[Amazon] DEA-C01 - Data Engineer Associate Exam Dumps & Study Guide
# Complete Study Guide for the AWS Certified Data Engineer - Associate (DEA-C01) Exam
The AWS Certified Data Engineer - Associate (DEA-C01) is a mid-level certification designed to validate your proficiency in implementing data pipelines, managing data lifecycle, and ensuring data quality across the Amazon Web Services (AWS) ecosystem. As data continues to grow in importance, this certification is increasingly sought after by data engineers, data scientists, and solutions architects.
## Why Pursue the AWS Data Engineer Associate Certification?
Earning the DEA-C01 badge demonstrates that you:
- Understand core AWS data services and their common use cases.
- Can design and implement data architectures that meet specific requirements.
- Understand the data lifecycle and how to manage data at scale.
- Can ensure data quality, security, and compliance across the entire data pipeline.
## Exam Overview
The DEA-C01 exam consists of 65 multiple-choice and multiple-response questions. You are given 130 minutes to complete the exam, and the passing score is 720 out of 1000.
### Key Domains Covered:
1. **Data Ingestion and Transformation (34%):** This is the largest domain. It covers your ability to ingest data from various sources (streaming and batch) and transform it using services like AWS Glue, AWS Lambda, and Amazon EMR. You'll need to understand data formats (Parquet, Avro, JSON) and how to handle schema changes.
2. **Data Store Management (26%):** This domain focuses on your knowledge of AWS data storage services. You must be able to choose the right data store for a given use case, whether it's Amazon S3, Amazon Redshift, Amazon DynamoDB, or Amazon Aurora. Understanding data partitioning, indexing, and storage optimization is crucial.
3. **Data Operations and Support (22%):** This section covers the ongoing maintenance and monitoring of your data pipelines. You’ll need to be proficient with AWS CloudWatch, AWS CloudTrail, and AWS Glue DataBrew to troubleshoot issues and ensure data quality.
4. **Data Security and Governance (18%):** Security is a top priority in AWS. This domain tests your knowledge of AWS IAM (Identity and Access Management), AWS KMS (Key Management Service), and AWS Lake Formation. You'll need to understand how to implement data encryption, access controls, and data masking.
## Top Resources for DEA-C01 Preparation
Successfully passing the DEA-C01 requires a mix of theoretical knowledge and hands-on experience. Here are some of the best resources:
- **AWS Certified Data Engineer - Associate Learning Plan:** This is a structured path provided by AWS that covers all the core concepts.
- **AWS Whitepapers and Documentation:** Focus on the "AWS Data Analytics" and "AWS Data Lake" whitepapers.
- **Hands-on Practice:** There is no substitute for building. Set up complex data pipelines, configure AWS Glue jobs, and experiment with Amazon Redshift clusters.
- **Practice Exams:** Taking high-quality practice tests is crucial for understanding the exam format and identifying knowledge gaps. Many successful candidates recommend using resources like [notjustexam.com](https://notjustexam.com) for their realistic exam simulations and detailed explanations.
## Critical Topics to Master
To excel in the DEA-C01, you should focus your studies on these high-impact areas:
- **AWS Glue:** Understand how to use AWS Glue for data cataloging, ETL (Extract, Transform, Load) jobs, and data quality checks.
- **Amazon Redshift:** Master the nuances of Redshift architecture, including distribution keys, sort keys, and Redshift Spectrum.
- **AWS Lake Formation:** Understand how to simplify the process of setting up a secure data lake and managing access permissions.
- **Amazon S3:** Know the different storage classes, bucket policies, and how to optimize S3 for data analytics.
- **Data Security:** Deep dive into IAM roles, KMS encryption, and how to implement fine-grained access control.
## Exam Day Strategy
1. **Pace Yourself:** With 130 minutes for 65 questions, you have about 2 minutes per question. If a question is too difficult, flag it and move on.
2. **Read Carefully:** Pay attention to keywords like "most cost-effective," "least operational overhead," or "highest availability." These often dictate the correct answer among several technically feasible options.
3. **Use the Process of Elimination:** If you aren't sure of the right choice, eliminating obviously incorrect options significantly increases your chances.
## Conclusion
The AWS Certified Data Engineer - Associate (DEA-C01) is a valuable credential that validates your skills in the rapidly growing field of data engineering. By following a structured study plan, using high-quality practice exams from [notjustexam.com](https://notjustexam.com), and gaining hands-on experience, you can master the complexities of AWS data services and join the ranks of certified data engineers.
Free [Amazon] DEA-C01 - Data Engineer Associate Practice Questions Preview
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Question 1
A data engineer is configuring an AWS Glue job to read data from an Amazon S3 bucket. The data engineer has set up the necessary AWS Glue connection details and an associated IAM role. However, when the data engineer attempts to run the AWS Glue job, the data engineer receives an error message that indicates that there are problems with the Amazon S3 VPC gateway endpoint.
The data engineer must resolve the error and connect the AWS Glue job to the S3 bucket.
Which solution will meet this requirement?
- A. Update the AWS Glue security group to allow inbound traffic from the Amazon S3 VPC gateway endpoint.
- B. Configure an S3 bucket policy to explicitly grant the AWS Glue job permissions to access the S3 bucket.
- C. Review the AWS Glue job code to ensure that the AWS Glue connection details include a fully qualified domain name.
- D. Verify that the VPC's route table includes inbound and outbound routes for the Amazon S3 VPC gateway endpoint.
Correct Answer:
D
Explanation:
The best solution is D. Verify that the VPC's route table includes inbound and outbound routes for the Amazon S3 VPC gateway endpoint.
Reasoning:
AWS Glue jobs running within a VPC need a route to access Amazon S3 through the VPC endpoint. If the route table associated with the subnet where the Glue job runs does not have the necessary routes to the S3 VPC endpoint, the Glue job will fail to connect to S3. Verifying and configuring the route table to include routes for the S3 VPC endpoint resolves this connectivity issue. The key to this issue is the error stating "...problems with the Amazon S3 VPC gateway endpoint." This is a strong indicator that the route table configuration is the root cause.
Reasons for not choosing the other options:
- A is incorrect: AWS Glue is a managed service, and you do not directly manage security groups for it in the same way you would for EC2 instances. While Glue uses security groups in the background, the issue described is related to VPC routing, not security group rules.
- B is incorrect: While S3 bucket policies are essential for controlling access to S3 buckets, the error message specifically mentions problems with the VPC gateway endpoint. This suggests that the issue lies within the network configuration, not with the permissions on the S3 bucket itself. IAM role is already configured.
- C is incorrect: While connection details are important, the error message focuses on the VPC gateway endpoint. This implies a network connectivity issue rather than a problem with the connection string or other details.
This approach ensures that the Glue job can communicate with the S3 bucket through the VPC endpoint, resolving the stated error.
- AWS PrivateLink, https://aws.amazon.com/privatelink/
- AWS Glue Network Options, https://docs.aws.amazon.com/glue/latest/dg/vpc-endpoints.html
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Question 2
A retail company has a customer data hub in an Amazon S3 bucket. Employees from many countries use the data hub to support company-wide analytics. A governance team must ensure that the company's data analysts can access data only for customers who are within the same country as the analysts.
Which solution will meet these requirements with the LEAST operational effort?
- A. Create a separate table for each country's customer data. Provide access to each analyst based on the country that the analyst serves.
- B. Register the S3 bucket as a data lake location in AWS Lake Formation. Use the Lake Formation row-level security features to enforce the company's access policies.
- C. Move the data to AWS Regions that are close to the countries where the customers are. Provide access to each analyst based on the country that the analyst serves.
- D. Load the data into Amazon Redshift. Create a view for each country. Create separate IAM roles for each country to provide access to data from each country. Assign the appropriate roles to the analysts.
Correct Answer:
B
Explanation:
The best answer is B: Register the S3 bucket as a data lake location in AWS Lake Formation. Use the Lake Formation row-level security features to enforce the company's access policies.
Reasoning:
AWS Lake Formation simplifies the process of setting up, securing, and managing data lakes. The key advantage of Lake Formation in this scenario is its row-level security feature, which allows fine-grained access control based on attributes within the data itself (in this case, the customer's country). This meets the requirement of allowing analysts to access only data for customers within their country with the least operational overhead.
Reasons for not choosing the other options:
- A: Create a separate table for each country's customer data. Provide access to each analyst based on the country that the analyst serves. This approach involves significantly more operational effort because it requires creating and managing multiple tables, setting up individual access controls for each table, and handling data partitioning.
- C: Move the data to AWS Regions that are close to the countries where the customers are. Provide access to each analyst based on the country that the analyst serves. Moving data to different regions adds complexity and cost and doesn't directly address the row-level security requirement. This option increases operational effort and introduces latency issues.
- D: Load the data into Amazon Redshift. Create a view for each country. Create separate IAM roles for each country to provide access to data from each country. Assign the appropriate roles to the analysts. Loading data into Redshift and creating separate views adds complexity compared to using Lake Formation, which is designed for managing data lake security. This also involves more operational overhead in terms of managing Redshift clusters, views, and IAM roles.
Because the question emphasizes "LEAST operational effort", Lake Formation provides the most streamlined and efficient solution.
- AWS Lake Formation, https://aws.amazon.com/lake-formation/
- AWS Lake Formation Row-Level Security, https://docs.aws.amazon.com/lake-formation/latest/dg/security-row-column.html
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Question 3
A media company wants to improve a system that recommends media content to customer based on user behavior and preferences. To improve the recommendation system, the company needs to incorporate insights from third-party datasets into the company's existing analytics platform.
The company wants to minimize the effort and time required to incorporate third-party datasets.
Which solution will meet these requirements with the LEAST operational overhead?
- A. Use API calls to access and integrate third-party datasets from AWS Data Exchange.
- B. Use API calls to access and integrate third-party datasets from AWS DataSync.
- C. Use Amazon Kinesis Data Streams to access and integrate third-party datasets from AWS CodeCommit repositories.
- D. Use Amazon Kinesis Data Streams to access and integrate third-party datasets from Amazon Elastic Container Registry (Amazon ECR).
Correct Answer:
A
Explanation:
The recommended answer is A: Use API calls to access and integrate third-party datasets from AWS Data Exchange.
Reasoning:
The question emphasizes minimizing effort, time, and operational overhead when incorporating third-party datasets. AWS Data Exchange is specifically designed for this purpose. It allows direct access to third-party data via API calls, which simplifies the integration process.
Here's a detailed breakdown:
- AWS Data Exchange: Provides a straightforward way to find, subscribe to, and use third-party data in the cloud. The API access minimizes the need for complex data pipelines or transformations, thus reducing operational overhead.
Reasons for not choosing the other options:
- B: Use API calls to access and integrate third-party datasets from AWS DataSync. AWS DataSync is used for data transfer between on-premises storage and AWS, or between AWS storage services. It is not designed for accessing third-party datasets from a marketplace or exchange. Therefore, it's not the right tool for this scenario, and will create unnecessary overhead.
- C: Use Amazon Kinesis Data Streams to access and integrate third-party datasets from AWS CodeCommit repositories. Kinesis Data Streams is for real-time data streaming. CodeCommit is a version control service. This combination is completely unsuitable for integrating third-party datasets, and would involve significant overhead and custom development.
- D: Use Amazon Kinesis Data Streams to access and integrate third-party datasets from Amazon Elastic Container Registry (Amazon ECR). Similar to option C, Kinesis Data Streams is for real-time data streaming and ECR is a container registry. This option makes no sense in the context of integrating third-party datasets and introduces unnecessary complexity.
Therefore,
AWS Data Exchange is the most appropriate service for integrating third-party datasets with the least operational overhead, as it directly addresses the problem of accessing and using external data sources through streamlined API calls.
- AWS Data Exchange, https://aws.amazon.com/data-exchange/
- AWS DataSync, https://aws.amazon.com/datasync/
- Amazon Kinesis Data Streams, https://aws.amazon.com/kinesis/data-streams/
- AWS CodeCommit, https://aws.amazon.com/codecommit/
- Amazon Elastic Container Registry (Amazon ECR), https://aws.amazon.com/ecr/
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Question 4
A financial company wants to implement a data mesh. The data mesh must support centralized data governance, data analysis, and data access control. The company has decided to use AWS Glue for data catalogs and extract, transform, and load (ETL) operations.
Which combination of AWS services will implement a data mesh? (Choose two.)
- A. Use Amazon Aurora for data storage. Use an Amazon Redshift provisioned cluster for data analysis.
- B. Use Amazon S3 for data storage. Use Amazon Athena for data analysis.
- C. Use AWS Glue DataBrew for centralized data governance and access control.
- D. Use Amazon RDS for data storage. Use Amazon EMR for data analysis.
- E. Use AWS Lake Formation for centralized data governance and access control.
Correct Answer:
BE
Explanation:
The best combination of AWS services to implement a data mesh with centralized data governance, data analysis, and data access control, alongside AWS Glue for data catalogs and ETL, is:
B. Use Amazon S3 for data storage. Use Amazon Athena for data analysis.
E. Use AWS Lake Formation for centralized data governance and access control.
Reasoning:
A data mesh architecture emphasizes decentralized data ownership and domain-oriented data management, while maintaining interoperability through centralized governance. To implement this on AWS:
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Amazon S3 is the preferred storage for data lakes because it is scalable, cost-effective, and can store data in various formats. A data lake is central to a data mesh as it provides a repository for data produced by different domains.
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Amazon Athena enables data analysis directly on data stored in Amazon S3 using standard SQL. This allows different domains to analyze the data they need without requiring complex data movement or transformation.
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AWS Lake Formation is specifically designed for building, securing, and managing data lakes. It centralizes data governance, access control, and security policies across the data lake, ensuring interoperability and compliance in a data mesh environment.
Reasons for not choosing other options:
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A. Amazon Aurora and Amazon Redshift: Aurora is a relational database, and Redshift is a data warehouse. While useful for specific use cases, they don't align with the flexible, scalable data lake foundation required for a data mesh. They are not the best choices for the underlying data storage and analysis platform for a data mesh.
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C. AWS Glue DataBrew: Glue DataBrew is a data preparation tool, not a governance or access control service. It is used for cleaning and normalizing data, which is a separate function from the core requirements of data governance and access control in a data mesh.
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D. Amazon RDS and Amazon EMR: RDS is a relational database service, and EMR is a big data processing framework. Similar to option A, these services are not ideal for the foundational data lake storage and governance aspects of a data mesh. While EMR can perform data analysis, it doesn't inherently provide the centralized governance and access control needed for a data mesh.
Based on these points, the correct answer is BE.
- AWS Lake Formation, https://aws.amazon.com/lake-formation/
- Amazon S3, https://aws.amazon.com/s3/
- Amazon Athena, https://aws.amazon.com/athena/
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Question 5
A data engineer maintains custom Python scripts that perform a data formatting process that many AWS Lambda functions use. When the data engineer needs to modify the Python scripts, the data engineer must manually update all the Lambda functions.
The data engineer requires a less manual way to update the Lambda functions.
Which solution will meet this requirement?
- A. Store a pointer to the custom Python scripts in the execution context object in a shared Amazon S3 bucket.
- B. Package the custom Python scripts into Lambda layers. Apply the Lambda layers to the Lambda functions.
- C. Store a pointer to the custom Python scripts in environment variables in a shared Amazon S3 bucket.
- D. Assign the same alias to each Lambda function. Call reach Lambda function by specifying the function's alias.
Correct Answer:
B
Explanation:
The best solution to avoid manual updates to multiple Lambda functions when modifying shared Python scripts is: B. Package the custom Python scripts into Lambda layers. Apply the Lambda layers to the Lambda functions.
Reasoning:
Lambda Layers provide a mechanism to package and share common code, libraries, or dependencies across multiple Lambda functions. By packaging the custom Python scripts into a Lambda Layer and applying that layer to all the Lambda functions that use them, you achieve the following:
- Centralized code management: The Python scripts are managed in a single location (the Lambda Layer).
- Reusability: The same scripts can be used by multiple Lambda functions.
- Simplified updates: When you need to modify the scripts, you update the Lambda Layer, and all Lambda functions using that layer automatically inherit the changes (after updating the function configuration to use the latest Layer version).
- Reduced deployment size: Each Lambda function doesn't need to contain the same scripts, reducing the size of deployment packages.
This approach directly addresses the requirement of a less manual way to update the Lambda functions, ensuring consistency and streamlining updates.
Reasons for not choosing the other options:
- A: Storing a pointer in S3 and accessing it from the execution context would require each Lambda function to fetch the script, adding overhead and complexity. It doesn't provide the automatic update mechanism of Lambda Layers.
- C: Storing a pointer in environment variables in S3 suffers from similar problems as option A. Environment variables are also limited in size and are not designed for storing file paths or large configuration data.
- D: Assigning the same alias to each Lambda function doesn't address the problem of updating the Python scripts. Aliases are used for managing different versions of Lambda functions and routing traffic, not for sharing code.
Lambda Layers are specifically designed for this use case, making option B the most efficient and maintainable solution.
- AWS Lambda Layers, https://docs.aws.amazon.com/lambda/latest/dg/configuration-layers.html
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Question 6
A company created an extract, transform, and load (ETL) data pipeline in AWS Glue. A data engineer must crawl a table that is in Microsoft SQL Server. The data engineer needs to extract, transform, and load the output of the crawl to an Amazon S3 bucket. The data engineer also must orchestrate the data pipeline.
Which AWS service or feature will meet these requirements MOST cost-effectively?
- A. AWS Step Functions
- B. AWS Glue workflows
- C. AWS Glue Studio
- D. Amazon Managed Workflows for Apache Airflow (Amazon MWAA)
Correct Answer:
B
Explanation:
The suggested answer is B: AWS Glue workflows.
Reasoning:
AWS Glue workflows are the most cost-effective option for orchestrating an ETL pipeline that involves crawling a Microsoft SQL Server table and loading the output to an Amazon S3 bucket. AWS Glue is specifically designed for ETL tasks on AWS and provides native integration with data sources like Microsoft SQL Server through JDBC connectors. AWS Glue crawlers can automatically infer the schema of the data in SQL Server. Glue workflows allow you to define and manage complex ETL pipelines, including triggering crawlers, running Glue jobs (which perform the extract, transform, and load operations), and handling dependencies between these tasks. This simplifies the orchestration process and reduces the need for custom scripting or external orchestration tools. The cost-effectiveness comes from using a service specifically designed and priced for ETL.
Reasons for not choosing the other options:
- A. AWS Step Functions: Step Functions can orchestrate various AWS services, including Glue jobs. However, using Step Functions alone would require more manual configuration and integration to manage the Glue crawler and data transformation jobs. It's less integrated for ETL orchestration compared to Glue workflows, making it a less cost-effective solution for this specific scenario.
- C. AWS Glue Studio: AWS Glue Studio provides a visual interface for designing and creating ETL jobs. While it simplifies job development, the underlying orchestration mechanism often relies on Glue workflows. Glue Studio itself doesn't replace the need for a workflow orchestrator. Glue Studio essentially provides the underlying functionality by making use of the AWS Glue Workflows.
- D. Amazon Managed Workflows for Apache Airflow (Amazon MWAA): Amazon MWAA is a fully managed service for Apache Airflow, a popular open-source workflow management platform. While MWAA is a powerful orchestration tool, it is generally more expensive and complex to set up and manage than AWS Glue workflows, especially for simple ETL pipelines. It's overkill for this specific scenario and would not be the most cost-effective choice.
- AWS Glue Documentation, https://docs.aws.amazon.com/glue/index.html
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Question 7
A financial services company stores financial data in Amazon Redshift. A data engineer wants to run real-time queries on the financial data to support a web-based trading application. The data engineer wants to run the queries from within the trading application.
Which solution will meet these requirements with the LEAST operational overhead?
- A. Establish WebSocket connections to Amazon Redshift.
- B. Use the Amazon Redshift Data API.
- C. Set up Java Database Connectivity (JDBC) connections to Amazon Redshift.
- D. Store frequently accessed data in Amazon S3. Use Amazon S3 Select to run the queries.
Correct Answer:
B
Explanation:
The best solution is B. Use the Amazon Redshift Data API.
Reasoning: The Amazon Redshift Data API is specifically designed for executing SQL queries against Amazon Redshift without needing persistent database connections. It abstracts away the complexities of managing connections, making it ideal for serverless applications and web-based applications that require low operational overhead. It's a secure, HTTPs-based API that integrates well with IAM for authorization. The Data API allows running SQL commands, including queries, and retrieves results.
Reasons for not choosing the other options:
- A. Establish WebSocket connections to Amazon Redshift: Amazon Redshift does not natively support WebSocket connections. This option would require significant custom development and is not a standard approach for querying Redshift from an application.
- C. Set up Java Database Connectivity (JDBC) connections to Amazon Redshift: While JDBC is a valid method for connecting to Redshift, it involves managing connection pools, handling connection failures, and ensuring proper security. This increases the operational overhead compared to the Data API. Managing JDBC connections from a web application can be complex and resource-intensive.
- D. Store frequently accessed data in Amazon S3. Use Amazon S3 Select to run the queries: Storing data in S3 and using S3 Select is suitable for querying data in S3 but introduces data duplication and requires data synchronization between Redshift and S3. This adds complexity and is not ideal for real-time querying of financial data already residing in Redshift. Also, S3 Select may not be as performant as Redshift for complex queries.
The Redshift Data API provides the least operational overhead and is well-suited for running real-time queries from a web-based application.
- Amazon Redshift Data API, https://docs.aws.amazon.com/redshift/latest/mgmt/data-api.html
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Question 8
A company uses Amazon Athena for one-time queries against data that is in Amazon S3. The company has several use cases. The company must implement permission controls to separate query processes and access to query history among users, teams, and applications that are in the same AWS account.
Which solution will meet these requirements?
- A. Create an S3 bucket for each use case. Create an S3 bucket policy that grants permissions to appropriate individual IAM users. Apply the S3 bucket policy to the S3 bucket.
- B. Create an Athena workgroup for each use case. Apply tags to the workgroup. Create an IAM policy that uses the tags to apply appropriate permissions to the workgroup.
- C. Create an IAM role for each use case. Assign appropriate permissions to the role for each use case. Associate the role with Athena.
- D. Create an AWS Glue Data Catalog resource policy that grants permissions to appropriate individual IAM users for each use case. Apply the resource policy to the specific tables that Athena uses.
Correct Answer:
B
Explanation:
The best solution is B: Create an Athena workgroup for each use case. Apply tags to the workgroup. Create an IAM policy that uses the tags to apply appropriate permissions to the workgroup.
Reasoning:
Athena workgroups are designed to isolate query processes and manage access to query history, aligning perfectly with the company's requirements. Each workgroup can be tailored to a specific use case, allowing for granular permission control. By applying tags to workgroups, IAM policies can be created to dynamically manage permissions based on these tags, simplifying administration and enhancing security.
Why other options are not the best:
- A: Creating separate S3 buckets does not address the requirement of separating query history and managing permissions within Athena itself. S3 bucket policies control access to data, but not Athena's query execution environment.
- C: Creating separate IAM roles for each use case could work, but it does not provide the same level of isolation and management as Athena workgroups. It would also be cumbersome to manage numerous IAM roles and ensure they have the correct permissions for Athena.
- D: While AWS Glue Data Catalog resource policies can control access to tables, they do not address the requirement of separating query history and managing permissions for query execution within Athena.
Therefore,
option B is the most suitable because it leverages Athena's built-in workgroup feature for workload isolation and permission management.
- Athena Workgroups, https://docs.aws.amazon.com/athena/latest/ug/workgroups-concept.html
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Question 9
A data engineer needs to schedule a workflow that runs a set of AWS Glue jobs every day. The data engineer does not require the Glue jobs to run or finish at a specific time.
Which solution will run the Glue jobs in the MOST cost-effective way?
- A. Choose the FLEX execution class in the Glue job properties.
- B. Use the Spot Instance type in Glue job properties.
- C. Choose the STANDARD execution class in the Glue job properties.
- D. Choose the latest version in the GlueVersion field in the Glue job properties.
Correct Answer:
A
Explanation:
The most cost-effective solution for running AWS Glue jobs daily without specific timing requirements is to A. Choose the FLEX execution class in the Glue job properties.
Reasoning:
The FLEX execution class is designed to leverage spare capacity within the AWS infrastructure, offering a discounted price compared to the STANDARD execution class. This makes it ideal for cost savings when there are no strict time constraints for job execution or completion. The key advantage of FLEX is its ability to utilize available compute capacity, leading to significant cost optimization for non-time-sensitive data integration workloads.
Reasons for not choosing other options:
- B. Use the Spot Instance type in Glue job properties: While Spot Instances can offer cost savings, they are subject to interruption if the capacity is needed elsewhere. This could lead to job failures or delays, which may not be acceptable even without strict time requirements. Spot Instances require handling of potential interruptions, adding complexity.
- C. Choose the STANDARD execution class in the Glue job properties: The STANDARD execution class provides dedicated resources and is suitable for time-sensitive jobs. However, it is more expensive than the FLEX execution class, making it less cost-effective when timing is not a concern.
- D. Choose the latest version in the GlueVersion field in the Glue job properties: Selecting the latest Glue version ensures you are using the most up-to-date features and improvements, but it does not directly impact the cost-effectiveness of running the job. It's more about functionality and compatibility than cost optimization.
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Question 10
A data engineer needs to create an AWS Lambda function that converts the format of data from .csv to Apache Parquet. The Lambda function must run only if a user uploads a .csv file to an Amazon S3 bucket.
Which solution will meet these requirements with the LEAST operational overhead?
- A. Create an S3 event notification that has an event type of s3:ObjectCreated:*. Use a filter rule to generate notifications only when the suffix includes .csv. Set the Amazon Resource Name (ARN) of the Lambda function as the destination for the event notification.
- B. Create an S3 event notification that has an event type of s3:ObjectTagging:* for objects that have a tag set to .csv. Set the Amazon Resource Name (ARN) of the Lambda function as the destination for the event notification.
- C. Create an S3 event notification that has an event type of s3:*. Use a filter rule to generate notifications only when the suffix includes .csv. Set the Amazon Resource Name (ARN) of the Lambda function as the destination for the event notification.
- D. Create an S3 event notification that has an event type of s3:ObjectCreated:*. Use a filter rule to generate notifications only when the suffix includes .csv. Set an Amazon Simple Notification Service (Amazon SNS) topic as the destination for the event notification. Subscribe the Lambda function to the SNS topic.
Correct Answer:
A
Explanation:
The best solution is A. Create an S3 event notification that has an event type of s3:ObjectCreated:*. Use a filter rule to generate notifications only when the suffix includes .csv. Set the Amazon Resource Name (ARN) of the Lambda function as the destination for the event notification.
Reasoning: This approach directly triggers the Lambda function upon the creation of a .csv file in the S3 bucket. The event type `s3:ObjectCreated:*` ensures that the notification is sent whenever a new object is created, and the suffix filter ensures that the Lambda function is invoked only for .csv files. This minimizes unnecessary Lambda invocations, thus reducing operational overhead.
Reasons for not choosing other options:
- B: Using `s3:ObjectTagging:*` requires additional steps to tag the uploaded objects with `.csv`, increasing operational overhead. The objects have to be tagged before the lambda function can be invoked.
- C: Using `s3:*` as the event type would trigger the Lambda function for all S3 events, not just object creation, leading to unnecessary invocations and increased costs.
- D: Introducing Amazon SNS adds an extra layer of complexity and operational overhead. While SNS can be useful for fan-out scenarios, it's not necessary for this simple use case where a single Lambda function needs to be triggered.
The key requirement is to minimize operational overhead, and option A achieves this by directly connecting the S3 event to the Lambda function with a precise filter.