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

Question 1

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 2

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.

Answer

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 3

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 4

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.. Retrieval-Augmented Generation (RAG) with a knowledge base on Amazon Bedrock grounds the model's responses in authoritative, up-to-date documents, dramatically reducing hallucinations. Pairing RAG with a system prompt that demands factual, sourced answers further constrains the model. This combination directly addresses the requirement to minimize hallucinations and ensure factual accuracy for patient triage.

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 5

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 6

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 7

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 8

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.

Answer

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 9

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.

Question 10

Which THREE are benefits of using Amazon Bedrock over self-managing foundation models on EC2? (Choose THREE.)

Accepted Answer

Built-in integration with AWS services such as AWS CloudWatch and AWS CloudTrail.. Option A is correct because Amazon Bedrock natively integrates with AWS monitoring and auditing services like CloudWatch (for metrics/logs) and CloudTrail (for API activity logging), giving visibility and compliance without custom instrumentation. Option C is correct because Bedrock provides a curated catalog of foundation models from multiple providers (e.g., Anthropic, AI21, Stability AI, Amazon Titan, Meta) through a single API, which self-managing on EC2 would require sourcing and integrating individually. Option D is correct because Bedrock is a fully managed service that handles model hosting, provisioning, and automatic scaling of inference capacity, removing the operational burden of managing EC2 instances, AMIs, autoscaling groups, and GPU capacity. Option B is not correct because Bedrock does not inherently reduce inter-region data transfer costs; those are governed by standard AWS data transfer pricing and are unrelated to the choice between Bedrock and self-managed EC2. Option E is not correct because self-managing foundation models on EC2 generally offers greater control over fine-tuning and customization, whereas Bedrock provides more constrained, managed customization options.

Answer

Lower data transfer costs between cloud regions.

Answer

Greater control over model fine-tuning and customization.

Question 11

Which TWO techniques can reduce the cost of running a fine-tuned foundation model on Amazon SageMaker? (Choose TWO.)

Accepted Answer

Implement structured pruning to remove less important model parameters.. Option A is correct because structured pruning removes less important weights, neurons, or channels from the fine-tuned model, producing a smaller model that requires fewer compute and memory resources during SageMaker inference, which directly lowers hosting cost. Option C is correct because quantization reduces numerical precision from FP32 to FP16 or INT8, shrinking model size and memory bandwidth needs and enabling faster, cheaper inference on SageMaker endpoints, especially with GPU instances that support lower-precision arithmetic. Option B is not correct because using larger GPU instances increases the hourly cost of the endpoint rather than reducing it. Option D is not correct because keeping parameters in FP32 preserves accuracy but consumes more memory and compute, raising cost. Option E is not correct because increasing training epochs affects training time and accuracy, not the cost of running inference on the deployed model.

Answer

Use larger instance types with more GPUs to speed up inference.

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Store the model parameters in FP32 to maintain accuracy during inference.

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Increase the number of training epochs to achieve higher accuracy.

Question 12

A company wants to automatically summarize customer support tickets into a short paragraph. Which AWS service is MOST appropriate for this task?

Accepted Answer

Amazon Bedrock. Amazon Bedrock provides access to foundation models (FMs) from providers like Anthropic and AI21 that excel at natural language generation tasks, including summarization. By invoking a model such as Claude or Jurassic-2 via Bedrock's API, you can pass the customer support ticket text and receive a concise paragraph summary. This makes Bedrock the most appropriate service for generative summarization.

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Amazon Rekognition

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Amazon Polly

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Amazon Comprehend

Question 13

A company runs a chatbot using a large language model on Amazon Bedrock. They notice high latency during peak hours. Which action would be MOST effective to reduce latency without degrading response quality?

Accepted Answer

Use Provisioned Throughput for model inference. Provisioned Throughput on Amazon Bedrock reserves dedicated capacity for model inference, ensuring consistent low latency even during peak hours. This eliminates the variability caused by resource contention in the on-demand tier, directly addressing high latency without altering model size or output quality.

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Increase the number of concurrent invocations

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Switch to a smaller model

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Decrease the maxTokens parameter

Question 14

A generative AI application occasionally produces factually incorrect responses. The team has already tried prompt engineering and increasing the temperature parameter. Which next step is MOST effective to improve factual accuracy?

Accepted Answer

Implement a Retrieval Augmented Generation (RAG) pipeline. Retrieval Augmented Generation (RAG) is the most effective next step because it grounds the model's responses in a verified external knowledge base, directly addressing factual inaccuracies without requiring retraining. Unlike prompt engineering or temperature adjustments, RAG provides real-time access to authoritative documents, reducing hallucinations by constraining the model's output to retrieved evidence.

Answer

Use a larger foundation model

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Fine-tune the model on company data

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Reduce the temperature to 0

Question 15

A startup needs to build a real-time text translation feature for a customer chat application. Latency must be under 200 ms per request. Which AWS approach is BEST suited?

Accepted Answer

Use Amazon Translate with real-time translation. Amazon Translate's real-time translation API is purpose-built for low-latency text translation, typically achieving sub-200 ms response times for small payloads. It directly translates text without the overhead of running a large foundation model or performing intermediate steps like transcription, making it the best fit for this latency-sensitive chat application.

Answer

Use Amazon Comprehend for language detection and a custom translation model

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Use Amazon Bedrock with a multilingual foundation model

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Use Amazon Transcribe and then Amazon Bedrock