AIF-C01 Applications of Foundation Models Practice Test 7 — 15 Questions

Question 1

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 2

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's content moderation with AI feature allows you to filter generated images for offensive or inappropriate content by applying predefined or custom moderation policies. This is the correct choice because it directly addresses the requirement to ensure generated images do not contain offensive material, leveraging machine learning models to detect and block such content at inference time.

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VPC endpoints

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AWS WAF

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IAM policies

Question 3

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 4

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 5

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. 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 6

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 7

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 8

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 9

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.'. 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 10

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.. Adding dropout layers introduces regularization by randomly dropping neurons during training, which prevents the model from relying too heavily on specific features and reduces overfitting. Reducing the learning rate helps the model converge more smoothly and avoid oscillating around a suboptimal minimum, which is especially important when fine-tuning a large model like Llama 2 7B on a small dataset. Together, these changes address the core issues of overfitting and non-decreasing loss.

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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 11

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.. 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.

Question 12

A media company is using Amazon Bedrock to generate marketing copy with a foundation model. They want to ensure the output adheres to brand voice guidelines (e.g., friendly, professional). Which prompt engineering strategy is most effective for this requirement?

Accepted Answer

Use a system prompt that explicitly describes the brand voice and expectations.. Amazon Bedrock supports system prompts that set overarching context and behavioral guidelines for the model. By explicitly describing the brand voice (e.g., 'friendly, professional') in the system prompt, the model consistently applies these constraints across all user interactions, which is more effective than per-instruction tuning.

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Provide five example outputs in the prompt that match the desired tone.

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Include instructions like 'Do not use technical jargon' in every user prompt.

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Set the temperature parameter to a low value (e.g., 0.1) to reduce randomness.

Question 13

A startup is deploying a foundation model on Amazon SageMaker for real-time inference. They notice high latency (over 2 seconds per request). Which action is most likely to reduce latency?

Accepted Answer

Switch to a smaller, distilled version of the model.. Using a smaller, distilled version of the model directly reduces the computational complexity per inference request. Distillation compresses the model by training a smaller student network to mimic a larger teacher model, resulting in fewer parameters and faster forward passes. This is the most direct way to cut latency when the model size is the bottleneck, as it reduces the number of floating-point operations (FLOPs) required per request.

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Enable auto-scaling on the SageMaker endpoint to handle more concurrent requests.

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Deploy the model on a CPU-based instance instead of GPU.

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Increase the batch size parameter in the inference request.

Question 14

A research team is using Amazon Bedrock to analyze scientific papers. They want the model to generate answers based only on papers published after 2023. Which approach should they use?

Accepted Answer

Use Amazon Bedrock Knowledge Bases with a metadata filter to retrieve only papers published after 2023, and generate responses based on retrieved content.. Amazon Bedrock Knowledge Bases with a metadata filter allows you to restrict retrieval to only documents that match specific metadata criteria, such as publication year. By filtering the vector search to only include papers published after 2023, the model generates responses based solely on that retrieved content, ensuring it does not rely on pre-2023 data. This approach is the only one that guarantees the model's answers are grounded exclusively in the specified time range.

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Fine-tune the model on a dataset of post-2023 papers and deploy it.

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Set the maxTokens to a low value to force the model to rely on recent context.

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Include a system prompt instructing the model to ignore data before 2023.

Question 15

A company is using Amazon Bedrock to generate code snippets. Developers report that the generated code sometimes contains security vulnerabilities. Which action should the team take to mitigate this risk?

Accepted Answer

Add a system prompt that instructs the model to follow security best practices and avoid known vulnerabilities.. Adding a system prompt that instructs the model to follow security best practices and avoid known vulnerabilities directly influences the model's output at inference time. Amazon Bedrock supports system prompts that act as high-level instructions to guide the foundation model's behavior, making this a proactive, scalable mitigation that does not require manual intervention or architectural changes.

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Deploy the model in a sandbox environment to limit its access to sensitive systems.

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Implement a manual code review process after generation.

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Reduce the temperature parameter to 0 to make the output deterministic.