, record processors, or both.
B. The AggregationEnabled configuration property was set to true.
C. The producer experienced network-related timeouts.
D. The stream's value for the IteratorAgeMilliseconds metric was too high.
E. The max_records configuration property was set to a number that was too high.

Answer: A,C

Explanation:
* Problem Analysis:
* The company usesKinesis Data Streamsfor both inventory management and reordering.
* TheKinesis Producer Library (KPL)publishes data, and theKinesis Client Library (KCL) consumes data.
* Duplicate records were observed in the inventory reordering system.
* Key Considerations:
* Kinesis streams are designed for durability but may produce duplicates under certain conditions.
* Factors such asnetwork timeouts,shard splits, or changes inrecord processorscan cause duplication.
* Solution Analysis:
* Option A: Network-Related Timeouts
* If the producer (KPL) experiences network timeouts, it retries data submission, potentially causing duplicates.
* Option B: High IteratorAgeMilliseconds
* High iterator age suggests delays in processing but does not directly cause duplication.
* Option C: Changes in Shards or Processors
* Changes in the number of shards or record processors can lead to re-processing of records, causing duplication.
* Option D: AggregationEnabled Set to True
* AggregationEnabled controls the aggregation of multiple records into one, but it does not cause duplication.
* Option E: High max_records Value
* A high max_records value increases batch size but does not lead to duplication.
* Final Recommendation:
* Network-related timeoutsandchanges in shards or processorsare the most likely causes of duplicate data in this scenario.
:
Amazon Kinesis Data Streams Best Practices
Kinesis Producer Library (KPL) Overview
Kinesis Client Library (KCL) Overview

NEW QUESTION # 241
A data engineer is troubleshooting an AWS Glue workflow that occasionally fails. The engineer determines that the failures are a result of data quality issues. A business reporting team needs to receive an email notification any time the workflow fails in the future.
Which solution will meet this requirement?

A. Create an Amazon Simple Queue Service (Amazon SQS) FIFO queue. Subscribe the team's email account to the SQS queue. Create an AWS Config rule that triggers when the AWS Glue job state changes to FAILED. Set the SQS queue as the target.
B. Create an Amazon Simple Notification Service (Amazon SNS) standard topic. Subscribe the team's email account to the SNS topic. Create an Amazon EventBridge rule that triggers when the AWS Glue Job state changes to FAILED. Set the SNS topic as the target.
C. Create an Amazon Simple Notification Service (Amazon SNS) FIFO topic. Subscribe the team's email account to the SNS topic. Create an AWS Lambda function that initiates when the AWS Glue job state changes to FAILED. Set the SNS topic as the target.
D. Create an Amazon Simple Queue Service (Amazon SQS) standard queue. Subscribe the team's email account to the SQS queue. Create an Amazon EventBridge rule that triggers when the AWS Glue job state changes to FAILED. Set the SQS queue as the target.

Answer: B

NEW QUESTION # 242
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