AIF-C01 Applications of Foundation Models Practice Test 6 — 15 Questions

Question 1

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.. Option D is correct because 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.

Answer

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 2

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.. Option B is correct because 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.

Answer

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 3

A financial services company is evaluating Amazon Bedrock for a compliance application that requires explainable AI decisions. The model's output must be auditable and traceable to specific reasoning. Which Bedrock feature should they use to meet this requirement?

Accepted Answer

Enable model invocation logging in Amazon Bedrock and store logs in Amazon S3.. Option C is correct because model invocation logging records all requests and responses, enabling traceability. Option A is wrong because guardrails filter content but don't provide reasoning. Option B is wrong because custom models are still black boxes. Option D is wrong because knowledge bases are for retrieval, not reasoning traceability.

Answer

Create a knowledge base with financial regulations to guide the model.

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Fine-tune a custom model on regulatory documents to improve reasoning.

Answer

Amazon Bedrock Guardrails to filter sensitive content.

Question 4

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.. Option D is correct because 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.

Answer

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 5

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.. Option C is correct because 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.

Answer

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 6

Which TWO actions are best practices when deploying foundation models on Amazon SageMaker for production? (Choose TWO.)

Accepted Answer

Use multi-model endpoints (MMEs) to serve multiple models on a single instance.. Option C is correct because Amazon SageMaker Multi-Model Endpoints (MMEs) allow you to host multiple models on a single instance, which reduces hosting costs by sharing resources across models while still providing low-latency inference. This is a best practice for production deployments where you need to serve many models efficiently without provisioning separate endpoints for each.

Answer

Manually warm up endpoints by sending dummy requests before traffic spikes.

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Create a separate endpoint for each model to isolate traffic.

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Deploy models directly to production without load testing to avoid delays.

Question 7

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 provides built-in integration with AWS services like CloudWatch for monitoring model invocation metrics and CloudTrail for auditing API calls. This eliminates the need to manually set up logging and monitoring infrastructure when self-managing foundation models on EC2, where you would have to configure these integrations yourself.

Answer

Lower data transfer costs between cloud regions.

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Greater control over model fine-tuning and customization.

Question 8

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.. Structured pruning reduces the number of parameters in the model by removing entire neurons, channels, or layers that contribute little to the output. This directly shrinks the model size and computational requirements, leading to lower memory usage and faster inference on SageMaker, which reduces cost.

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 9

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 that can perform summarization. Option C is correct because Bedrock is a managed service offering pre-trained models for tasks like text summarization. Option A (Amazon Comprehend) is for NLP tasks like entity extraction, not summarization. Option B (Amazon Rekognition) is for image/video analysis. Option D (Amazon Polly) is text-to-speech.

Answer

Amazon Rekognition

Answer

Amazon Polly

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Amazon Comprehend

Question 10

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.

Answer

Increase the number of concurrent invocations

Answer

Switch to a smaller model

Answer

Decrease the maxTokens parameter

Question 11

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. Option D is correct because Retrieval Augmented Generation (RAG) provides external knowledge to ground responses. Option A (larger model) may not fix factual errors. Option B (lower temperature) can reduce randomness but not correct false facts. Option C (fine-tuning with company data) could help but requires curated dataset; RAG is more direct for factual accuracy.

Answer

Use a larger foundation model

Answer

Fine-tune the model on company data

Answer

Reduce the temperature to 0

Question 12

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

Answer

Use Amazon Bedrock with a multilingual foundation model

Answer

Use Amazon Transcribe and then Amazon Bedrock

Question 13

A healthcare company processes patient records using a foundation model on Amazon Bedrock. They must ensure no patient data is used to improve the base model. What is the MOST effective configuration?

Accepted Answer

Disable model improvement logging in the Bedrock service settings. Option C is correct because disabling model improvement logging ensures data is not used for training. Option A (data encryption) does not prevent usage for training. Option B (private endpoints) secures traffic but not data usage. Option D (retain logs) could still allow AWS to use data for model improvement.

Answer

Create a VPC endpoint for Bedrock

Answer

Set a data retention policy to store logs for only 30 days

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Enable data encryption at rest and in transit

Question 14

A team is fine-tuning a foundation model using SageMaker. They want to minimize training time while keeping the model's original knowledge. Which technique is BEST suited?

Accepted Answer

Use Parameter Efficient Fine-Tuning (PEFT) such as LoRA. Parameter Efficient Fine-Tuning (PEFT) methods like LoRA (Low-Rank Adaptation) are best suited because they freeze the pre-trained model weights and inject trainable low-rank matrices into specific layers, drastically reducing the number of trainable parameters. This minimizes training time and computational cost while preserving the model's original knowledge, as only a small fraction of parameters are updated during fine-tuning.

Answer

Use distributed training across multiple GPUs

Answer

Use prompt engineering instead of fine-tuning

Answer

Full fine-tuning on the new dataset

Question 15

A data scientist wants to quickly experiment with a pre-trained LLM for text generation without writing any code. Which AWS service is MOST suitable?

Accepted Answer

Amazon SageMaker JumpStart. Amazon SageMaker JumpStart provides a curated set of pre-trained foundation models (including LLMs) that can be deployed with just a few clicks, requiring no code. This makes it the most suitable service for a data scientist who wants to quickly experiment with a pre-trained LLM for text generation without writing any code.

Answer

Amazon Bedrock

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Amazon EC2

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AWS Lambda