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New Data-Engineer-Associate Mock Exam | Test Data-Engineer-Associate Passing Score

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Amazon AWS Certified Data Engineer - Associate (DEA-C01) Sample Questions (Q34-Q39):

NEW QUESTION # 34
A financial company recently added more features to its mobile app. The new features required the company to create a new topic in an existing Amazon Managed Streaming for Apache Kafka (Amazon MSK) cluster.
A few days after the company added the new topic, Amazon CloudWatch raised an alarm on the RootDiskUsed metric for the MSK cluster.
How should the company address the CloudWatch alarm?

  • A. Update the MSK broker instance to a larger instance type. Restart the MSK cluster.
  • B. Expand the storage of the Apache ZooKeeper nodes.
  • C. Expand the storage of the MSK broker. Configure the MSK cluster storage to expand automatically.
  • D. Specify the Target-Volume-in-GiB parameter for the existing topic.

Answer: C

Explanation:
The RootDiskUsed metric for the MSK cluster indicates that the storage on the broker is reaching its capacity. The best solution is to expand the storage of the MSK broker and enable automatic storage expansion to prevent future alarms.
Expand MSK Broker Storage:
AWS Managed Streaming for Apache Kafka (MSK) allows you to expand the broker storage to accommodate growing data volumes. Additionally, auto-expansion of storage can be configured to ensure that storage grows automatically as the data increases.
Reference:
Alternatives Considered:
B (Expand Zookeeper storage): Zookeeper is responsible for managing Kafka metadata and not for storing data, so increasing Zookeeper storage won't resolve the root disk issue.
C (Update instance type): Changing the instance type would increase computational resources but not directly address the storage problem.
D (Target-Volume-in-GiB): This parameter is irrelevant for the existing topic and will not solve the storage issue.
Amazon MSK Storage Auto Scaling


NEW QUESTION # 35
A company has a frontend ReactJS website that uses Amazon API Gateway to invoke REST APIs. The APIs perform the functionality of the website. A data engineer needs to write a Python script that can be occasionally invoked through API Gateway. The code must return results to API Gateway.
Which solution will meet these requirements with the LEAST operational overhead?

  • A. Create an AWS Lambda function. Ensure that the function is warm by scheduling an Amazon EventBridge rule to invoke the Lambda function every 5 minutes by using mock events.
  • B. Create an AWS Lambda Python function with provisioned concurrency.
  • C. Deploy a custom Python script on an Amazon Elastic Container Service (Amazon ECS) cluster.
  • D. Deploy a custom Python script that can integrate with API Gateway on Amazon Elastic Kubernetes Service (Amazon EKS).

Answer: B

Explanation:
AWS Lambda is a serverless compute service that lets you run code without provisioning or managing servers. You can use Lambda to create functions that perform custom logic and integrate with other AWS services, such as API Gateway. Lambda automatically scales your application by running code in response to each trigger. You pay only for the compute time you consume1.
Amazon ECS is a fully managed container orchestration service that allows you to run and scale containerized applications on AWS. You can use ECS to deploy, manage, and scale Docker containers using either Amazon EC2 instances or AWS Fargate, a serverless compute engine for containers2.
Amazon EKS is a fully managed Kubernetes service that allows you to run Kubernetes clusters on AWS without needing to install, operate, or maintain your own Kubernetes control plane. You can use EKS to deploy, manage, and scale containerized applications using Kubernetes on AWS3.
The solution that meets the requirements with the least operational overhead is to create an AWS Lambda Python function with provisioned concurrency. This solution has the following advantages:
* It does not require you to provision, manage, or scale any servers or clusters, as Lambda handles all the infrastructure for you. This reduces the operational complexity and cost of running your code.
* It allows you to write your Python script as a Lambda function and integrate it with API Gateway using a simple configuration. API Gateway can invoke your Lambda function synchronously or asynchronously, and return the results to the frontend website.
* It ensures that your Lambda function is ready to respond to API requests without any cold start delays, by using provisioned concurrency. Provisioned concurrency is a feature that keeps your function initialized and hyper-ready to respond in double-digit milliseconds. You can specify the number of concurrent executions that you want to provision for your function.
Option A is incorrect because it requires you to deploy a custom Python script on an Amazon ECS cluster.
This solution has the following disadvantages:
* It requires you to provision, manage, and scale your own ECS cluster, either using EC2 instances or Fargate. This increases the operational complexity and cost of running your code.
* It requires you to package your Python script as a Docker container image and store it in a container registry, such as Amazon ECR or Docker Hub. This adds an extra step to your deployment process.
* It requires you to configure your ECS cluster to integrate with API Gateway, either using an Application Load Balancer or a Network Load Balancer. This adds another layer of complexity to your architecture.
Option C is incorrect because it requires you to deploy a custom Python script that can integrate with API Gateway on Amazon EKS. This solution has the following disadvantages:
* It requires you to provision, manage, and scale your own EKS cluster, either using EC2 instances or Fargate. This increases the operational complexity and cost of running your code.
* It requires you to package your Python script as a Docker container image and store it in a container registry, such as Amazon ECR or Docker Hub. This adds an extra step to your deployment process.
* It requires you to configure your EKS cluster to integrate with API Gateway, either using an Application Load Balancer, a Network Load Balancer, or a service of type LoadBalancer. This adds another layer of complexity to your architecture.
Option D is incorrect because it requires you to create an AWS Lambda function and ensure that the function is warm by scheduling an Amazon EventBridge rule to invoke the Lambda function every 5 minutes by using mock events. This solution has the following disadvantages:
* It does not guarantee that your Lambda function will always be warm, as Lambda may scale down your function if it does not receive any requests for a long period of time. This may cause cold start delays when your function is invoked by API Gateway.
* It incurs unnecessary costs, as you pay for the compute time of your Lambda function every time it is invoked by the EventBridge rule, even if it does not perform any useful work1.
References:
* 1: AWS Lambda - Features
* 2: Amazon Elastic Container Service - Features
* 3: Amazon Elastic Kubernetes Service - Features
* [4]: Building API Gateway REST API with Lambda integration - Amazon API Gateway
* [5]: Improving latency with Provisioned Concurrency - AWS Lambda
* [6]: Integrating Amazon ECS with Amazon API Gateway - Amazon Elastic Container Service
* [7]: Integrating Amazon EKS with Amazon API Gateway - Amazon Elastic Kubernetes Service
* [8]: Managing concurrency for a Lambda function - AWS Lambda


NEW QUESTION # 36
A company uses an Amazon QuickSight dashboard to monitor usage of one of the company's applications. The company uses AWS Glue jobs to process data for the dashboard. The company stores the data in a single Amazon S3 bucket. The company adds new data every day.
A data engineer discovers that dashboard queries are becoming slower over time. The data engineer determines that the root cause of the slowing queries is long-running AWS Glue jobs.
Which actions should the data engineer take to improve the performance of the AWS Glue jobs? (Choose two.)

  • A. Increase the AWS Glue instance size by scaling up the worker type.
  • B. Partition the data that is in the S3 bucket. Organize the data by year, month, and day.
  • C. Convert the AWS Glue schema to the DynamicFrame schema class.
  • D. Modify the 1AM role that grants access to AWS glue to grant access to all S3 features.
  • E. Adjust AWS Glue job scheduling frequency so the jobs run half as many times each day.

Answer: A,B

Explanation:
Partitioning the data in the S3 bucket can improve the performance of AWS Glue jobs by reducing the amount of data that needs to be scanned and processed. By organizing the data by year, month, and day, the AWS Glue job can use partition pruning to filter out irrelevant data and only read the data that matches the query criteria. This can speed up the data processing and reduce the cost of running the AWS Glue job. Increasing the AWS Glue instance size by scaling up the worker type can also improve the performance of AWS Glue jobs by providing more memory and CPU resources for the Spark execution engine. This can help the AWS Glue job handle larger data sets and complex transformations more efficiently. The other options are either incorrect or irrelevant, as they do not affect the performance of the AWS Glue jobs. Converting the AWS Glue schema to the DynamicFrame schema class does not improve the performance, but rather provides additional functionality and flexibility for data manipulation. Adjusting the AWS Glue job scheduling frequency does not improve the performance, but rather reduces the frequency of data updates. Modifying the IAM role that grants access to AWS Glue does not improve the performance, but rather affects the security and permissions of the AWS Glue service. Reference:
Optimising Glue Scripts for Efficient Data Processing: Part 1 (Section: Partitioning Data in S3) Best practices to optimize cost and performance for AWS Glue streaming ETL jobs (Section: Development tools) Monitoring with AWS Glue job run insights (Section: Requirements) AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide (Chapter 5, page 133)


NEW QUESTION # 37
A company uses an Amazon Redshift cluster that runs on RA3 nodes. The company wants to scale read and write capacity to meet demand. A data engineer needs to identify a solution that will turn on concurrency scaling.
Which solution will meet this requirement?

  • A. Turn on concurrency scaling at the workload management (WLM) queue level in the Redshift cluster.
  • B. Turn on concurrency scaling for the daily usage quota for the Redshift cluster.
  • C. Turn on concurrency scaling in the settings during the creation of and new Redshift cluster.
  • D. Turn on concurrency scaling in workload management (WLM) for Redshift Serverless workgroups.

Answer: A

Explanation:
Concurrency scaling is a feature that allows you to support thousands of concurrent users and queries, with consistently fast query performance. When you turn on concurrency scaling, Amazon Redshift automatically adds query processing power in seconds to process queries without any delays. You can manage which queries are sent to the concurrency-scaling cluster by configuring WLM queues. To turn on concurrency scaling for a queue, set the Concurrency Scaling mode value to auto. The other options are either incorrect or irrelevant, as they do not enable concurrency scaling for the existing Redshift cluster on RA3 nodes.
References:
* Working with concurrency scaling - Amazon Redshift
* Amazon Redshift Concurrency Scaling - Amazon Web Services
* Configuring concurrency scaling queues - Amazon Redshift
* AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide (Chapter 6, page 163)


NEW QUESTION # 38
A company is planning to use a provisioned Amazon EMR cluster that runs Apache Spark jobs to perform big data analysis. The company requires high reliability. A big data team must follow best practices for running cost-optimized and long-running workloads on Amazon EMR. The team must find a solution that will maintain the company's current level of performance.
Which combination of resources will meet these requirements MOST cost-effectively? (Choose two.)

  • A. Use x86-based instances for core nodes and task nodes.
  • B. Use Amazon S3 as a persistent data store.
  • C. Use Spot Instances for all primary nodes.
  • D. Use Graviton instances for core nodes and task nodes.
  • E. Use Hadoop Distributed File System (HDFS) as a persistent data store.

Answer: B,D

Explanation:
The best combination of resources to meet the requirements of high reliability, cost-optimization, and performance for running Apache Spark jobs on Amazon EMR is to use Amazon S3 as a persistent data store and Graviton instances for core nodes and task nodes.
Amazon S3 is a highly durable, scalable, and secure object storage service that can store any amount of data for a variety of use cases, including big data analytics1. Amazon S3 is a better choice than HDFS as a persistent data store for Amazon EMR, as it decouples the storage from the compute layer, allowing for more flexibility and cost-efficiency. Amazon S3 also supports data encryption, versioning, lifecycle management, and cross-region replication1. Amazon EMR integrates seamlessly with Amazon S3, using EMR File System (EMRFS) to access data stored in Amazon S3 buckets2. EMRFS also supports consistent view, which enables Amazon EMR to provide read-after-write consistency for Amazon S3 objects that are accessed through EMRFS2.
Graviton instances are powered by Arm-based AWS Graviton2 processors that deliver up to 40% better price performance over comparable current generation x86-based instances3. Graviton instances are ideal for running workloads that are CPU-bound, memory-bound, or network-bound, such as big data analytics, web servers, and open-source databases3. Graviton instances are compatible with Amazon EMR, and can be used for both core nodes and task nodes. Core nodes are responsible for running the data processing frameworks, such as Apache Spark, and storing data in HDFS or the local file system. Task nodes are optional nodes that can be added to a cluster to increase the processing power and throughput. By using Graviton instances for both core nodes and task nodes, you can achieve higher performance and lower cost than using x86-based instances.
Using Spot Instances for all primary nodes is not a good option, as it can compromise the reliability and availability of the cluster. Spot Instances are spare EC2 instances that are available at up to 90% discount compared to On-Demand prices, but they can be interrupted by EC2 with a two-minute notice when EC2 needs the capacity back. Primary nodes are the nodes that run the cluster software, such as Hadoop, Spark, Hive, and Hue, and are essential for the cluster operation. If a primary node is interrupted by EC2, the cluster will fail or become unstable. Therefore, it is recommended to use On-Demand Instances or Reserved Instances for primary nodes, and use Spot Instances only for task nodes that can tolerate interruptions.
References:
* Amazon S3 - Cloud Object Storage
* EMR File System (EMRFS)
* AWS Graviton2 Processor-Powered Amazon EC2 Instances
* [Plan and Configure EC2 Instances]
* [Amazon EC2 Spot Instances]
* [Best Practices for Amazon EMR]


NEW QUESTION # 39
......

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