Amazon Web ServicesAssociate levelDEA-C01Page 3 of 6

DEA-C01 exam dumps: questions 201 to 300 of 549

Page 3 of the free DEA-C01 question bank for the AWS Certified Data Engineer - Associate exam. Questions 201 to 300 are listed below, the first 5 in full with answers and explanations. Back to page 1 for the exam overview and FAQ.

Question bank last updated December 2024

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Free DEA-C01 practice questions

Questions 201 to 205 of 549

Pick an answer before you open the explanation. Each question also has its own page with a permalink.

DEA-C01 Question 201

Select 3

A data engineering team is setting up an AWS Glue Data Catalog for their organization. They want to catalog their data stored in Amazon S3 buckets and make it queryable using Amazon Athena. Which steps must the team take to create and populate the Data Catalog correctly?

  1. A

    Create an AWS Glue crawler, configure it with the S3 bucket location, and run the crawler to populate the Data Catalog.

  2. B

    Manually define tables and schemas in the AWS Glue Data Catalog without using a crawler.

  3. C

    Ensure an IAM role with proper permissions to the S3 bucket and Glue service is configured and attached to the crawler.

  4. D

    Use AWS Glue ETL jobs directly to create the Data Catalog without running a crawler.

  5. E

    Integrate the AWS Glue Data Catalog with Amazon Athena by enabling the Glue Data Catalog as the query metadata source in Athena settings.

Show answer and explanation

Correct answers: A, C, E

Explanation

To create and populate an AWS Glue Data Catalog for data stored in Amazon S3, you should use a Glue crawler to scan the S3 bucket and automatically define table schemas in the Data Catalog. Proper IAM permissions must be configured to allow the crawler access to the S3 bucket and Glue service. Additionally, you need to enable the Glue Data Catalog as the metadata source in Amazon Athena to make the cataloged data queryable. These steps ensure a seamless integration between S3, Glue, and Athena for data analysis.

  • A. Correct.

    Correct. AWS Glue crawlers are used to scan data in S3 and automatically populate the Data Catalog with table definitions and schema details.

  • B. Incorrect.

    Incorrect. While manual table creation is possible, it is not the recommended or automated way to catalog large datasets, especially for S3-based storage.

  • C. Correct.

    Correct. An IAM role with the necessary permissions is required for the crawler to access the S3 bucket and update the Glue Data Catalog.

  • D. Incorrect.

    Incorrect. AWS Glue ETL jobs are designed for data transformation and processing, not for directly creating or populating the Data Catalog.

  • E. Correct.

    Correct. To make the Glue Data Catalog queryable with Amazon Athena, you need to configure Athena to use the Glue Data Catalog as the metadata source.

DEA-C01 Question 202

Select 3

You are a data engineer tasked with creating a data catalog for an S3-based data lake. The catalog should enable querying the data using Amazon Athena. Which steps should you take to create and populate the data catalog effectively?

  1. A

    Use AWS Glue to create a crawler and configure it to scan the S3 bucket containing the data.

  2. B

    Manually create tables in the AWS Glue Data Catalog by specifying the schema and location of the S3 data.

  3. C

    Run the AWS Glue crawler to automatically detect the schema and populate the AWS Glue Data Catalog.

  4. D

    Ensure that the S3 bucket has a policy allowing access to AWS Glue and Athena services.

  5. E

    Launch an Amazon RDS database to store metadata for the data catalog.

Show answer and explanation

Correct answers: A, C, D

Explanation

To create a data catalog for an S3-based data lake, AWS Glue crawlers are typically used to scan the S3 bucket, detect schemas, and populate the Glue Data Catalog. This catalog serves as a metadata repository that is natively integrated with Amazon Athena, enabling efficient querying. It's also critical to ensure that the S3 bucket permissions allow Glue and Athena to access the data. While manual table creation and external databases are possible, they are not necessary for this use case and would complicate the workflow.

  • A. Correct.

    Correct. AWS Glue crawlers are designed to scan data sources like S3 and automatically detect the schema, creating tables in the Glue Data Catalog.

  • B. Incorrect.

    Incorrect. While you can manually create tables in AWS Glue, this is not the recommended approach for automating and managing schema detection, especially when dealing with large data lakes.

  • C. Correct.

    Correct. Running the AWS Glue crawler is essential to automatically populate the Glue Data Catalog with the metadata and schema of your S3 data.

  • D. Correct.

    Correct. To enable AWS Glue and Athena to access the S3 bucket, proper permissions must be granted via an S3 bucket policy.

  • E. Incorrect.

    Incorrect. AWS Glue Data Catalog serves as the metadata repository, and there is no need to launch an Amazon RDS database for this purpose.

DEA-C01 Question 203

Select 2

You are a data engineer working for a retail company that processes large amounts of transactional data daily. You need to create a central metadata repository to enable your team to search, query, and manage the data stored in Amazon S3, Redshift, and RDS. Which steps should you take to create a data catalog using AWS Glue?

  1. A

    Set up an AWS Glue Crawler to scan your data stores and populate the Data Catalog.

  2. B

    Manually define the metadata schema for each table in the AWS Glue Data Catalog.

  3. C

    Configure IAM roles to grant AWS Glue access to the data sources.

  4. D

    Use AWS Glue Studio to automatically visualize and extract the metadata from the data sources.

  5. E

    Enable encryption at rest for the AWS Glue Data Catalog to secure the metadata.

Show answer and explanation

Correct answers: A, C

Explanation

To create a Data Catalog in AWS Glue, you need to use AWS Glue Crawlers to scan the data stores and extract metadata automatically. Additionally, you must configure IAM roles to grant Glue the necessary permissions to access the data sources. These steps ensure that the metadata is populated and accessible for querying and managing data. While other options like manual schema definition or encryption are valid considerations, they are not core steps in the process of creating an AWS Glue Data Catalog.

  • A. Correct.

    Correct: AWS Glue Crawlers automatically scan your data stores, extract metadata, and populate the Data Catalog with the necessary information.

  • B. Incorrect.

    Incorrect: While manually defining metadata is possible, it is not required when using AWS Glue Crawlers, which automate metadata extraction.

  • C. Correct.

    Correct: IAM roles are required to grant AWS Glue the necessary permissions to access and interact with the data sources.

  • D. Incorrect.

    Incorrect: AWS Glue Studio is primarily used for creating and managing ETL jobs, not for extracting metadata to populate the Data Catalog.

  • E. Incorrect.

    Incorrect: While encryption is important for securing metadata, it is not a required step for creating a Data Catalog in AWS Glue.

DEA-C01 Question 204

Select 2

Your company wants to organize and manage metadata for an S3 data lake to make it searchable for analytics workloads. You must create a data catalog in AWS that integrates with AWS Glue for ETL and supports querying with Amazon Athena. What steps should you take to create and populate the data catalog effectively?

  1. A

    Use AWS Glue to crawl the S3 bucket and populate the metadata in the Glue Data Catalog.

  2. B

    Manually upload a JSON metadata file to the Glue Data Catalog for each dataset in the S3 bucket.

  3. C

    Define a Glue database and use AWS Glue Crawlers to scan the data and update the catalog automatically.

  4. D

    Use Amazon Redshift to query the S3 bucket and automatically populate the Glue Data Catalog.

  5. E

    Enable automatic schema inference in Amazon Athena to directly populate the Glue Data Catalog without additional tools.

Show answer and explanation

Correct answers: A, C

Explanation

To create a data catalog for an S3 data lake, AWS Glue provides powerful tools like Glue Crawlers that can automatically scan S3 buckets, infer schemas, and populate metadata into the Glue Data Catalog. Defining a Glue database ensures that the metadata is organized correctly. Manual approaches or reliance on unrelated services like Redshift or Athena are either inefficient or not feasible for this task.

  • A. Correct.

    Correct. AWS Glue Crawlers can scan the S3 bucket, infer the schema, and populate the Glue Data Catalog automatically. This is a standard method for creating and maintaining a data catalog in AWS.

  • B. Incorrect.

    Incorrect. Manually uploading metadata is not an efficient or scalable solution, especially for large datasets. AWS Glue provides automated tools for this purpose.

  • C. Correct.

    Correct. Defining a Glue database and using Glue Crawlers ensures that the scanned metadata is organized and stored appropriately in the Glue Data Catalog.

  • D. Incorrect.

    Incorrect. Amazon Redshift is primarily a data warehousing service and does not automatically integrate with the Glue Data Catalog to populate metadata from S3. Redshift Spectrum can query S3, but it doesn't populate the Glue Data Catalog.

  • E. Incorrect.

    Incorrect. Amazon Athena uses the Glue Data Catalog for querying but does not have a feature to populate the catalog directly. Glue Crawlers are needed to scan and populate the catalog.

DEA-C01 Question 205

Select 3

You are a Data Engineer tasked with setting up a data catalog for a large retail company to enable discovery and querying of data stored in Amazon S3 using Amazon Athena. The company wants the catalog to automatically update whenever new data is added to the S3 bucket. Which steps should you take to create and maintain this data catalog effectively?

  1. A

    Use AWS Glue to create a crawler that scans the S3 bucket and populates the data catalog.

  2. B

    Manually define each table and schema in the AWS Glue Data Catalog.

  3. C

    Configure the AWS Glue crawler to run on a schedule or trigger it using an event-based mechanism.

  4. D

    Ensure that the S3 bucket is set up with versioning to support the data catalog.

  5. E

    Create an IAM role with sufficient permissions for the AWS Glue crawler to access the S3 bucket.

Show answer and explanation

Correct answers: A, C, E

Explanation

To create and maintain a data catalog for querying data in S3 with Amazon Athena, AWS Glue crawlers are the recommended solution. They automate the process of scanning data and updating the Data Catalog. Scheduling or triggering the crawler ensures the metadata stays up-to-date, and appropriate IAM permissions are necessary for the crawler to access the data in S3. Manually defining tables and enabling S3 versioning are not required steps in this process.

  • A. Correct.

    Correct. AWS Glue crawlers can automatically scan data in your S3 bucket and populate the Data Catalog with metadata such as table definitions and schemas.

  • B. Incorrect.

    Incorrect. Manually defining each table and schema is time-consuming and error-prone, especially for large datasets. AWS Glue crawlers are designed to automate this process.

  • C. Correct.

    Correct. Scheduling or triggering the AWS Glue crawler ensures that the data catalog is updated automatically whenever new data is added.

  • D. Incorrect.

    Incorrect. While S3 versioning is useful for data management, it is not a requirement for creating or maintaining the AWS Glue Data Catalog.

  • E. Correct.

    Correct. AWS Glue crawlers require an IAM role with the necessary permissions to access the S3 bucket and populate the Data Catalog.

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DEA-C01 practice questions 201 to 300 of 549

Every question has a page with the answer and explanation. Numbers are stable, so you can bookmark or share them. The bank is split into 6 pages of up to 100 questions.

  1. 201.A data engineering team is setting up an AWS Glue Data Catalog for their organization. They want to catalog...
  2. 202.You are a data engineer tasked with creating a data catalog for an S3-based data lake. The catalog should...
  3. 203.You are a data engineer working for a retail company that processes large amounts of transactional data...
  4. 204.Your company wants to organize and manage metadata for an S3 data lake to make it searchable for analytics...
  5. 205.You are a Data Engineer tasked with setting up a data catalog for a large retail company to enable discovery...
  6. 206.You are working as a Data Engineer for a financial services company that needs to store and process sensitive...
  7. 207.A financial company uses AWS to store sensitive customer data, including Personally Identifiable Information...
  8. 208.A company is building a data lake on AWS to store and analyze vast amounts of customer data, including...
  9. 209.You are working as a data engineer for a financial institution that handles sensitive customer data,...
  10. 210.A company is building a data lake on Amazon S3 to store and analyze vast amounts of data from multiple...
  11. 211.You are designing a data lake architecture on AWS and need to ensure that datasets are discoverable and...
  12. 212.Your company uses AWS Glue Data Catalog to manage metadata for various datasets. You are tasked with ensuring...
  13. 213.You are working as a Data Engineer for a company that uses AWS Glue as a data integration service. The...
  14. 214.You are working as a data engineer for a company that uses AWS Glue Data Catalog to manage metadata for their...
  15. 215.You are a data engineer tasked with setting up a metadata and data cataloging solution for your...
  16. 216.You are working as a data engineer for a company that stores large volumes of customer data in Amazon S3. To...
  17. 217.Your company uses Amazon S3 to store large amounts of data for analytics and compliance. Recently, the...
  18. 218.Your company stores large volumes of data in Amazon S3. To optimize costs, you need to implement a solution...
  19. 219.Your organization stores raw data in an Amazon S3 bucket for analytics purposes. Due to compliance...
  20. 220.You are designing a data management strategy for a company that processes large volumes of e-commerce...
  21. 221.A company is using AWS Glue for an ETL (Extract, Transform, Load) process to transform raw data from an...
  22. 222.A data engineering team is building a real-time data pipeline to process streaming data from IoT devices. The...
  23. 223.You are a Data Engineer at a company that collects large-scale IoT sensor data from devices distributed...
  24. 224.You are building a data pipeline on AWS to process and analyze real-time streaming data from IoT sensors. The...
  25. 225.You are working as a data engineer for a retail company. The company stores customer transaction data in an...
  26. 226.A company is designing a data storage strategy for a new analytics platform. The platform handles two types...
  27. 227.A retail company processes customer transaction data and needs to store it cost-effectively based on access...
  28. 228.You are designing a data storage architecture for a financial analytics platform that processes large volumes...
  29. 229.A data analytics company processes large amounts of customer data daily. Frequently accessed transactional...
  30. 230.A company processes large amounts of data daily and needs to implement a cost-effective storage solution for...
  31. 231.A company is building a data lake on Amazon S3 to store large volumes of data generated daily. The data is...
  32. 232.Your company stores large volumes of customer transaction data in Amazon S3. This data is accessed frequently...
  33. 233.You are working as a data engineer for a company that processes large volumes of data daily and uses Amazon...
  34. 234.You are a data engineer at a company that processes large volumes of user activity logs daily. The logs are...
  35. 235.You are working as a data engineer for a company that stores large volumes of e-commerce transaction logs in...
  36. 236.A financial services company is required to delete customer data from their AWS storage systems to comply...
  37. 237.A retail company stores customer data in Amazon S3, including sensitive information such as payment details....
  38. 238.Your company processes sensitive customer data stored in an Amazon S3 bucket. Due to new legal regulations,...
  39. 239.Your company needs to comply with GDPR requirements by ensuring that customer data stored in Amazon S3 can be...
  40. 240.Your company stores customer data in an Amazon S3 bucket. Due to new legal requirements, you must ensure that...
  41. 241.An organization uses Amazon S3 to store large volumes of data for analytics. They want to implement a data...
  42. 242.A company is storing critical business data in Amazon S3. The company needs to implement a data retention...
  43. 243.A healthcare organization stores patient data in an Amazon S3 bucket. Due to compliance requirements, the...
  44. 244.Your company is implementing a data retention policy for a data lake built on Amazon S3. The policy states...
  45. 245.Your company processes large amounts of customer transaction data daily and stores it in Amazon S3. The...
  46. 246.A data engineering team is designing a data pipeline to store and process critical business data on AWS. They...
  47. 247.A company is building a data lake on Amazon S3 to store critical business data. To ensure data resiliency and...
  48. 248.A company is designing a data lake on Amazon S3 to store critical financial data. The data must be highly...
  49. 249.You are designing a data storage solution for a financial institution that requires extremely high resiliency...
  50. 250.You are designing a data storage solution for a financial institution that requires high resiliency and...
  51. 251.You are designing a data lake solution on Amazon S3 to store clickstream data from a web application. The...
  52. 252.You are designing a data lake architecture on AWS to store and analyze customer transaction data. The data is...
  53. 253.A company is using Amazon Redshift to store their customer data in a star schema format. Recently, they...
  54. 254.A data engineering team is tasked with designing a data model for a retail application that ingests...
  55. 255.Your team is designing a data pipeline to process product sales data in Amazon Redshift. The schema for the...
  56. 256.A company is using Amazon Kinesis Data Streams to collect real-time clickstream data from its e-commerce...
  57. 257.An e-commerce company needs to analyze clickstream data from its website in near real-time to understand user...
  58. 258.An analytics team is building a data pipeline to process large volumes of clickstream data in near real-time....
  59. 259.You are a data engineer tasked with designing a data pipeline for a retail company that collects real-time...
  60. 260.You are designing a data pipeline for a retail company to process and analyze customer transactions in near...
  61. 261.You have been tasked with designing a data model for a large-scale e-commerce application. The application...
  62. 262.You are working as a data engineer for a retail company that collects customer purchase data in real-time....
  63. 263.You are designing a data model for an e-commerce platform that tracks customer orders, product inventory, and...
  64. 264.A data engineering team is designing a data warehouse solution on Amazon Redshift. The team needs to optimize...
  65. 265.You are designing a data model for an e-commerce application using Amazon DynamoDB. The application needs to...
  66. 266.You are a data engineer at a company that uses AWS Glue for ETL processing and Amazon Redshift for analytics....
  67. 267.You are working as a data engineer for a financial services company that requires strict regulatory...
  68. 268.Your organization uses AWS Glue to manage ETL jobs and a data lake in Amazon S3. You need to ensure the...
  69. 269.A financial organization is building a data pipeline for processing sensitive customer transactions using AWS...
  70. 270.Your organization processes large volumes of transactional data in an AWS Glue ETL pipeline and stores the...
  71. 271.You are designing a data lake architecture for an e-commerce company on Amazon S3. The company frequently...
  72. 272.You are tasked with optimizing a large Amazon Redshift table that stores sales transaction data. This table...
  73. 273.You are working on a data pipeline that ingests large volumes of time-series data into Amazon Redshift. The...
  74. 274.You are designing a data pipeline in AWS where large volumes of data are ingested daily into an Amazon S3...
  75. 275.A data engineering team is tasked with optimizing a 1 TB Amazon Redshift table used for analytical queries....
  76. 276.You are designing a data pipeline for a retail company that processes data from multiple sources. The data...
  77. 277.You are designing a data lake architecture for an e-commerce company that collects transactional data,...
  78. 278.A company needs to design a data processing pipeline to handle three types of data: structured sales records...
  79. 279.You are designing a data lake architecture for a media company that processes multiple types of data,...
  80. 280.You are designing a data lake architecture to store and analyze data from multiple sources. The data includes...
  81. 281.Your team is using Amazon Athena to query data stored in an S3 data lake. The data schema in the data lake...
  82. 282.You are working on an AWS Glue ETL pipeline that processes data stored in Amazon S3 using an Apache Parquet...
  83. 283.You are designing a data pipeline using Amazon S3 and AWS Glue. The schema of your data stored in S3 is...
  84. 284.Your team is working on a data pipeline that uses an Amazon S3 data lake to store customer data in Parquet...
  85. 285.You are managing a data lake on Amazon S3 using AWS Glue for ETL jobs and Amazon Athena for querying the...
  86. 286.Your organization processes financial transaction data using an Amazon Kinesis Data Stream. You notice that...
  87. 287.A company uses Amazon Redshift as their data warehouse and wants to ensure efficient query performance for...
  88. 288.A company uses Amazon Redshift as their data warehouse and has implemented a sophisticated ETL pipeline that...
  89. 289.You are a data engineer managing a data pipeline that ingests, processes, and stores streaming data using...
  90. 290.A data engineering team is responsible for managing a large-scale data pipeline that ingests and transforms...
  91. 291.A company processes large volumes of log data generated by its applications and wants to automate the...
  92. 292.You are a data engineer for a company that processes large volumes of e-commerce transaction data daily. The...
  93. 293.You are tasked with designing a solution to automate the processing of daily sales data files uploaded to an...
  94. 294.You are tasked with building a data processing pipeline that ingests data from IoT sensors, processes the...
  95. 295.Your company receives daily sales data files in an Amazon S3 bucket. You need to automate the process of...
  96. 296.A company is implementing a real-time analytics pipeline where data from IoT devices is ingested into AWS....
  97. 297.You are tasked with designing a data pipeline in AWS to process large volumes of clickstream data in near...
  98. 298.You are designing a data ingestion pipeline for a large-scale IoT application that generates millions of...
  99. 299.A company is using Amazon Kinesis Data Streams to collect log data from its application in real-time. The...
  100. 300.A company is using an Amazon Redshift cluster to store analytics data and has recently observed a significant...

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