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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Security and Governance | 18% | - Protect sensitive data - Enforce compliance and data governance
- Implement access control and authentication
|
| Topic 2: Data Store Management | 26% | - Manage data lifecycle and storage tiers - Design and implement data storage solutions
|
| Topic 3: Data Ingestion and Transformation | 34% | - Implement data quality and validation - Transform and enrich data
|
| Topic 4: Data Operations and Support | 22% | - Monitor and troubleshoot data pipelines
- Automate operational tasks - Ensure reliability and scalability |
>> Data-Engineer-Associate Exam Questions And Answers <<
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NEW QUESTION # 68
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?
Answer: D
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 # 69
A company uses Amazon Athena to run SQL queries for extract, transform, and load (ETL) tasks by using Create Table As Select (CTAS). The company must use Apache Spark instead of SQL to generate analytics.
Which solution will give the company the ability to use Spark to access Athena?
Answer: A
Explanation:
Athena data source is a solution that allows you to use Spark to access Athena by using the Athena JDBC driver and the Spark SQL interface. You can use the Athena data source to create Spark DataFrames from Athena tables, run SQL queries on the DataFrames, and write the results back to Athena. The Athena data source supports various data formats, such as CSV, JSON, ORC, and Parquet, and also supports partitioned and bucketed tables. The Athena data source is a cost-effective and scalable way to use Spark to access Athena, as it does not require any additional infrastructure or services, and you only pay for the data scanned by Athena.
The other options are not solutions that give the company the ability to use Spark to access Athena. Option A, Athena query settings, is a feature that allows you to configure various parameters for your Athena queries, such as the output location, the encryption settings, the query timeout, and the workgroup. Option B, Athena workgroup, is a feature that allows you to isolate and manage your Athena queries and resources, such as the query history, the query notifications, the query concurrency, and the query cost. Option D, Athena query editor, is a feature that allows you to write and run SQL queries on Athena using the web console or the API. None of these options enable you to use Spark instead of SQL to generate analytics on Athena. Reference:
Using Apache Spark in Amazon Athena
Athena JDBC Driver
Spark SQL
Athena query settings
[Athena workgroups]
[Athena query editor]
NEW QUESTION # 70
A data engineer needs to use AWS Step Functions to design an orchestration workflow. The workflow must parallel process a large collection of data files and apply a specific transformation to each file.
Which Step Functions state should the data engineer use to meet these requirements?
Answer: D
Explanation:
Option C is the correct answer because the Map state is designed to process a collection of data in parallel by applying the same transformation to each element. The Map state can invoke a nested workflow for each element, which can be another state machine ora Lambda function. The Map state will wait until all the parallel executions are completed before moving to the next state.
Option A is incorrect because the Parallel state is used to execute multiple branches of logic concurrently, not to process a collection of data. The Parallel state can have different branches with different logic and states, whereas the Map state has only one branch that is applied to each element of the collection.
Option B is incorrect because the Choice state is used to make decisions based on a comparison of a value to a set of rules. The Choice state does not process any data or invoke any nested workflows.
Option D is incorrect because the Wait state is used to delay the state machine from continuing for a specified time. The Wait state does not process any data or invoke any nested workflows.
References:
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide, Chapter 5: Data Orchestration, Section 5.3: AWS Step Functions, Pages 131-132 Building Batch Data Analytics Solutions on AWS, Module 5: Data Orchestration, Lesson 5.2: AWS Step Functions, Pages 9-10 AWS Documentation Overview, AWS Step Functions Developer Guide, Step Functions Concepts, State Types, Map State, Pages 1-3
NEW QUESTION # 71
A company has an application that uses an Amazon API Gateway REST API and an AWS Lambda function to retrieve data from an Amazon DynamoDB instance. Users recently reported intermittent high latency in the application's response times. A data engineer finds that the Lambda function experiences frequent throttling when the company's other Lambda functions experience increased invocations.
The company wants to ensure the API's Lambda function operates without being affected by other Lambda functions.
Which solution will meet this requirement MOST cost-effectively?
Answer: D
Explanation:
Reserved concurrencyallows you to dedicate a portion of your account's available concurrency to aspecific Lambda function. This ensures that the function always has sufficient capacity, regardless of how many other functions are running. It's more cost-effective than provisioned concurrency, which reserves warm environments at a higher cost.
"You can use reserved concurrency to guarantee that your critical Lambda functions have the capacity to run as needed without being throttled by other functions."
-Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf Provisioned concurrency would be overkill unless latency is also a concern for cold starts, which isn't stated in this case.
NEW QUESTION # 72
A global company currently uses Amazon Redshift to store data and Amazon Quick Suite (previously known as Amazon QuickSight) to generate reports.
A team of business analysts have varying levels of technical expertise. Some analysts lack SQL knowledge.
All the analysts need to create new reports frequently. The company wants to use natural program language queries to create dashboards and reports more efficiently.
Which solution will meet these requirements with the LEAST operational effort?
Answer: C
Explanation:
Option B is correct because Amazon Q in QuickSight is the AWS feature built specifically for natural language interaction with business data. AWS documentation explains that Amazon Q in QuickSight lets users ask questions in natural language, generate summaries and data stories, and create visuals from author prompts. AWS Prescriptive Guidance also notes that users can generate insights with natural language prompts and that they do not have to write SQL queries or learn complex BI tooling. That matches the requirement that some analysts lack SQL knowledge and need to create reports frequently with minimal effort.
Option A is not the best answer because zero-ETL access addresses data movement and integration, not natural-language dashboard creation. Option C introduces another analytics platform and therefore more operational effort. Option D provides access to data, but federated querying still does not address the key requirement for natural-language dashboard and report generation for nontechnical analysts. The AWS-native feature that directly solves this problem with the least additional management is Amazon Q in QuickSight.
NEW QUESTION # 73
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