DATA-ENGINEER-ASSOCIATE CERTIFICATION EXAM QUESTIONS IN 3 USER-FRIENDLY FORMATS

Data-Engineer-Associate Certification Exam Questions in 3 User-Friendly Formats

Data-Engineer-Associate Certification Exam Questions in 3 User-Friendly Formats

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Amazon AWS Certified Data Engineer - Associate (DEA-C01) Sample Questions (Q108-Q113):

NEW QUESTION # 108
A company currently uses a provisioned Amazon EMR cluster that includes general purpose Amazon EC2 instances. The EMR cluster uses EMR managed scaling betweenone to five task nodes for the company's long- running Apache Spark extract, transform, and load (ETL) job. The company runs the ETL job every day.
When the company runs the ETL job, the EMR cluster quickly scales up to five nodes. The EMR cluster often reaches maximum CPU usage, but the memory usage remains under 30%.
The company wants to modify the EMR cluster configuration to reduce the EMR costs to run the daily ETL job.
Which solution will meet these requirements MOST cost-effectively?

  • A. Switch the task node type from general purpose EC2 instances to compute optimized EC2 instances.
  • B. Increase the maximum number of task nodes for EMR managed scaling to 10.
  • C. Change the task node type from general purpose EC2 instances to memory optimized EC2 instances.
  • D. Reduce the scaling cooldown period for the provisioned EMR cluster.

Answer: A

Explanation:
The company's Apache Spark ETL job on Amazon EMR uses high CPU but low memory, meaning that compute-optimized EC2 instanceswould be the most cost-effective choice. These instances are designed for high-performance compute applications, where CPU usage is high, but memory needs are minimal, which is exactly the case here.
* Compute Optimized Instances:
* Compute-optimized instances, such as the C5 series, provide a higher ratio of CPU to memory, which is more suitable for jobs with high CPU usage and relatively low memory consumption.
* Switching from general-purpose EC2 instances to compute-optimized instances canreduce costs while improving performance, as these instances are optimized for workloads like Spark jobs that perform a lot of computation.
Reference:Amazon EC2 Compute Optimized Instances
Managed Scaling: The EMR cluster's scaling is currently managed between 1 and 5 nodes, so changing the instance type will leverage the current scaling strategy but optimize it for the workload.
Alternatives Considered:
A (Increase task nodes to 10): Increasing the number of task nodes would increase costs without necessarily improving performance. Since memory usageis low, the bottleneck is more likely the CPU, which compute- optimized instances can handle better.
B (Memory optimized instances): Memory-optimized instances are not suitable since the current job is CPU- bound, and memory usage remains low (under 30%).
D (Reduce scaling cooldown): This could marginally improve scaling speed but does not address the need for cost optimization and improved CPU performance.
References:
Amazon EMR Cluster Optimization
Compute Optimized EC2 Instances


NEW QUESTION # 109
A data engineer is using Amazon Athena to analyze sales data that is in Amazon S3. The data engineer writes a query to retrieve sales amounts for 2023 for several products from a table named sales_data. However, the query does not return results for all of the products that are in the sales_data table. The data engineer needs to troubleshoot the query to resolve the issue.
The data engineer's original query is as follows:
SELECT product_name, sum(sales_amount)
FROM sales_data
WHERE year = 2023
GROUP BY product_name
How should the data engineer modify the Athena query to meet these requirements?

  • A. Remove the GROUP BY clause
  • B. Add HAVING sumfsales amount) > 0 after the GROUP BY clause.
  • C. Replace sum(sales amount) with count(*J for the aggregation.
  • D. Change WHERE year = 2023 to WHERE extractlyear FROM sales data) = 2023.

Answer: D

Explanation:
The original query does not return results for all of the products because the year column in the sales_data table is not an integer, but a timestamp. Therefore, the WHERE clause does not filter the data correctly, and only returns the products that have a null value for the year column. To fix this, the data engineer should use the extract function to extract the year from the timestamp and compare it with 2023. This way, the query will return the correct results for all of the products in the sales_data table. The other options are either incorrect or irrelevant, as they do not address the root cause of the issue. Replacing sum with count does not change the filtering condition, adding HAVING clause does not affect the grouping logic, and removing the GROUP BY clause does not solve the problem of missing products. References:
* Troubleshooting JSON queries - Amazon Athena (Section: JSON related errors)
* When I query a table in Amazon Athena, the TIMESTAMP result is empty (Section: Resolution)
* AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide (Chapter 7, page 197)


NEW QUESTION # 110
An ecommerce company wants to use AWS to migrate data pipelines from an on-premises environment into the AWS Cloud. The company currently uses a third-party too in the on-premises environment to orchestrate data ingestion processes.
The company wants a migration solution that does not require the company to manage servers. The solution must be able to orchestrate Python and Bash scripts. The solution must not require the company to refactor any code.
Which solution will meet these requirements with the LEAST operational overhead?

  • A. Amazon Managed Workflows for Apache Airflow (Amazon MWAA)
  • B. AWS Lambda
  • C. AWS Glue
  • D. AWS Step Functions

Answer: A

Explanation:
The ecommerce company wants to migrate its data pipelines into the AWS Cloud without managing servers, and the solution must orchestrate Python and Bash scripts without refactoring code. Amazon Managed Workflows for Apache Airflow (Amazon MWAA) is the most suitable solution for this scenario.
* Option B: Amazon Managed Workflows for Apache Airflow (Amazon MWAA)MWAA is a managed orchestration service that supports Python and Bash scripts via Directed Acyclic Graphs (DAGs) for workflows. It is a serverless, managed version of Apache Airflow, which is commonly used for orchestrating complex data workflows, making it an ideal choice for migrating existing pipelines without refactoring. It supports Python, Bash, and other scripting languages, and the company would not need to manage the underlying infrastructure.
Other options:
* AWS Lambda (Option A) is more suited for event-driven workflows but would require breaking down the pipeline into individual Lambda functions, which may require refactoring.
* AWS Step Functions (Option C) is good for orchestration but lacks native support for Python and Bash without using Lambda functions, and it may require code changes.
* AWS Glue (Option D) is an ETL service primarily for data transformation and not suitable for orchestrating general scripts without modification.
References:
* Amazon Managed Workflows for Apache Airflow (MWAA) Documentation


NEW QUESTION # 111
A company is building a data stream processing application. The application runs in an Amazon Elastic Kubernetes Service (Amazon EKS) cluster. The application stores processed data in an Amazon DynamoDB table.
The company needs the application containers in the EKS cluster to have secure access to the DynamoDB table. The company does not want to embed AWS credentials in the containers.
Which solution will meet these requirements?

  • A. Create an IAM user that has an access key to access the DynamoDB table. Use environment variables in the EKS containers to store the IAM user access key data.
  • B. Create an IAM user that has an access key to access the DynamoDB table. Use Kubernetes secrets that are mounted in a volume of the EKS cluster nodes to store the user access key data.
  • C. Store the AWS credentials in an Amazon S3 bucket. Grant the EKS containers access to the S3 bucket to retrieve the credentials.
  • D. Attach an IAM role to the EKS worker nodes. Grant the IAM role access to DynamoDB. Use the IAM role to set up IAM roles service accounts (IRSA) functionality.

Answer: D

Explanation:
In this scenario, the company is using Amazon Elastic Kubernetes Service (EKS) and wants secure access to DynamoDB without embedding credentials inside the application containers. The best practice is to use IAM roles for service accounts (IRSA), which allows assigning IAM roles to Kubernetes service accounts. This lets the EKS pods assume specific IAM roles securely, without the need to store credentials in containers.
* IAM Roles for Service Accounts (IRSA):
* With IRSA, each pod in the EKS cluster can assume an IAM role that grants access to DynamoDB without needing to manage long-term credentials. The IAM role can be attached to the service account associated with the pod.
* This ensures least privilege access, improving security by preventing credentials from being embedded in the containers.


NEW QUESTION # 112
A data engineer uses Amazon Managed Workflows for Apache Airflow (Amazon MWAA) to run data pipelines in an AWS account. A workflow recently failed to run. The data engineer needs to use Apache Airflow logs to diagnose the failure of the workflow. Which log type should the data engineer use to diagnose the cause of the failure?

  • A. YourEnvironmentName-Scheduler
  • B. YourEnvironmentName-WebServer
  • C. YourEnvironmentName-DAGProcessing
  • D. YourEnvironmentName-Task

Answer: D

Explanation:
In Amazon Managed Workflows for Apache Airflow (MWAA), the type of log that is most useful for diagnosing workflow (DAG) failures is the Task logs. These logs provide detailed information on the execution of each task within the DAG, including error messages, exceptions, and other critical details necessary for diagnosing failures.
* Option D: YourEnvironmentName-TaskTask logs capture the output from the execution of each task within a workflow (DAG), which is crucial for understanding what went wrong when a DAG fails.
These logs contain detailed execution information, including errors and stack traces, making them the best source for debugging.
Other options (WebServer, Scheduler, and DAGProcessing logs) provide general environment-level logs or logs related to scheduling and DAG parsing, but they do not provide the granular task-level execution details needed for diagnosing workflow failures.
References:
* Amazon MWAA Logging and Monitoring
* Apache Airflow Task Logs


NEW QUESTION # 113
......

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