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Free AWS Certified AI Practitioner Exam Practice Questions

Question # 31

A company wants to use an AI/ML service to build a conversational AI application.

Which AWS service will meet these requirements?

Options:

A.  

Amazon Comprehend

B.  

Amazon Lex

C.  

Amazon Polly

D.  

Amazon Translate

Discussion 0
Question # 32

A company is using an Amazon Nova Canvas model to generate images. The model generates images successfully. The company needs to prevent the model from including specific items in the generated images.

Which solution will meet this requirement?

Options:

A.  

Use a higher temperature value.

B.  

Use a more detailed prompt.

C.  

Use a negative prompt.

D.  

Use another foundation model (FM).

Discussion 0
Question # 33

A company wants to enhance response quality for a large language model (LLM) for complex problem-solving tasks. The tasks require detailed reasoning and a step-by-step explanation process.

Which prompt engineering technique meets these requirements?

Options:

A.  

Few-shot prompting

B.  

Zero-shot prompting

C.  

Directional stimulus prompting

D.  

Chain-of-thought prompting

Discussion 0
Question # 34

A company creates video content. The company wants to use generative AI to generate new creative content and to reduce video creation time. Which solution will meet these requirements in the MOST operationally efficient way?

Options:

A.  

Use the Amazon Titan Image Generator model on Amazon Bedrock to generate intermediate images. Use video editing software to create videos.

B.  

Use the Amazon Nova Canvas model on Amazon Bedrock to generate intermediate images. Use video editing software to create videos.

C.  

Use the Amazon Nova Reel model on Amazon Bedrock to generate videos.

D.  

Use the Amazon Nova Pro model on Amazon Bedrock to generate videos.

Discussion 0
Question # 35

A company is developing a new model to predict the prices of specific items. The model performed well on the training dataset. When the company deployed the model to production, the model ' s performance decreased significantly.

What should the company do to mitigate this problem?

Options:

A.  

Reduce the volume of data that is used in training.

B.  

Add hyperparameters to the model.

C.  

Increase the volume of data that is used in training.

D.  

Increase the model training time.

Discussion 0
Question # 36

Which AWS feature records details about ML instance data for governance and reporting?

Options:

A.  

Amazon SageMaker Model Cards

B.  

Amazon SageMaker Debugger

C.  

Amazon SageMaker Model Monitor

D.  

Amazon SageMaker JumpStart

Discussion 0
Question # 37

A financial company is developing a generative AI application for loan approval decisions. The company needs the application output to be responsible and fair.

Options:

A.  

Review the training data to check for biases. Include data from all demographics in the training data.

B.  

Use a deep learning model with many hidden layers.

C.  

Keep the model ' s decision-making process a secret to protect proprietary algorithms.

D.  

Continuously monitor the model ' s performance on a static test dataset.

Discussion 0
Question # 38

Which functionality does Amazon SageMaker Clarify provide?

Options:

A.  

Integrates a Retrieval Augmented Generation (RAG) workflow

B.  

Monitors the quality of ML models in production

C.  

Documents critical details about ML models

D.  

Identifies potential bias during data preparation

Discussion 0
Question # 39

A financial company is developing a generative AI application for loan approval decisions. The company needs the application output to be responsible and fair.

Which solution meets these requirements?

Options:

A.  

Review the training data to check for biases. Include data from all demographics in the training data.

B.  

Use a deep learning model with many hidden layers.

C.  

Keep the model ' s decision-making process a secret to protect proprietary algorithms.

D.  

Continuously monitor the model’s performance on a static test dataset.

Discussion 0
Question # 40

What is the benefit of fine-tuning a foundation model (FM)?

Options:

A.  

Fine-tuning reduces the FM ' s size and complexity and enables slower inference.

B.  

Fine-tuning uses specific training data to retrain the FM from scratch to adapt to a specific use case.

C.  

Fine-tuning keeps the FM ' s knowledge up to date by pre-training the FM on more recent data.

D.  

Fine-tuning improves the performance of the FM on a specific task by further training the FM on new labeled data.

Discussion 0

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