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Amazon AWS Certified AI Practitioner Sample Questions (Q14-Q19):

NEW QUESTION # 14
A company wants to use language models to create an application for inference on edge devices. The inference must have the lowest latency possible.
Which solution will meet these requirements?

A. Deploy optimized small language models (SLMs) on edge devices.
B. Deploy optimized large language models (LLMs) on edge devices.
C. Incorporate a centralized small language model (SLM) API for asynchronous communication with edge devices.
D. Incorporate a centralized large language model (LLM) API for asynchronous communication with edge devices.

Answer: A

Explanation:
To achieve the lowest latency possible for inference on edge devices, deploying optimized small language models (SLMs) is the most effective solution. SLMs require fewer resources and havefaster inference times, making them ideal for deployment on edge devices where processing power and memory are limited.
Option A (Correct): "Deploy optimized small language models (SLMs) on edge devices": This is the correct answer because SLMs provide fast inference with low latency, which is crucial for edge deployments.
Option B: "Deploy optimized large language models (LLMs) on edge devices" is incorrect because LLMs are resource-intensive and may not perform well on edge devices due to their size and computational demands.
Option C: "Incorporate a centralized small language model (SLM) API for asynchronous communication with edge devices" is incorrect because it introduces network latency due to the need for communication with a centralized server.
Option D: "Incorporate a centralized large language model (LLM) API for asynchronous communication with edge devices" is incorrect for the same reason, with even greater latency due to the larger model size.
AWS AI Practitioner Reference:
Optimizing AI Models for Edge Devices on AWS: AWS recommends using small, optimized models for edge deployments to ensure minimal latency and efficient performance.

NEW QUESTION # 15
A company needs to choose a model from Amazon Bedrock to use internally. The company must identify a model that generates responses in a style that the company's employees prefer.
What should the company do to meet these requirements?

A. Evaluate the models by using built-in prompt datasets.
B. Use the model InvocationLatency runtime metrics in Amazon CloudWatch when trying models.
C. Use public model leaderboards to identify the model.
D. Evaluate the models by using a human workforce and custom prompt datasets.

Answer: D

Explanation:
To determine which model generates responses in a style that the company's employees prefer, the best approach is to use a human workforce to evaluate the models with custom prompt datasets. This method allows for subjective evaluation based on the specific stylistic preferences of the company's employees, which cannot be effectively assessed through automated methods or pre-built datasets.
Option B (Correct): "Evaluate the models by using a human workforce and custom prompt datasets": This is the correct answer as it directly involves human judgment to evaluate the style and quality of the responses, aligning with employee preferences.
Option A: "Evaluate the models by using built-in prompt datasets" is incorrect because built-in datasets may not capture the company's specific stylistic requirements.
Option C: "Use public model leaderboards to identify the model" is incorrect as leaderboards typically measure model performance on standard benchmarks, not on stylistic preferences.
Option D: "Use the model InvocationLatency runtime metrics in Amazon CloudWatch" is incorrect because latency metrics do not provide any information about the style of the model's responses.
AWS AI Practitioner Reference:
Model Evaluation Techniques on AWS: AWS suggests using human evaluators to assess qualitative aspects of model outputs, such as style and tone, to ensure alignment with organizational preferences

NEW QUESTION # 16
An animation company wants to provide subtitles for its content. Which AWS service meets this requirement?

A. Amazon Translate
B. Amazon Comprehend
C. Amazon Transcribe
D. Amazon Polly

Answer: C

Explanation:
Amazon Transcribe is the AWS service that converts speech to text, enabling the generation of subtitles (closed captions) for audio and video content automatically.
* C is correct:
"Amazon Transcribe is an automatic speech recognition (ASR) service that makes it easy for developers to add speech-to-text capability to applications." This feature supports creating subtitles and transcripts for media files.
(Reference: Amazon Transcribe Overview, AWS AI Practitioner Official Study Guide)
* A (Comprehend) is for NLP/text analytics.
* B (Polly) is text-to-speech.
* D (Translate) translates text, but does not create subtitles from audio/video.

NEW QUESTION # 17
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. Remove personally identifiable information (PII) from the customer data before fine-tuning the LLM.
B. Increase the Top-K parameter of the LLM.
C. Store customer data in Amazon S3. Encrypt the data before fine-tuning the LLM.
D. Use Amazon Bedrock Guardrails.

Answer: A

Explanation:
A: Amazon Bedrock Guardrails: Guardrails in Amazon Bedrock allow users to define policies to filter harmful or sensitive content in model inputs and outputs. While useful for real-time content moderation, they do not address the risk of private data being embedded in the model during fine-tuning, as the model could still memorize sensitive information.
B: Remove personally identifiable information (PII) from the customer data before fine-tuning the LLM: Removing PII (e.g., names, addresses, account numbers) from the training dataset ensures that the model does not learn or memorize sensitive customer data, reducing the risk of data leakage. This is a proactive and effective approach to data privacy during model training.
C: Increase the Top-K parameter of the LLM: The Top-K parameter controls the randomness of the model's output by limiting the number of tokens considered during generation. Adjusting this parameter affects output diversity but does not address the privacy of customer data embedded in the model.
D: Store customer data in Amazon S3. Encrypt the data before fine-tuning the LLM: Encrypting data in Amazon S3 protects data at rest and in transit, but during fine-tuning, the data is decrypted and used to train the model. If PII is present, the model could still learn and potentially expose it, so encryption alone does not solve the problem.
Exact Extract Reference: AWS emphasizes data privacy in AI/ML workflows, stating, "To protect sensitive data, you can preprocess datasets to remove personally identifiable information (PII) before using them for model training. This reduces the risk of models inadvertently learning or exposing sensitive information." (Source: AWS Best Practices for Responsible AI, https://aws.amazon.com/machine-learning/responsible-ai/). Additionally, the Amazon Bedrock documentation notes that users are responsible for ensuring compliance with data privacy regulations during fine-tuning (https://docs.aws.amazon.com/bedrock/latest/userguide/model-customization.html).
Removing PII before fine-tuning is the most direct and effective way to prevent the model from revealing private customer data, making B the correct answer.
Explanation:
The goal is to prevent a fine-tuned large language model (LLM) on Amazon Bedrock from revealing private customer data. Let's analyze the options:
Reference:
AWS Bedrock Documentation: Model Customization (https://docs.aws.amazon.com/bedrock/latest/userguide/model-customization.html) AWS Responsible AI Best Practices (https://aws.amazon.com/machine-learning/responsible-ai/) AWS AI Practitioner Study Guide (emphasis on data privacy in LLM fine-tuning)

NEW QUESTION # 18
A bank is building a chatbot to answer customer questions about opening a bank account. The chatbot will use public bank documents to generate responses. The company will use Amazon Bedrock and prompt engineering to improve the chatbot's responses.
Which prompt engineering technique meets these requirements?

A. Zero-shot prompting
B. Directional stimulus prompting
C. Few-shot prompting
D. Complexity-based prompting

Answer: B

NEW QUESTION # 19
......

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Amazon AWS Certified AI Practitioner Sample Questions (Q14-Q19):

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