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Amazon Data-Engineer-Associate Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Operations and Support	22%	- Monitor and troubleshoot data pipelines 1. CloudWatch, X-Ray, logging and metrics - Automate operational tasks - Ensure reliability and scalability - Backup, restore, and disaster recovery
Topic 2: Data Ingestion and Transformation	34%	- Transform and enrich data 1. Use Spark, EMR, Step Functions 2. Orchestrate data pipelines 3. Apply data processing logic - Ingest data from various sources 1. Batch and streaming data ingestion 2. Use services like Kinesis, DMS, Glue, S3 - Implement data quality and validation
Topic 3: Data Store Management	26%	- Manage data lifecycle and storage tiers - Optimize storage performance and cost - Design and implement data storage solutions 1. Data lakes, data warehouses, databases 2. S3, Redshift, DynamoDB, RDS, Lake Formation
Topic 4: Data Security and Governance	18%	- Enforce compliance and data governance 1. Data lineage, audit logging, regulatory requirements - Protect sensitive data - Encrypt data at rest and in transit - Implement access control and authentication 1. IAM, Lake Formation, resource policies

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Amazon AWS Certified Data Engineer - Associate (DEA-C01) Sample Questions (Q206-Q211):

NEW QUESTION # 206
A company has as JSON file that contains personally identifiable information (PIT) data and non-PII data.
The company needs to make the data available for querying and analysis. The non-PII data must be available to everyone in the company. The PII data must be available only to a limited group of employees. Which solution will meet these requirements with the LEAST operational overhead?

A. Create two Amazon RDS PostgreSQL databases. Load the PII data and the non-PII data into the separate databases. Grant access to the databases based on the type of user.
B. Store the JSON file in an Amazon S3 bucket. Catalog the file schema in AWS Lake Formation. Use Lake Formation permissions to provide access to the required data based on the type of user.
C. Store the JSON file in an Amazon S3 bucket. Configure AWS Glue to split the file into one file that contains the PII data and one file that contains the non-PII data. Store the output files in separate S3 buckets. Grant the required access to the buckets based on the type of user.
D. Store the JSON file in an Amazon S3 bucket. Use Amazon Macie to identify PII data and to grant access based on the type of user.

Answer: B

NEW QUESTION # 207
A company currently stores all of its data in Amazon S3 by using the S3 Standard storage class.
A data engineer examined data access patterns to identify trends. During the first 6 months, most data files are accessed several times each day. Between 6 months and 2 years, most data files are accessed once or twice each month. After 2 years, data files are accessed only once or twice each year.
The data engineer needs to use an S3 Lifecycle policy to develop new data storage rules. The new storage solution must continue to provide high availability.
Which solution will meet these requirements in the MOST cost-effective way?

A. Transition objects to S3 One Zone-Infrequent Access (S3 One Zone-IA) after 6 months. Transfer objects to S3 Glacier Deep Archive after 2 years.
B. Transition objects to S3 Standard-Infrequent Access (S3 Standard-IA) after 6 months. Transfer objects to S3 Glacier Flexible Retrieval after 2 years.
C. Transition objects to S3 Standard-Infrequent Access (S3 Standard-IA) after 6 months. Transfer objects to S3 Glacier Deep Archive after 2 years.
D. Transition objects to S3 One Zone-Infrequent Access (S3 One Zone-IA) after 6 months. Transfer objects to S3 Glacier Flexible Retrieval after 2 years.

Answer: C

Explanation:
To achieve the most cost-effective storage solution, the data engineer needs to use an S3 Lifecycle policy that transitions objects to lower-cost storage classes based on their access patterns, and deletes them when they are no longer needed. The storage classes should also provide high availability, which means they should be resilient to the loss of data in a single Availability Zone1. Therefore, the solution must include the following steps:
Transition objects to S3 Standard-Infrequent Access (S3 Standard-IA) after 6 months. S3 Standard-IA is designed for data that is accessed less frequently, but requires rapid access when needed. It offers the same high durability, throughput, and low latency as S3 Standard, but with a lower storage cost and a retrieval fee2. Therefore, it is suitable for data files that are accessed once or twice each month. S3 Standard-IA also provides high availability, as it stores data redundantly across multiple Availability Zones1.
Transfer objects to S3 Glacier Deep Archive after 2 years. S3 Glacier Deep Archive is the lowest-cost storage class that offers secure and durable storage for data that is rarely accessed and can tolerate a 12-hour retrieval time. It is ideal for long-term archiving and digital preservation3. Therefore, it is suitable for data files that are accessed only once or twice each year. S3 Glacier Deep Archive also provides high availability, as it stores data across at least three geographically dispersed Availability Zones1.
Delete objects when they are no longer needed. The data engineer can specify an expiration action in the S3 Lifecycle policy to delete objects after a certain period of time. This will reduce the storage cost and comply with any data retention policies.
Option C is the only solution that includes all these steps. Therefore, option C is the correct answer.
Option A is incorrect because it transitions objects to S3 One Zone-Infrequent Access (S3 One Zone-IA) after 6 months. S3 One Zone-IA is similar to S3 Standard-IA, but it stores data in a single Availability Zone. This means it has a lower availability and durability than S3 Standard-IA, and it is not resilient to the loss of data in a single Availability Zone1. Therefore, it does not provide high availability as required.
Option B is incorrect because it transfers objects to S3 Glacier Flexible Retrieval after 2 years. S3 Glacier Flexible Retrieval is a storage class that offers secure and durable storage for data that is accessed infrequently and can tolerate a retrieval time of minutes to hours. It is more expensive than S3 Glacier Deep Archive, and it is not suitable for data that is accessed only once or twice each year3. Therefore, it is not the most cost-effective option.
Option D is incorrect because it combines the errors of option A and B. It transitions objects to S3 One Zone-IA after 6 months, which does not provide high availability, and it transfers objects to S3 Glacier Flexible Retrieval after 2 years, which is not the most cost-effective option.
Reference:
1: Amazon S3 storage classes - Amazon Simple Storage Service
2: Amazon S3 Standard-Infrequent Access (S3 Standard-IA) - Amazon Simple Storage Service
3: Amazon S3 Glacier and S3 Glacier Deep Archive - Amazon Simple Storage Service
[4]: Expiring objects - Amazon Simple Storage Service
[5]: Managing your storage lifecycle - Amazon Simple Storage Service
[6]: Examples of S3 Lifecycle configuration - Amazon Simple Storage Service
[7]: Amazon S3 Lifecycle further optimizes storage cost savings with new features - What's New with AWS

NEW QUESTION # 208
A retail company needs to implement a solution to capture data updates from multiple Amazon Aurora MySQL databases. The company needs to make the updates available for analytics in near real time. The solution must be serverless and require minimal maintenance.
Which solution will meet these requirements with the LEAST operational overhead?

A. Set up AWS Database Migration Service (AWS DMS) tasks that perform schema conversions for each database. Load the changes into Amazon Redshift Serverless.
B. Use Amazon Managed Streaming for Apache Kafka (Amazon MSK) Connect with Debezium connectors to load data into Amazon Redshift Serverless.
C. Use Aurora zero-ETL integrations with Amazon Redshift Serverless for each database to load Aurora MySQL changes in Amazon Redshift Serverless.
D. Use AWS Database Migration Service (AWS DMS) to set up binary log replication to Amazon Kinesis Data Streams. Load the data into Amazon Redshift Serverless after schema conversion.

Answer: C

Explanation:
Comprehensive and Detailed Explanation (150-250 words)
Aurora zero-ETL integration with Amazon Redshift Serverless is specifically designed to provide near real- time analytics on Aurora transactional data without the need to build or manage data pipelines. This integration continuously replicates data changes from Aurora MySQL into Redshift Serverless automatically.
The solution is fully serverless, requires no infrastructure provisioning, and eliminates the need for schema conversion, replication tasks, or streaming frameworks. Data is kept in sync with low latency, making it immediately available for analytical queries in Redshift Serverless.
AWS DMS-based solutions require task configuration, monitoring, and maintenance. Amazon MSK with Debezium introduces significant operational complexity and is not serverless. Kinesis-based replication pipelines require additional services and ongoing operational management.
Aurora zero-ETL integration is the lowest-maintenance, highest-efficiency solution and is explicitly recommended by AWS for this use case.

NEW QUESTION # 209
A company stores sales data in an Amazon RDS for MySQL database. The company needs to start a reporting process between 6:00 A.M. and 6:10 A.M. every Monday. The reporting process must generate a CSV file and store the file in an Amazon S3 bucket.
Which combination of steps will meet these requirements with the LEAST operational overhead? (Select TWO.)

A. Create and invoke an AWS Glue ETL job to generate the report and to save it to the S3 bucket.
B. Create and invoke an AWS Batch job that runs a script in an Amazon Elastic Container Service (Amazon ECS) container. Configure the script to generate the report and to save it to the S3 bucket.
C. Create an Amazon EventBridge rule to run every Monday at 6:00 A.M.
D. Create and invoke an Amazon EMR Serverless job to generate the report and to save it to the S3 bucket.
E. Create an Amazon EventBridge Scheduler to run every Monday at 6:00 A.M.

Answer: A,E

Explanation:
The Amazon EventBridge Scheduler offers a simple, serverless cron-based execution mechanism. It can trigger an AWS Glue ETL job that extracts data from Amazon RDS, formats it as CSV, and writes it to Amazon S3 - all without manual orchestration or servers.
"For scheduled data extraction and transformation, use AWS Glue jobs triggered by EventBridge Scheduler for fully managed, low-maintenance workflows."
- Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf Glue natively integrates with RDS and S3, avoiding the need to manage Batch or EMR infrastructure.

NEW QUESTION # 210
A company processes a CSV file that contains millions of transaction records every day. The file is stored in Amazon S3. Each transaction must be validated before updating a database. The company needs a solution that will process the data in parallel. The solution must use error handling that stops the entire process if more than 15% of the records fail validation.
Which solution will meet these requirements with the LEAST operational overhead?

A. Deploy an Amazon EMR cluster with Spark to process the file. Configure a custom failure threshold to
15%.
B. Use AWS Lambda with S3 Batch Operations to process the file and track validation failures to be less than 15%.
C. Create an AWS Batch job that processes chunks of the file in parallel with a custom error tracking mechanism.
D. Use AWS Step Functions Distributed Map state with the ToleratedFailurePercentage field set to 15%.

Answer: D

NEW QUESTION # 211
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