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Exam Code: AWS Certified Cloud Practitioner CLF-C02
Exam Name: AWS Certified Cloud Practitioner CLF-C02
Certification Provider: Amazon
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AWS Certified Cloud Practitioner CLF-C02 Practice Test 66

Question 650

During a cloud review, this risk is identified: Amazon Polly converts text to speech and is not a general model-training platform. Which change addresses the stated risk most directly?

  1. Use Amazon Athena for serverless SQL on supported data in S3.
  2. Use Amazon SageMaker AI for the ML lifecycle.
  3. Use Amazon Transcribe for speech-to-text.
  4. Use Amazon Polly for text-to-speech.

Explanation

Correct Answer

B:

SageMaker supports model development, training and deployment workflows. Given that a data science team needs a managed platform to build, train, and deploy custom ML models, success means the team develops and deploys its model within a managed ML workflow.

Incorrect Answers

A:

Athena queries supported datasets in S3 without provisioning dedicated database clusters. The condition for that alternative is a retail analyst wants to query data files directly in S3 using SQL without provisioning a database server; this question instead needs the team develops and deploys its model within a managed ML workflow.

C:

Transcribe converts spoken audio into written text. This action handles a contact center must turn recorded calls into searchable text transcripts, but it does not address the observed risk that amazon Polly converts text to speech and is not a general model-training platform.

D:

Polly synthesizes speech audio from written text. It would instead yield text instructions are rendered as spoken audio; the problem requires the team develops and deploys its model within a managed ML workflow.

Question 651

Cloud governance review finds the following exposure: Amazon Rekognition focuses on image and video analysis. Which change most directly fixes the issue?

  1. Use Amazon Bedrock for access to supported foundation models.
  2. Use Amazon Comprehend for natural language analysis.
  3. Use Amazon Polly for text-to-speech.
  4. Use AWS Glue with a catalog and ETL jobs.

Explanation

Correct Answer

B:

Comprehend provides managed NLP features including entity and sentiment analysis. The requirement is that a support platform must detect entities and sentiment in customer messages; the observable success condition is that the application receives sentiment and entity results from text.

Incorrect Answers

A:

Bedrock provides managed foundation model access for generative AI applications. The condition for that alternative is a developer wants to create an application using managed foundation models through an AWS API; this question instead needs the application receives sentiment and entity results from text.

C:

Polly synthesizes speech audio from written text. This action handles a reading assistant should speak written instructions aloud, but it does not address the observed risk that amazon Rekognition focuses on image and video analysis.

D:

Glue supplies data catalog metadata and managed data integration tools. It addresses a different situation: a data team needs a managed data integration service for discovering data catalogs and performing ETL; the target requires the application receives sentiment and entity results from text.

Question 652

A company needs a targeted remedy for this cloud problem: Amazon Polly generates speech from text rather than transcribing recordings. Which proposed action is best?

  1. Use Amazon QuickSight for business intelligence dashboards.
  2. Use Amazon Athena for serverless SQL on supported data in S3.
  3. Use Amazon Kinesis services for appropriate data stream workflows.
  4. Use Amazon Transcribe for speech-to-text.

Explanation

Correct Answer

D:

Transcribe converts spoken audio into written text. It avoids the specific failure that amazon Polly generates speech from text rather than transcribing recordings, and the outcome confirms that the recorded call produces a text transcript.

Incorrect Answers

A:

QuickSight provides managed reporting and interactive visualizations. That approach would be appropriate when a business intelligence group wants visual dashboards from analytical datasets, not for the current constraint: a contact center must turn recorded calls into searchable text transcripts.

B:

Athena queries supported datasets in S3 without provisioning dedicated database clusters. This action handles a retail analyst wants to query data files directly in S3 using SQL without provisioning a database server, but it does not address the observed risk that amazon Polly generates speech from text rather than transcribing recordings.

C:

Kinesis supports real-time streaming data ingestion and processing use cases. It would instead yield events are consumed shortly after production in the stream; the problem requires the recorded call produces a text transcript.

Question 653

An architect justifies this AWS design: Use Amazon Textract for document extraction. Which explanation is consistent with AWS service responsibilities and behavior?

  1. SageMaker supports model development, training and deployment workflows.
  2. Textract detects text and document structure in scanned images and PDFs.
  3. Bedrock provides managed foundation model access for generative AI applications.
  4. Comprehend provides managed NLP features including entity and sentiment analysis.

Explanation

Correct Answer

B:

Textract detects text and document structure in scanned images and PDFs. Given that a loan-processing application needs to extract key-value pairs and tables from scanned forms, success means the workflow obtains structured form fields and extracted tables.

Incorrect Answers

A:

SageMaker supports model development, training and deployment workflows. This is the rationale for use Amazon SageMaker AI for the ML lifecycle in a different situation; the chosen action is use Amazon Textract for document extraction.

C:

Bedrock provides managed foundation model access for generative AI applications. This is the rationale for use Amazon Bedrock for access to supported foundation models in a different situation; the chosen action is use Amazon Textract for document extraction.

D:

Comprehend provides managed NLP features including entity and sentiment analysis. This is the rationale for use Amazon Comprehend for natural language analysis in a different situation; the chosen action is use Amazon Textract for document extraction.

Question 654

The cloud team has decided on this approach: Use Amazon Athena for serverless SQL on supported data in S3. Which statement explains why it satisfies the scenario?

  1. Comprehend provides managed NLP features including entity and sentiment analysis.
  2. Transcribe converts spoken audio into written text.
  3. Athena queries supported datasets in S3 without provisioning dedicated database clusters.
  4. Rekognition provides image and video vision features such as label detection.

Explanation

Correct Answer

C:

Athena queries supported datasets in S3 without provisioning dedicated database clusters. The decision directly serves a retail analyst wants to query data files directly in S3 using SQL without provisioning a database server; it produces the testable result that analysts execute SQL queries against S3-stored data.

Incorrect Answers

A:

Comprehend provides managed NLP features including entity and sentiment analysis. This is the rationale for use Amazon Comprehend for natural language analysis in a different situation; the chosen action is use Amazon Athena for serverless SQL on supported data in S3.

B:

Transcribe converts spoken audio into written text. This is the rationale for use Amazon Transcribe for speech-to-text in a different situation; the chosen action is use Amazon Athena for serverless SQL on supported data in S3.

D:

Rekognition provides image and video vision features such as label detection. This is the rationale for use Amazon Rekognition for supported image analysis in a different situation; the chosen action is use Amazon Athena for serverless SQL on supported data in S3.

Question 655

A company wants to avoid unnecessary complexity while meeting this requirement: A contact center must turn recorded calls into searchable text transcripts. Which action should it take?

  1. Use AWS Glue with a catalog and ETL jobs.
  2. Use Amazon QuickSight for business intelligence dashboards.
  3. Use Amazon Athena for serverless SQL on supported data in S3.
  4. Use Amazon Transcribe for speech-to-text.

Explanation

Correct Answer

D:

Transcribe converts spoken audio into written text. Given that a contact center must turn recorded calls into searchable text transcripts, success means the recorded call produces a text transcript.

Incorrect Answers

A:

Glue supplies data catalog metadata and managed data integration tools. Its observable effect is the team catalogs source tables and runs transformation jobs, while the intended outcome here is the recorded call produces a text transcript.

B:

QuickSight provides managed reporting and interactive visualizations. It addresses a different situation: a business intelligence group wants visual dashboards from analytical datasets; the target requires the recorded call produces a text transcript.

C:

Athena queries supported datasets in S3 without provisioning dedicated database clusters. The condition for that alternative is a retail analyst wants to query data files directly in S3 using SQL without provisioning a database server; this question instead needs the recorded call produces a text transcript.

Question 656

A migration or service change has been completed: Use Amazon Rekognition for supported image analysis. Which result demonstrates its intended effect?

  1. The source descriptions are available in the selected target language.
  2. Analysts execute SQL queries against S3-stored data.
  3. Text instructions are rendered as spoken audio.
  4. The image analysis returns detected visual labels.

Explanation

Correct Answer

D:

Rekognition provides image and video vision features such as label detection. Given that a retailer wants to analyze images for objects and inappropriate visual content, success means the image analysis returns detected visual labels.

Incorrect Answers

A:

Translate converts text from one natural language to another. That result validates use Amazon Translate for language translation, rather than the stated decision to use Amazon Rekognition for supported image analysis.

B:

Athena queries supported datasets in S3 without provisioning dedicated database clusters. That result validates use Amazon Athena for serverless SQL on supported data in S3, rather than the stated decision to use Amazon Rekognition for supported image analysis.

C:

Polly synthesizes speech audio from written text. That result validates use Amazon Polly for text-to-speech, rather than the stated decision to use Amazon Rekognition for supported image analysis.

Question 657

An AWS solution uses this decision: Use Amazon SageMaker AI for the ML lifecycle. What underlying property makes this approach appropriate?

  1. Glue supplies data catalog metadata and managed data integration tools.
  2. SageMaker supports model development, training and deployment workflows.
  3. Athena queries supported datasets in S3 without provisioning dedicated database clusters.
  4. Textract detects text and document structure in scanned images and PDFs.

Explanation

Correct Answer

B:

SageMaker supports model development, training and deployment workflows. It avoids the specific failure that amazon Polly converts text to speech and is not a general model-training platform, and the outcome confirms that the team develops and deploys its model within a managed ML workflow.

Incorrect Answers

A:

Glue supplies data catalog metadata and managed data integration tools. This is the rationale for use AWS Glue with a catalog and ETL jobs in a different situation; the chosen action is use Amazon SageMaker AI for the ML lifecycle.

C:

Athena queries supported datasets in S3 without provisioning dedicated database clusters. This is the rationale for use Amazon Athena for serverless SQL on supported data in S3 in a different situation; the chosen action is use Amazon SageMaker AI for the ML lifecycle.

D:

Textract detects text and document structure in scanned images and PDFs. This is the rationale for use Amazon Textract for document extraction in a different situation; the chosen action is use Amazon SageMaker AI for the ML lifecycle.

Question 658

For a workload about to launch on AWS, the requirement is: A loan-processing application needs to extract key-value pairs and tables from scanned forms. Which option addresses it?

  1. Use Amazon Transcribe for speech-to-text.
  2. Use Amazon Polly for text-to-speech.
  3. Use AWS Glue with a catalog and ETL jobs.
  4. Use Amazon Textract for document extraction.

Explanation

Correct Answer

D:

Textract detects text and document structure in scanned images and PDFs. The requirement is that a loan-processing application needs to extract key-value pairs and tables from scanned forms; the observable success condition is that the workflow obtains structured form fields and extracted tables.

Incorrect Answers

A:

Transcribe converts spoken audio into written text. It would instead yield the recorded call produces a text transcript; the problem requires the workflow obtains structured form fields and extracted tables.

B:

Polly synthesizes speech audio from written text. This action handles a reading assistant should speak written instructions aloud, but it does not address the observed risk that amazon Rekognition labels visual objects rather than extracting tabular form fields.

C:

Glue supplies data catalog metadata and managed data integration tools. That approach would be appropriate when a data team needs a managed data integration service for discovering data catalogs and performing ETL, not for the current constraint: a loan-processing application needs to extract key-value pairs and tables from scanned forms.

Question 659

A service owner selects this control or capability: Use AWS Glue with a catalog and ETL jobs. What evidence would show the requirement is satisfied?

  1. The team catalogs source tables and runs transformation jobs.
  2. The recorded call produces a text transcript.
  3. Text instructions are rendered as spoken audio.
  4. Stakeholders view business metrics in an interactive dashboard.

Explanation

Correct Answer

A:

Glue supplies data catalog metadata and managed data integration tools. The requirement is that a data team needs a managed data integration service for discovering data catalogs and performing ETL; the observable success condition is that the team catalogs source tables and runs transformation jobs.

Incorrect Answers

B:

Transcribe converts spoken audio into written text. That result validates use Amazon Transcribe for speech-to-text, rather than the stated decision to use AWS Glue with a catalog and ETL jobs.

C:

Polly synthesizes speech audio from written text. That result validates use Amazon Polly for text-to-speech, rather than the stated decision to use AWS Glue with a catalog and ETL jobs.

D:

QuickSight provides managed reporting and interactive visualizations. That result validates use Amazon QuickSight for business intelligence dashboards, rather than the stated decision to use AWS Glue with a catalog and ETL jobs.

Question 660

A cloud team has received the following request: A reading assistant should speak written instructions aloud. Which AWS approach is the best match?

  1. Use Amazon Comprehend for natural language analysis.
  2. Use Amazon Athena for serverless SQL on supported data in S3.
  3. Use Amazon Polly for text-to-speech.
  4. Use AWS Glue with a catalog and ETL jobs.

Explanation

Correct Answer

C:

Polly synthesizes speech audio from written text. It avoids the specific failure that amazon Transcribe takes audio input and generates text, the reverse flow, and the outcome confirms that text instructions are rendered as spoken audio.

Incorrect Answers

A:

Comprehend provides managed NLP features including entity and sentiment analysis. Its observable effect is the application receives sentiment and entity results from text, while the intended outcome here is text instructions are rendered as spoken audio.

B:

Athena queries supported datasets in S3 without provisioning dedicated database clusters. That approach would be appropriate when a retail analyst wants to query data files directly in S3 using SQL without provisioning a database server, not for the current constraint: a reading assistant should speak written instructions aloud.

D:

Glue supplies data catalog metadata and managed data integration tools. It addresses a different situation: a data team needs a managed data integration service for discovering data catalogs and performing ETL; the target requires text instructions are rendered as spoken audio.

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