AIF-C01 Applications of Foundation Models Practice Test 5 — 15 Questions

Question 1

A company fine-tunes a foundation model on SageMaker using a custom dataset. They notice the training job takes too long. Which optimization technique is specifically designed to reduce training time for foundation models?

Accepted Answer

Distributed training using SageMaker Data Parallelism. SageMaker Data Parallelism distributes the training workload across multiple GPUs or instances, splitting the data and synchronizing gradients using optimized all-reduce algorithms. This specifically reduces training time for large foundation models by enabling parallel computation, which is the most direct technique for accelerating training at scale.

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Using a smaller instance type

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Using Spot Instances

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Reducing batch size

Question 2

An enterprise deploys a foundation model on Amazon Bedrock with a knowledge base. Users report that the model is returning outdated information. What is the most likely cause?

Accepted Answer

The knowledge base data source is not refreshed. When a knowledge base is attached to a foundation model on Amazon Bedrock, the model retrieves information from the data source to augment its responses. If the data source is not refreshed, the model will return outdated information even if the model itself is current. Option C directly addresses this by identifying the stale data source as the root cause.

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The model was fine-tuned

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The model is not the latest version

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The inference parameters are incorrect

Question 3

A developer wants to experiment with a foundation model for code generation without writing any code. Which AWS service provides a playground for models like CodeWhisperer?

Accepted Answer

Amazon Bedrock Playground. Amazon Bedrock Playground is the correct answer because it provides a web-based interface for experimenting with foundation models (FMs) from providers like AI21 Labs, Anthropic, Cohere, Meta, Stability AI, and Amazon itself, including the CodeWhisperer model for code generation. This allows the developer to test prompts, adjust parameters, and see model responses without writing any code, directly fulfilling the requirement of a no-code playground.

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Amazon CodeGuru

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AWS Lambda

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Amazon SageMaker Studio

Question 4

A company is using Amazon Bedrock to generate images from text prompts. They need to ensure the generated images do not contain offensive content. Which feature should be enabled?

Accepted Answer

Content moderation with AI. Amazon Bedrock includes built-in content moderation that can filter harmful content in inputs and outputs. IAM policies (B) control access but not content. WAF (C) protects web applications. VPC endpoints (D) secure network traffic.

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VPC endpoints

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AWS WAF

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IAM policies

Question 5

An organization uses SageMaker JumpStart to deploy a foundation model for real-time inference. They observe high latency. What is the most effective way to reduce latency?

Accepted Answer

Compile the model with SageMaker Neo. SageMaker Neo compiles the model to optimize it for the target hardware, reducing inference latency by applying hardware-specific optimizations such as kernel fusion, quantization, and memory layout tuning. This directly addresses the high latency issue for real-time inference without changing the instance type or inference mode.

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Use a larger instance with more memory

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Use batch transform instead

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Enable SageMaker Inference Recommender

Question 6

A company is building a chatbot using Amazon Bedrock. They want to provide up-to-date information from a continuously changing database. Which TWO services can be used as a data source for a Bedrock knowledge base? (Select TWO.)

Accepted Answer

Amazon Kendra. Amazon Bedrock knowledge bases can directly ingest data from Amazon S3, which is a supported data source for indexing documents. Amazon Kendra is also a supported data source, allowing Bedrock to leverage existing Kendra indexes for retrieval-augmented generation (RAG). Both services integrate natively with Bedrock knowledge bases to provide up-to-date information from continuously changing data.

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Amazon RDS for MySQL

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Amazon DynamoDB

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AWS Glue

Question 7

A data scientist is fine-tuning a foundation model on SageMaker. They want to prevent overfitting. Which THREE actions can help? (Select THREE.)

Accepted Answer

Apply dropout. Option A is correct because dropout is a regularization technique that randomly drops a fraction of neurons during training, which prevents the model from relying too heavily on specific features and reduces overfitting. In SageMaker, dropout can be applied via framework-specific APIs (e.g., `tf.keras.layers.Dropout` in TensorFlow) or by configuring the model architecture in the training script.

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Increase the number of epochs

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Use a smaller learning rate

Question 8

Which TWO of the following are benefits of using Amazon Bedrock for foundation models compared to managing your own infrastructure? (Select TWO.)

Accepted Answer

Access to multiple foundation models. Amazon Bedrock provides a serverless experience (A) so you don't manage infrastructure, and it offers access to multiple foundation models (C) from a single API. Full control over model weights (B) is not possible as Bedrock is managed. Higher throughput (D) is not guaranteed. Built-in content moderation (E) is a feature but not a primary benefit over managed infrastructure.

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Higher throughput for custom models

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Built-in content moderation

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Full control over model weights

Question 9

A developer receives the above response from invoking a Bedrock model. Which field indicates that the model completed its response normally?

Accepted Answer

stop_reason. The `stop_reason` field in the Bedrock response indicates why the model stopped generating text. A value of `"stop"` or `"end_turn"` (depending on the model) signals that the model completed its response normally, as opposed to hitting a token limit, content filter, or other interruption.

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output

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text

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role

Question 10

A developer encounters the error shown above when using Amazon Bedrock. What is the most likely cause?

Accepted Answer

The IAM role lacks the required permission. The error indicates an access denied or authorization failure when invoking the Amazon Bedrock model. The most likely cause is that the IAM role used by the developer does not have the required permission, such as `bedrock:InvokeModel`, attached to its policy. Without this permission, the API call to Bedrock is rejected regardless of model availability or service status.

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The model is not available in the region

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The request is throttled

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The model is out of service

Question 11

A developer sends the above request to Amazon Bedrock with Anthropic Claude. The model returns a response that stops before reaching 500 tokens. What is the most likely reason?

Accepted Answer

The model reached a stop sequence. The model stopped before reaching 500 tokens because the request likely included a stop sequence (e.g., `

Human:` or a custom stop token) that matched the generated output. When a stop sequence is encountered, Bedrock immediately halts generation, even if the token limit has not been reached. This is the most direct explanation for a premature stop.

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The temperature is set too high

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The model is not trained on this topic

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The token limit is exceeded

Question 12

A company uses Amazon Bedrock to generate summarizations of lengthy reports. Users report that the summaries are too verbose and include excessive detail. Which prompt engineering technique should the team apply to address this issue?

Accepted Answer

Add explicit constraints like 'Provide a concise summary in two sentences.'. Option D is correct because adding explicit constraints like 'Provide a concise summary in two sentences' directly instructs the model to limit verbosity and detail. This prompt engineering technique uses clear, specific instructions to control output length and style, which is the most effective way to address overly verbose summaries without altering model parameters or input data.

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Reduce the input context length to limit available information.

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Increase the maxTokens parameter in the inference request.

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Include few-shot examples of desired outputs.

Question 13

A healthcare company is deploying a conversational AI using a foundation model on Amazon Bedrock for patient triage. The application must minimize hallucinations and ensure factual accuracy. Which combination of techniques should the team implement?

Accepted Answer

Implement Retrieval-Augmented Generation (RAG) using a knowledge base on Amazon Bedrock and a system prompt demanding factual responses.. Option C is correct because RAG grounds responses in retrieved documents, and system prompts can enforce safety and accuracy constraints. Option A is wrong because fine-tuning alone may still lead to hallucinations if the training data is incomplete. Option B is wrong because RLHF is complex to implement on Bedrock and doesn't directly ground responses. Option D is wrong because reducing max tokens does not improve accuracy.

Answer

Fine-tune the model on a large dataset of medical transcripts and deploy with default parameters.

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Use reinforcement learning from human feedback (RLHF) on the deployed model.

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Set the maxTokens to a low value to force shorter, more focused answers.

Question 14

A data science team is fine-tuning a Llama 2 7B model on Amazon SageMaker for a text classification task. After the first training run, they notice the loss is not decreasing and the model is overfitting to the small training set. What should the team change to mitigate overfitting?

Accepted Answer

Add dropout layers and reduce the learning rate.. Option D is correct because increasing dropout and reducing learning rate are standard regularization techniques. Option A is wrong because increasing batch size can slightly regularize but often insufficiently. Option B is wrong because increasing epochs typically worsens overfitting. Option C is wrong because removing dropout reduces regularization, worsening overfitting.

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Increase the number of epochs to allow the model to learn more patterns.

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Increase the batch size and use gradient accumulation.

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Remove dropout layers from the model architecture.

Question 15

An e-commerce company uses Amazon Bedrock to generate product descriptions from keywords. Some descriptions contain inaccurate details about product specifications. Which approach should the company take to reduce factual errors?

Accepted Answer

Include the product specifications in the prompt and instruct the model to base the description on the provided data.. Option D is correct because providing the product specifications directly in the prompt and instructing the model to base the description on that data grounds the generation in factual information, reducing hallucinations. This technique, known as prompt engineering with in-context learning, ensures the model uses the given data rather than relying on its training data, which may contain inaccuracies.

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Increase the maxTokens parameter to allow more detailed descriptions.

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Use a different foundation model from Bedrock for each product category.

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Deploy the model to a SageMaker endpoint and use human-in-the-loop validation.