DATA-ENGINEER-ASSOCIATE OFFICIAL CERT GUIDE | BOOKS DATA-ENGINEER-ASSOCIATE PDF

Data-Engineer-Associate Official Cert Guide | Books Data-Engineer-Associate PDF

Data-Engineer-Associate Official Cert Guide | Books Data-Engineer-Associate PDF

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Amazon AWS Certified Data Engineer - Associate (DEA-C01) Sample Questions (Q58-Q63):

NEW QUESTION # 58
A company has an application that uses a microservice architecture. The company hosts the application on an Amazon Elastic Kubernetes Services (Amazon EKS) cluster.
The company wants to set up a robust monitoring system for the application. The company needs to analyze the logs from the EKS cluster and the application. The company needs to correlate the cluster's logs with the application's traces to identify points of failure in the whole application request flow.
Which combination of steps will meet these requirements with the LEAST development effort? (Select TWO.)

  • A. Use Amazon CloudWatch to collect logs. Use Amazon Kinesis to collect traces.
  • B. Use AWS Glue to correlate the logs and traces.
  • C. Use Amazon OpenSearch to correlate the logs and traces.
  • D. Use Amazon CloudWatch to collect logs. Use Amazon Managed Streaming for Apache Kafka (Amazon MSK) to collect traces.
  • E. Use FluentBit to collect logs. Use OpenTelemetry to collect traces.

Answer: C,E

Explanation:
Step 1: Log Collection (FluentBit and CloudWatch)
Option A suggests using FluentBit to collect logs and OpenTelemetry to collect traces.
FluentBit is a lightweight log processor that integrates with Amazon EKS to collect and forward logs from Kubernetes clusters. It is widely used with minimal overhead, making it an ideal choice for log collection in this scenario. FluentBit is also natively compatible with AWS services.
OpenTelemetry is a popular framework to collect traces from distributed applications. It provides observability, making it easier to monitor microservices.
This combination allows you to effectively gather both logs and traces with minimal setup and configuration, aligning with the goal of least development effort.
CloudWatch can be used to monitor logs (Option B and C). However, for applications that need more custom and fine-grained control over logging mechanisms, FluentBit and OpenTelemetry are the preferred choice in microservice environments.
Step 2: Log and Trace Correlation (Amazon OpenSearch)
Option D (Amazon OpenSearch) is specifically designed to search, analyze, and visualize logs, metrics, and traces in real-time. OpenSearch allows you to correlate logs and traces effectively.
With Amazon OpenSearch, you can set up dashboards that help in visualizing both logs and traces together, which assists in identifying any failure points across the entire request flow.
It offers integrations with FluentBit and OpenTelemetry, ensuring that both logs from the EKS cluster and application traces are centrally collected, stored, and correlated without additional heavy development.
Step 3: Why Other Options Are Not Suitable
Option B (Amazon Kinesis) is designed for real-time data streaming and analytics but is not as well-suited for tracing microservice requests and logs correlation compared to OpenSearch.
Option C (Amazon MSK) provides a managed Kafka streaming service, but this adds complexity when trying to integrate and correlate logs and traces from a microservice environment. Setting up Kafka requires more development effort compared to using FluentBit and OpenTelemetry.
Option E (AWS Glue) is primarily an ETL (Extract, Transform, Load) service. While Glue is powerful for data processing, it is not a native tool for log and trace correlation, and using it would add unnecessary complexity for this use case.
Conclusion:
To meet the requirements with the least development effort:
Use FluentBit for log collection and OpenTelemetry for tracing (Option A).
Correlate logs and traces using Amazon OpenSearch (Option D).
This approach leverages AWS-native services designed for seamless integration with microservices hosted on Amazon EKS and ensures effective monitoring with minimal overhead.


NEW QUESTION # 59
A company stores server logs in an Amazon 53 bucket. The company needs to keep the logs for 1 year. The logs are not required after 1 year.
A data engineer needs a solution to automatically delete logs that are older than 1 year.
Which solution will meet these requirements with the LEAST operational overhead?

  • A. Create an AWS Lambda function to delete the logs after 1 year.
  • B. Schedule a cron job on an Amazon EC2 instance to delete the logs after 1 year.
  • C. Define an S3 Lifecycle configuration to delete the logs after 1 year.
  • D. Configure an AWS Step Functions state machine to delete the logs after 1 year.

Answer: A

Explanation:
* Problem Analysis:
* The company usesAWS Gluefor ETL pipelines and requires automaticdata quality checks during pipeline execution.
* The solution must integrate with existing AWS Glue pipelines and evaluatedata quality rules based on predefined thresholds.
* Key Considerations:
* Ensure minimal implementation effort by leveraging built-in AWS Glue features.
* Use a standardized approach for defining and evaluating data quality rules.
* Avoid custom libraries or external frameworks unless absolutely necessary.
* Solution Analysis:
* Option A: SQL Transform
* Adding SQL transforms to define and evaluate data quality rules is possible but requires writing complex queries for each rule.
* Increases operational overhead and deviates from Glue's declarative approach.
* Option B: Evaluate Data Quality Transform with DQDL
* AWS Glue provides a built-inEvaluate Data Quality transform.
* Allows defining rules inData Quality Definition Language (DQDL), a concise and declarative way to define quality checks.
* Fully integrated with Glue Studio, making it the least effort solution.
* Option C: Custom Transform with PyDeequ
* PyDeequ is a powerful library for data quality checks but requires custom code and integration.
* Increases implementation effort compared to Glue's native capabilities.
* Option D: Custom Transform with Great Expectations
* Great Expectations is another powerful library for data quality but adds complexity and external dependencies.
* Final Recommendation:
* UseEvaluate Data Quality transformin AWS Glue.
* Define rules inDQDLfor checking thresholds, null values, or other quality criteria.
* This approach minimizes development effort and ensures seamless integration with AWS Glue.
:
AWS Glue Data Quality Overview
DQDL Syntax and Examples
Glue Studio Transformations


NEW QUESTION # 60
A company stores its processed data in an S3 bucket. The company has a strict data access policy. The company uses IAM roles to grant teams within the company different levels of access to the S3 bucket.
The company wants to receive notifications when a user violates the data access policy. Each notification must include the username of the user who violated the policy.
Which solution will meet these requirements?

  • A. Use Amazon S3 server access logs to monitor access to the bucket. Forward the access logs to an Amazon CloudWatch log group. Use metric filters on the log group to set up CloudWatch alarms.
  • B. Use Amazon CloudWatch metrics to gather object-level metrics. Set up CloudWatch alarms.
  • C. Use AWS Config rules to detect violations of the data access policy. Set up compliance alarms.
  • D. Use AWS CloudTrail to track object-level events for the S3 bucket. Forward events to Amazon CloudWatch to set up CloudWatch alarms.

Answer: D

Explanation:
The requirement is to detect violations of data access policies and receive notifications with the username of the violator. AWS CloudTrail can provide object-level tracking for S3 to capture detailed API actions on specific S3 objects, including the user who performed the action.
AWS CloudTrail:
CloudTrail can monitor API calls made to an S3 bucket, including object-level API actions such as GetObject, PutObject, and DeleteObject. This will help detect access violations based on the API calls made by different users.
CloudTrail logs include details such as the user identity, which is essential for meeting the requirement of including the username in notifications.
The CloudTrail logs can be forwarded to Amazon CloudWatch to trigger alarms based on certain access patterns (e.g., violations of specific policies).
Reference:
Amazon CloudWatch:
By forwarding CloudTrail logs to CloudWatch, you can set up alarms that are triggered when a specific condition is met, such as unauthorized access or policy violations. The alarm can include detailed information from the CloudTrail log, including the username.
Alternatives Considered:
A (AWS Config rules): While AWS Config can track resource configurations and compliance, it does not provide real-time, detailed tracking of object-level events like CloudTrail does.
B (CloudWatch metrics): CloudWatch does not gather object-level metrics for S3 directly. For this use case, CloudTrail provides better granularity.
D (S3 server access logs): S3 server access logs can monitor access, but they do not provide the real-time monitoring and alerting features that CloudTrail with CloudWatch alarms offer. They also do not include API-level granularity like CloudTrail.
AWS CloudTrail Integration with S3
Amazon CloudWatch Alarms


NEW QUESTION # 61
A company uses an on-premises Microsoft SQL Server database to store financial transaction data. The company migrates the transaction data from the on-premises database to AWS at the end of each month. The company has noticed that the cost to migrate data from the on-premises database to an Amazon RDS for SQL Server database has increased recently.
The company requires a cost-effective solution to migrate the data to AWS. The solution must cause minimal downtown for the applications that access the database.
Which AWS service should the company use to meet these requirements?

  • A. AWS DataSync
  • B. AWS Lambda
  • C. AWS Database Migration Service (AWS DMS)
  • D. AWS Direct Connect

Answer: C

Explanation:
AWS Database Migration Service (AWS DMS) is a cloud service that makes it possible to migrate relational databases, data warehouses, NoSQL databases, and other types of data stores to AWS quickly, securely, and with minimal downtime and zero data loss1. AWS DMS supports migration between 20-plus database and analytics engines, such as Microsoft SQL Server to Amazon RDS for SQL Server2. AWS DMS takes overmany of the difficult or tedious tasks involved in a migration project, such as capacity analysis, hardware and software procurement, installation and administration, testing and debugging, and ongoing replication and monitoring1. AWS DMS is a cost-effective solution, as you only pay for the compute resources and additional log storage used during the migration process2. AWS DMS is the best solution for the company to migrate the financial transaction data from the on-premises Microsoft SQL Server database to AWS, as it meets the requirements of minimal downtime, zero data loss, and low cost.
Option A is not the best solution, as AWS Lambda is a serverless compute service that lets you run code without provisioning or managing servers, but it does not provide any built-in features for database migration.
You would have to write your own code to extract, transform, and load the data from the source to the target, which would increase the operational overhead and complexity.
Option C is not the best solution, as AWS Direct Connect is a service that establishes a dedicated network connection from your premises to AWS, but it does not provide any built-in features for database migration.
You would still need to use another service or tool to perform the actual data transfer, which would increase the cost and complexity.
Option D is not the best solution, as AWS DataSync is a service that makes it easy to transfer data between on-premises storage systems and AWS storage services, such as Amazon S3, Amazon EFS, and Amazon FSx for Windows File Server, but it does not support Amazon RDS for SQL Server as a target. You would have to use another service or tool to migrate the data from Amazon S3 to Amazon RDS for SQL Server, which would increase the latency and complexity. References:
Database Migration - AWS Database Migration Service - AWS
What is AWS Database Migration Service?
AWS Database Migration Service Documentation
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide


NEW QUESTION # 62
A company needs a solution to manage costs for an existing Amazon DynamoDB table. The company also needs to control the size of the table. The solution must not disrupt any ongoing read or write operations. The company wants to use a solution that automatically deletes data from the table after 1 month.
Which solution will meet these requirements with the LEAST ongoing maintenance?

  • A. Use an AWS Lambda function to periodically scan the DynamoDB table for data that is older than 1 month. Configure the Lambda function to delete old data.
  • B. Configure a scheduled Amazon EventBridge rule to invoke an AWS Lambda function to check for data that is older than 1 month. Configure the Lambda function to delete old data.
  • C. Configure a stream on the DynamoDB table to invoke an AWS Lambda function. Configure the Lambda function to delete data in the table that is older than 1 month.
  • D. Use the DynamoDB TTL feature to automatically expire data based on timestamps.

Answer: D

Explanation:
The requirement is to manage the size of an Amazon DynamoDB table by automatically deleting data older than 1 month without disrupting ongoing read or write operations. The simplest and most maintenance-free solution is to use DynamoDB Time-to-Live (TTL).
Option A: Use the DynamoDB TTL feature to automatically expire data based on timestamps.
DynamoDB TTL allows you to specify an attribute (e.g., a timestamp) that defines when items in the table should expire. After the expiration time, DynamoDB automatically deletes the items, freeing up storage space and keeping the table size under control without manual intervention or disruptions to ongoing operations.
Other options involve higher maintenance and manual scheduling or scanning operations, which increase complexity unnecessarily compared to the native TTL feature.
Reference:
DynamoDB Time-to-Live (TTL)


NEW QUESTION # 63
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