Amazon AIF-C01 Exam Dumps

Amazon AIF-C01 Sample Question Answers

Amazon AIF-C01 Sample Questions

Question # 1

A bank is fine-tuning a large language model (LLM) on Amazon Bedrock to assist customers with questions about their loans. The bank wants to ensure that the model does not reveal any private customer data.Which solution meets these requirements?

A. Use Amazon Bedrock Guardrails.
B. Remove personally identifiable information (PII) from the customer data before fine-tuning the LLM.
C. Increase the Top-K parameter of the LLM.
D. Store customer data in Amazon S3. Encrypt the data before fine-tuning the LLM.

Answer: B

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Question # 2

Sentiment analysis is a subset of which broader field of AI?

A. Computer vision
B. Robotics
C. Natural language processing (NLP)
D. Time series forecasting

Answer: C

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Question # 3

Which prompting technique can protect against prompt injection attacks?

A. Adversarial prompting
B. Zero-shot prompting
C. Least-to-most prompting
D. Chain-of-thought prompting

Answer: A

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Question # 4

A digital devices company wants to predict customer demand for memory hardware. The company does not have coding experience or knowledge of ML algorithms and needs to develop a data-driven predictive model. The company needs to perform analysis on internal data and external data.Which solution will meet these requirements?

A. Store the data in Amazon S3. Create ML models and demand forecast predictions by using Amazon SageMaker built-in algorithms that use the data from Amazon S3.
B. Import the data into Amazon SageMaker Data Wrangler. Create ML models and demand forecast predictions by using SageMaker built-in algorithms.
C. Import the data into Amazon SageMaker Data Wrangler. Build ML models and demand forecast predictions by using an Amazon Personalize Trending-Now recipe.

Answer: D

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Question # 5

A company that streams media is selecting an Amazon Nova foundation model (FM) to process documents and images. The company is comparing Nova Micro and Nova Lite. The company wants to minimize costs.

A. Nova Micro uses transformer-based architectures. Nova Lite does not use transformer-based architectures.
B. Nova Micro supports only text data. Nova Lite is optimized for numerical data.
C. Nova Micro supports only text. Nova Lite supports images, videos, and text.
D. Nova Micro runs only on CPUs. Nova Lite runs only on GPUs.

Answer: C

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Question # 6

A company is building an AI application to summarize books of varying lengths. During testing, the application fails to summarize some books. Why does the application fail to summarize some books?

A. The temperature is set too high.
B. The selected model does not support fine-tuning.
C. The Top P value is too high.
D. The input tokens exceed the model's context size.

Answer: D

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Question # 7

A company wants to identify harmful language in the comments section of social media posts by using an ML model. The company will not use labeled data to train the model. Which strategy should the company use to identify harmful language?

A. Use Amazon Rekognition moderation.
B. Use Amazon Comprehend toxicity detection.
C. Use Amazon SageMaker AI built-in algorithms to train the model.
D. Use Amazon Polly to monitor comments.

Answer: B

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Question # 8

A social media company wants to use a large language model (LLM) for content moderation. The company wants to evaluate the LLM outputs for bias and potential discrimination against specific groups or individuals.Which data source should the company use to evaluate the LLM outputs with the LEAST administrative effort?

A. User-generated content
B. Moderation logs
C. Content moderation guidelines
D. Benchmark datasets

Answer: D

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Question # 9

A company that uses multiple ML models wants to identify changes in original model quality so that the company can resolve any issues.Which AWS service or feature meets these requirements?

A. Amazon SageMaker JumpStart
B. Amazon SageMaker HyperPod
C. Amazon SageMaker Data Wrangler
D. Amazon SageMaker Model Monitor

Answer: D

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Question # 10

A company wants to use a pre-trained generative AI model to generate content for its marketing campaigns. The company needs to ensure that the generated content aligns with the company's brand voice and messaging requirements.Which solution meets these requirements?

A. Optimize the model's architecture and hyperparameters to improve the model's overall performance.
B. Increase the model's complexity by adding more layers to the model's architecture.
C. Create effective prompts that provide clear instructions and context to guide the model's generation.
D. Select a large, diverse dataset to pre-train a new generative model.

Answer: C

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Question # 11

A company acquires International Organization for Standardization (ISO) accreditation to manage AI risks and to use AI responsibly. What does this accreditation certify?

A. All members of the company are ISO certified.
B. All AI systems that the company uses are ISO certified.
C. All AI application team members are ISO certified.
D. The company’s development framework is ISO certified.

Answer: D

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Question # 12

A company wants to use a large language model (LLM) on Amazon Bedrock for sentiment analysis. The company wants to know how much information can fit into one prompt.Which consideration will inform the company's decision?

A. Temperature
B. Context window
C. Batch size
D. Model size

Answer: B

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Question # 13

A company wants to label training datasets by using human feedback to fine-tune a foundation model (FM). The company does not want to develop labeling applications or manage a labeling workforce. Which AWS service or feature meets these requirements?

A. Amazon SageMaker Data Wrangler
B. Amazon SageMaker Ground Truth Plus
C. Amazon Transcribe
D. Amazon Macie

Answer: B

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Question # 14

A bank has fine-tuned a large language model (LLM) to expedite the loan approval process. During an external audit of the model, the company discovered that the model was approving loans at a faster pace for a specific demographic than for other demographics.How should the bank fix this issue MOST cost-effectively?

A. Include more diverse training data. Fine-tune the model again by using the new data.
B. Use Retrieval Augmented Generation (RAG) with the fine-tuned model.
C. Use AWS Trusted Advisor checks to eliminate bias.
D. Pre-train a new LLM with more diverse training data.

Answer: A

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Question # 15

Which scenario describes a potential risk and limitation of prompt engineering In the context of a generative AI model?

A. Prompt engineering does not ensure that the model always produces consistent and deterministic outputs, eliminating the need for validation.
B. Prompt engineering could expose the model to vulnerabilities such as prompt injection attacks.
C. Properly designed prompts reduce but do not eliminate the risk of data poisoning or model hijacking.
D. Prompt engineering does not ensure that the model will consistently generate highly reliable outputs when working with real-world data.

Answer: B

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