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Exam Code: AWS Certified Cloud Practitioner CLF-C02

Exam Name AWS Certified Cloud Practitioner CLF-C02

Certification Provider: Amazon

Corresponding Certification: AWS Certified Cloud Practitioner

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Amazon AWS Certified Cloud Practitioner CLF-C02 Practice Test 50

Question 490

Which service-to-requirement explanation best fits this situation: A data science team needs managed tools to build, train and deploy machine learning models?

  1. Amazon Lex — Lex builds conversational interfaces using speech recognition and natural language understanding
  2. Amazon SageMaker AI — SageMaker AI provides managed infrastructure and tooling for the machine-learning lifecycle
  3. Amazon Polly — converts text into lifelike speech
  4. Amazon Rekognition — analyzes image and video content using computer vision

Explanation

Correct Answer

B:

SageMaker AI provides managed infrastructure and tooling for the machine-learning lifecycle. This fits the situation: A data science team needs managed tools to build, train and deploy machine learning models.

Incorrect Answers

A:

Amazon Lex: Lex builds conversational interfaces using speech recognition and natural language understanding. This does not address the need for A data science team needs managed tools to build, train and deploy machine learning models. The appropriate Amazon SageMaker AI applies because SageMaker AI provides managed infrastructure and tooling for the machine-learning lifecycle.

C:

Amazon Polly: Converts text into lifelike speech. This does not address the need for A data science team needs managed tools to build, train and deploy machine learning models. The appropriate Amazon SageMaker AI applies because SageMaker AI provides managed infrastructure and tooling for the machine-learning lifecycle.

D:

Amazon Rekognition: Analyzes image and video content using computer vision. This does not address the need for A data science team needs managed tools to build, train and deploy machine learning models. The appropriate Amazon SageMaker AI applies because SageMaker AI provides managed infrastructure and tooling for the machine-learning lifecycle.

Question 491

A company develops custom predictive models and needs an AWS ML development platform. Which option is the best fit?

  1. Amazon Lex
  2. Amazon Rekognition
  3. Amazon SageMaker AI
  4. Amazon Polly

Explanation

Correct Answer

C:

SageMaker AI provides managed infrastructure and tooling for the machine-learning lifecycle. This fits the case: A company develops custom predictive models and needs an AWS ML development platform.

Incorrect Answers

A:

Amazon Lex: Lex builds conversational interfaces using speech recognition and natural language understanding. That does not meet the requirement: A company develops custom predictive models and needs an AWS ML development platform.

B:

Amazon Rekognition: Analyzes image and video content using computer vision. That does not meet the requirement: A company develops custom predictive models and needs an AWS ML development platform.

D:

Amazon Polly: Converts text into lifelike speech. That does not meet the requirement: A company develops custom predictive models and needs an AWS ML development platform.

Question 492

A company develops custom predictive models and needs an AWS ML development platform. Which justification most accurately supports the recommended choice?

  1. Amazon Lex — Lex builds conversational interfaces using speech recognition and natural language understanding
  2. Amazon Polly — converts text into lifelike speech
  3. Amazon Rekognition — analyzes image and video content using computer vision
  4. Amazon SageMaker AI — SageMaker AI provides managed infrastructure and tooling for the machine-learning lifecycle

Explanation

Correct Answer

D:

SageMaker AI provides managed infrastructure and tooling for the machine-learning lifecycle. This fits the situation: A company develops custom predictive models and needs an AWS ML development platform.

Incorrect Answers

A:

Amazon Lex: Lex builds conversational interfaces using speech recognition and natural language understanding. This does not address the need for A company develops custom predictive models and needs an AWS ML development platform. The appropriate Amazon SageMaker AI applies because SageMaker AI provides managed infrastructure and tooling for the machine-learning lifecycle.

B:

Amazon Polly: Converts text into lifelike speech. This does not address the need for A company develops custom predictive models and needs an AWS ML development platform. The appropriate Amazon SageMaker AI applies because SageMaker AI provides managed infrastructure and tooling for the machine-learning lifecycle.

C:

Amazon Rekognition: Analyzes image and video content using computer vision. This does not address the need for A company develops custom predictive models and needs an AWS ML development platform. The appropriate Amazon SageMaker AI applies because SageMaker AI provides managed infrastructure and tooling for the machine-learning lifecycle.

Question 493

Consider this AWS requirement: A customer support team wants an automated conversational chatbot using natural language understanding. What should the team select?

  1. Amazon Lex
  2. Amazon Translate
  3. Amazon Comprehend
  4. Amazon Polly

Explanation

Correct Answer

A:

Lex builds conversational interfaces using speech recognition and natural language understanding. This fits the case: A customer support team wants an automated conversational chatbot using natural language understanding.

Incorrect Answers

B:

Amazon Translate: Translates text between languages. That does not meet the requirement: Consider this AWS requirement: A customer support team wants an automated conversational chatbot using natural language understanding.

C:

Amazon Comprehend: Detects meaning, entities, sentiment and other structure in text. That does not meet the requirement: Consider this AWS requirement: A customer support team wants an automated conversational chatbot using natural language understanding.

D:

Amazon Polly: Converts text into lifelike speech. That does not meet the requirement: Consider this AWS requirement: A customer support team wants an automated conversational chatbot using natural language understanding.

Question 494

A review board wants evidence for this choice: A customer support team wants an automated conversational chatbot using natural language understanding. Which reasoning is best aligned with the requirement?

  1. Amazon Polly — converts text into lifelike speech
  2. Amazon Lex — Lex builds conversational interfaces using speech recognition and natural language understanding
  3. Amazon Translate — translates text between languages
  4. Amazon Comprehend — detects meaning, entities, sentiment and other structure in text

Explanation

Correct Answer

B:

Lex builds conversational interfaces using speech recognition and natural language understanding. This fits the situation: A customer support team wants an automated conversational chatbot using natural language understanding.

Incorrect Answers

A:

Amazon Polly: Converts text into lifelike speech. This does not address the need for A customer support team wants an automated conversational chatbot using natural language understanding. The appropriate Amazon Lex applies because Lex builds conversational interfaces using speech recognition and natural language understanding.

C:

Amazon Translate: Translates text between languages. This does not address the need for A customer support team wants an automated conversational chatbot using natural language understanding. The appropriate Amazon Lex applies because Lex builds conversational interfaces using speech recognition and natural language understanding.

D:

Amazon Comprehend: Detects meaning, entities, sentiment and other structure in text. This does not address the need for A customer support team wants an automated conversational chatbot using natural language understanding. The appropriate Amazon Lex applies because Lex builds conversational interfaces using speech recognition and natural language understanding.

Question 495

A travel site needs text and voice interfaces to understand customer intents. Which cloud capability or service directly addresses the requirement?

  1. Amazon Polly
  2. Amazon Comprehend
  3. Amazon Lex
  4. Amazon Translate

Explanation

Correct Answer

C:

Lex builds conversational interfaces using speech recognition and natural language understanding. This fits the case: A travel site needs text and voice interfaces to understand customer intents.

Incorrect Answers

A:

Amazon Polly: Converts text into lifelike speech. That does not meet the requirement: A travel site needs text and voice interfaces to understand customer intents.

B:

Amazon Comprehend: Detects meaning, entities, sentiment and other structure in text. That does not meet the requirement: A travel site needs text and voice interfaces to understand customer intents.

D:

Amazon Translate: Translates text between languages. That does not meet the requirement: A travel site needs text and voice interfaces to understand customer intents.

Question 496

A travel site needs text and voice interfaces to understand customer intents. Which explanation correctly distinguishes the appropriate approach from the alternatives?

  1. Amazon Translate — translates text between languages
  2. Amazon Polly — converts text into lifelike speech
  3. Amazon Comprehend — detects meaning, entities, sentiment and other structure in text
  4. Amazon Lex — Lex builds conversational interfaces using speech recognition and natural language understanding

Explanation

Correct Answer

D:

Lex builds conversational interfaces using speech recognition and natural language understanding. This fits the situation: A travel site needs text and voice interfaces to understand customer intents.

Incorrect Answers

A:

Amazon Translate: Translates text between languages. This does not address the need for A travel site needs text and voice interfaces to understand customer intents. The appropriate Amazon Lex applies because Lex builds conversational interfaces using speech recognition and natural language understanding.

B:

Amazon Polly: Converts text into lifelike speech. This does not address the need for A travel site needs text and voice interfaces to understand customer intents. The appropriate Amazon Lex applies because Lex builds conversational interfaces using speech recognition and natural language understanding.

C:

Amazon Comprehend: Detects meaning, entities, sentiment and other structure in text. This does not address the need for A travel site needs text and voice interfaces to understand customer intents. The appropriate Amazon Lex applies because Lex builds conversational interfaces using speech recognition and natural language understanding.

Question 497

A technical reviewer evaluates this need: An analyst needs serverless SQL queries on datasets stored in Amazon S3. Which choice is most appropriate?

  1. Amazon Athena
  2. Amazon Redshift
  3. AWS Glue
  4. Amazon Kinesis

Explanation

Correct Answer

A:

Athena queries data directly in S3 with serverless SQL capabilities. This fits the case: An analyst needs serverless SQL queries on datasets stored in Amazon S3.

Incorrect Answers

B:

Amazon Redshift: Serves analytics warehousing SQL workloads. That does not meet the requirement: A technical reviewer evaluates this need: An analyst needs serverless SQL queries on datasets stored in Amazon S3.

C:

AWS Glue: Supports managed ETL and data catalog workflows. That does not meet the requirement: A technical reviewer evaluates this need: An analyst needs serverless SQL queries on datasets stored in Amazon S3.

D:

Amazon Kinesis: Kinesis services support collection and analysis of real-time streaming data. That does not meet the requirement: A technical reviewer evaluates this need: An analyst needs serverless SQL queries on datasets stored in Amazon S3.

Question 498

For the following need, select the most defensible technical rationale: An analyst needs serverless SQL queries on datasets stored in Amazon S3.

  1. Amazon Kinesis — Kinesis services support collection and analysis of real-time streaming data
  2. Amazon Athena — Athena queries data directly in S3 with serverless SQL capabilities
  3. AWS Glue — supports managed ETL and data catalog workflows
  4. Amazon Redshift — serves analytics warehousing SQL workloads

Explanation

Correct Answer

B:

Athena queries data directly in S3 with serverless SQL capabilities. This fits the situation: An analyst needs serverless SQL queries on datasets stored in Amazon S3.

Incorrect Answers

A:

Amazon Kinesis: Kinesis services support collection and analysis of real-time streaming data. This does not address the need for An analyst needs serverless SQL queries on datasets stored in Amazon S3. The appropriate Amazon Athena applies because Athena queries data directly in S3 with serverless SQL capabilities.

C:

AWS Glue: Supports managed ETL and data catalog workflows. This does not address the need for An analyst needs serverless SQL queries on datasets stored in Amazon S3. The appropriate Amazon Athena applies because Athena queries data directly in S3 with serverless SQL capabilities.

D:

Amazon Redshift: Serves analytics warehousing SQL workloads. This does not address the need for An analyst needs serverless SQL queries on datasets stored in Amazon S3. The appropriate Amazon Athena applies because Athena queries data directly in S3 with serverless SQL capabilities.

Question 499

A reporting team wants to inspect S3 logs using SQL without maintaining a database server. Identify the AWS approach that most directly meets the stated goal.

  1. Amazon Redshift
  2. Amazon Kinesis
  3. Amazon Athena
  4. AWS Glue

Explanation

Correct Answer

C:

Athena queries data directly in S3 with serverless SQL capabilities. This fits the case: A reporting team wants to inspect S3 logs using SQL without maintaining a database server.

Incorrect Answers

A:

Amazon Redshift: Serves analytics warehousing SQL workloads. That does not meet the requirement: A reporting team wants to inspect S3 logs using SQL without maintaining a database server.

B:

Amazon Kinesis: Kinesis services support collection and analysis of real-time streaming data. That does not meet the requirement: A reporting team wants to inspect S3 logs using SQL without maintaining a database server.

D:

AWS Glue: Supports managed ETL and data catalog workflows. That does not meet the requirement: A reporting team wants to inspect S3 logs using SQL without maintaining a database server.

Question 500

Which service-to-requirement explanation best fits this situation: A reporting team wants to inspect S3 logs using SQL without maintaining a database server?

  1. AWS Glue — supports managed ETL and data catalog workflows
  2. Amazon Kinesis — Kinesis services support collection and analysis of real-time streaming data
  3. Amazon Redshift — serves analytics warehousing SQL workloads
  4. Amazon Athena — Athena queries data directly in S3 with serverless SQL capabilities

Explanation

Correct Answer

D:

Athena queries data directly in S3 with serverless SQL capabilities. This fits the situation: A reporting team wants to inspect S3 logs using SQL without maintaining a database server.

Incorrect Answers

A:

AWS Glue: Supports managed ETL and data catalog workflows. This does not address the need for A reporting team wants to inspect S3 logs using SQL without maintaining a database server. The appropriate Amazon Athena applies because Athena queries data directly in S3 with serverless SQL capabilities.

B:

Amazon Kinesis: Kinesis services support collection and analysis of real-time streaming data. This does not address the need for A reporting team wants to inspect S3 logs using SQL without maintaining a database server. The appropriate Amazon Athena applies because Athena queries data directly in S3 with serverless SQL capabilities.

C:

Amazon Redshift: Serves analytics warehousing SQL workloads. This does not address the need for A reporting team wants to inspect S3 logs using SQL without maintaining a database server. The appropriate Amazon Athena applies because Athena queries data directly in S3 with serverless SQL capabilities.