AIF-C01 Free Practice Test

A company wants to use a large language model (LLM) on Amazon Bedrock for sentiment analysis. The company wants to classify the sentiment of text passages as positive or negative.
Which prompt engineering strategy meets these requirements?

1. A. Provide examples of text passages with corresponding positive or negative labels in the prompt followed by the new text passage to be classified.
2. B. Provide a detailed explanation of sentiment analysis and how LLMs work in the prompt.
3. C. Provide the new text passage to be classified without any additional context or examples.
4. D. Provide the new text passage with a few examples of unrelated tasks, such as text summarization or question answering.

Correct Answer: A
Providing examples of text passages with corresponding positive or negative labels in the prompt followed by the new text passage to be classified is the correct prompt engineering strategy for using a large language model (LLM) on Amazon Bedrock for sentiment analysis.
✑ Example-Driven Prompts:
✑ Why Option A is Correct:
✑ Why Other Options are Incorrect:

A company wants to make a chatbot to help customers. The chatbot will help solve technical problems without human intervention. The company chose a foundation model (FM) for the chatbot. The chatbot needs to produce responses that adhere to company tone.
Which solution meets these requirements?

1. A. Set a low limit on the number of tokens the FM can produce.
2. B. Use batch inferencing to process detailed responses.
3. C. Experiment and refine the prompt until the FM produces the desired responses.
4. D. Define a higher number for the temperature parameter.

Correct Answer: C
Experimenting and refining the prompt is the best approach to ensure that the chatbot using a foundation model (FM) produces responses that adhere to the company's tone.
✑ Prompt Engineering:
✑ Why Option C is Correct:
✑ Why Other Options are Incorrect:

A company is implementing the Amazon Titan foundation model (FM) by using Amazon Bedrock. The company needs to supplement the model by using relevant data from the company's private data sources.
Which solution will meet this requirement?

1. A. Use a different FM
2. B. Choose a lower temperature value
3. C. Create an Amazon Bedrock knowledge base
4. D. Enable model invocation logging

Correct Answer: C
Creating an Amazon Bedrock knowledge base allows the integration of external or private data sources with a foundation model (FM) like Amazon Titan. This integration helps supplement the model with relevant data from the company's private data sources to enhance its responses.
✑ Option C (Correct): "Create an Amazon Bedrock knowledge base": This is the
correct answer as it enables the company to incorporate private data into the FM to improve its effectiveness.
✑ Option A: "Use a different FM" is incorrect because it does not address the need to
supplement the current model with private data.
✑ Option B: "Choose a lower temperature value" is incorrect as it affects output randomness, not the integration of private data.
✑ Option D: "Enable model invocation logging" is incorrect because logging does not help in supplementing the model with additional data.
AWS AI Practitioner References:
✑ Amazon Bedrock and Knowledge Integration: AWS explains how creating a knowledge base allows Amazon Bedrock to use external data sources to improve the FM??s relevance and accuracy.

A company is using Amazon SageMaker Studio notebooks to build and train ML models. The company stores the data in an Amazon S3 bucket. The company needs to manage the flow of data from Amazon S3 to SageMaker Studio notebooks.
Which solution will meet this requirement?

1. A. Use Amazon Inspector to monitor SageMaker Studio.
2. B. Use Amazon Macie to monitor SageMaker Studio.
3. C. Configure SageMaker to use a VPC with an S3 endpoint.
4. D. Configure SageMaker to use S3 Glacier Deep Archive.

Correct Answer: C
To manage the flow of data from Amazon S3 to SageMaker Studio notebooks securely, using a VPC with an S3 endpoint is the best solution.
✑ Amazon SageMaker and S3 Integration:
✑ Why Option C is Correct:
✑ Why Other Options are Incorrect:

A company has built a solution by using generative AI. The solution uses large language models (LLMs) to translate training manuals from English into other languages. The company wants to evaluate the accuracy of the solution by examining the text generated for the manuals.
Which model evaluation strategy meets these requirements?

1. A. Bilingual Evaluation Understudy (BLEU)
2. B. Root mean squared error (RMSE)
3. C. Recall-Oriented Understudy for Gisting Evaluation (ROUGE)
4. D. F1 score

Correct Answer: A
BLEU (Bilingual Evaluation Understudy) is a metric used to evaluate the accuracy of machine-generated translations by comparing them against reference translations. It is commonly used for translation tasks to measure how close the generated output is to professional human translations.
✑ Option A (Correct): "Bilingual Evaluation Understudy (BLEU)": This is the correct answer because BLEU is specifically designed to evaluate the quality of translations, making it suitable for the company's use case.
✑ Option B: "Root mean squared error (RMSE)" is incorrect because RMSE is used for regression tasks to measure prediction errors, not translation quality.
✑ Option C: "Recall-Oriented Understudy for Gisting Evaluation (ROUGE)" is incorrect as it is used to evaluate text summarization, not translation.
✑ Option D: "F1 score" is incorrect because it is typically used for classification tasks, not for evaluating translation accuracy.
AWS AI Practitioner References:
✑ Model Evaluation Metrics on AWS: AWS supports various metrics like BLEU for specific use cases, such as evaluating machine translation models.