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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Security and Governance | 18% | - Implement data security controls - Apply governance and compliance best practices |
| Topic 2: Data Ingestion and Transformation | 34% | - Ingest and transform data using AWS services - Build and manage data pipelines |
| Topic 3: Data Operations and Support | 22% | - Troubleshoot data workflow issues - Monitor and maintain data pipelines |
| Topic 4: Data Store Management | 26% | - Optimize storage performance and cost - Select appropriate data storage solutions |
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NEW QUESTION # 104
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: A
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.
Reference:
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 # 105
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: A
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 or a 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.
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 # 106
A company has a data lake in Amazon S3. The company collects AWS CloudTrail logs for multiple applications. The company stores the logs in the data lake, catalogs the logs in AWS Glue, and partitions the logs based on the year. The company uses Amazon Athena to analyze the logs.
Recently, customers reported that a query on one of the Athena tables did not return any dat a. A data engineer must resolve the issue.
Which combination of troubleshooting steps should the data engineer take? (Select TWO.)
Answer: B,D
Explanation:
The problem likely arises from Athena not being able to read from the correct S3 location or missing partitions. The two most relevant troubleshooting steps involve checking the S3 location and repairing the table metadata.
A . Confirm that Athena is pointing to the correct Amazon S3 location:
One of the most common issues with missing data in Athena queries is that the query is pointed to an incorrect or outdated S3 location. Checking the S3 path ensures Athena is querying the correct data.
Reference:
C . Use the MSCK REPAIR TABLE command:
When new partitions are added to the S3 bucket without being reflected in the Glue Data Catalog, Athena queries will not return data from those partitions. The MSCK REPAIR TABLE command updates the Glue Data Catalog with the latest partitions.
Alternatives Considered:
B (Increase query timeout): Timeout issues are unrelated to missing data.
D (Restart Athena): Athena does not require restarting.
E (Delete and recreate table): This introduces unnecessary overhead when the issue can be resolved by repairing the table and confirming the S3 location.
Athena Query Fails to Return Data
NEW QUESTION # 107
A data engineer configures a large number of AWS Glue jobs that all start up around the same time. All the jobs run for less than 1 hour in the same subnet of the same VPC. All the AWS Glue jobs run on a G.1X worker type.
Some of the jobs occasionally fail with the following error: "The specified subnet does not have enough free addresses to satisfy the request." What is the likely root cause of the error?
Answer: D
Explanation:
Option A is correct because when many AWS Glue jobs start at the same time in the same subnet, Glue needs to create network interfaces in that subnet. If the subnet does not have enough free private IP addresses available, jobs can fail with exactly this type of error. AWS service documentation for VPC-based managed data services consistently notes that if there is no available free IP address in a specified subnet, the service cannot create or add the required ENIs and the workload can fail or degrade.
This is a subnet-level exhaustion issue, not a VPC-wide issue. A VPC can still have free addresses in other subnets while the specific subnet chosen for the jobs is out of usable addresses. That makes D incorrect.
Option B is incorrect because G.1X is a valid Glue worker type and does not inherently prevent subnet access.
Option C is also incorrect because the error is about address availability, not IAM authorization. In practice, this problem is usually resolved by using a larger subnet or spreading workloads across additional subnets so enough IPs are available when multiple Glue jobs launch concurrently. This aligns with the exam's focus on networking capacity planning for managed data services.
NEW QUESTION # 108
A company uses Amazon Redshift as its data warehouse. Data encoding is applied to the existing tables of the data warehouse. A data engineer discovers that the compression encoding applied to some of the tables is not the best fit for the data.
The data engineer needs to improve the data encoding for the tables that have sub-optimal encoding.
Which solution will meet this requirement?
Answer: A
Explanation:
To improve data encoding for Amazon Redshift tables where sub-optimal encoding has been applied, the correct approach is to analyze the table to determine the optimal encoding based on the data distribution and characteristics.
* Option B: Run the ANALYZE COMPRESSION command against the identified tables.
Manually update the compression encoding of columns based on the output of the command.The ANALYZE COMPRESSION command in Amazon Redshift analyzes the columnar data and suggests the best compression encoding for each column. The output provides recommendations for changing the current encoding to improve storage efficiency and query performance. After analyzing, you can manually apply the recommended encoding to the columns.
* Option A (ANALYZE command) is incorrect because it is primarily used to update statistics on tables, not to analyze or suggest compression encoding.
* Options C and D (VACUUM commands) deal with reclaiming disk space and reorganizing data, not optimizing compression encoding.
References:
* Amazon Redshift ANALYZE COMPRESSION Command
NEW QUESTION # 109
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