AIF-C01 Applications of Foundation Models Practice Test 8 — 15 Questions

Question 1

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.. 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.

Answer

Create a separate endpoint for each model to isolate traffic.

Answer

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

Question 2

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.. 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.

Answer

Greater control over model fine-tuning and customization.

Question 3

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.

Answer

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

Answer

Increase the number of training epochs to achieve higher accuracy.

Question 4

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 5

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 6

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 7

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

Answer

Amazon EC2

Answer

AWS Lambda

Question 8

A team deployed a text generation model on Amazon Bedrock. They want to monitor for toxic content in model outputs. Which evaluation approach is MOST effective?

Accepted Answer

Use Amazon Bedrock Model Evaluation with toxicity metrics. Amazon Bedrock Model Evaluation with toxicity metrics is the most effective approach because it provides automated, built-in evaluation of model outputs for toxic content using predefined metrics, directly integrated with the Bedrock service. This eliminates the need for manual effort or custom filtering, ensuring consistent and scalable monitoring of harmful content.

Answer

Enable CloudWatch Logs and set a metric filter for toxic words

Answer

Use Amazon SageMaker Ground Truth for human annotation

Answer

Manually review a sample of outputs each week

Question 9

A company is building a multi-modal application that processes images and text to answer questions about product defects. Which foundation model approach is BEST?

Accepted Answer

Use a multi-modal foundation model that processes both images and text. Multi-modal foundation models (e.g., CLIP, Flamingo, GPT-4V) are specifically designed to jointly process and reason over images and text in a unified architecture. This allows the model to directly correlate visual defects with textual descriptions without intermediate lossy transformations, making it the most effective approach for a multi-modal QA task.

Answer

Use an image captioning model and then analyze the caption text

Answer

Use a text-to-image generation model and analyze the generated image

Answer

Use a separate image analysis model and a text model, then combine outputs

Question 10

Refer to the exhibit. The training job is failing with an error 'CUDA out of memory'. Which hyperparameter change is MOST likely to resolve the issue?

Accepted Answer

Reduce per_device_train_batch_size to 4. The 'CUDA out of memory' error indicates that the GPU memory is exhausted during training. Reducing `per_device_train_batch_size` directly decreases the number of samples processed simultaneously per GPU, which lowers memory consumption for activations, gradients, and optimizer states. This is the most direct and effective hyperparameter change to resolve an out-of-memory condition.

Answer

Increase the number of epochs to 10

Answer

Increase learning_rate to 5e-4

Answer

Increase max_seq_length to 1024

Question 11

Refer to the exhibit. A developer sees this error when calling Amazon Bedrock for inference. What is the MOST likely cause and recommended solution?

Accepted Answer

The request rate exceeds the model's throughput limit; implement retries with exponential backoff. The error indicates a throttling exception from Amazon Bedrock, which occurs when the request rate exceeds the model's throughput limit. The recommended solution is to implement retries with exponential backoff to handle transient rate limits gracefully, as this aligns with AWS best practices for managing API call limits.

Answer

The model ID is incorrect; use a different model

Answer

The prompt is too long; reduce the number of tokens in the prompt

Answer

Increase the max_tokens_to_sample value

Question 12

A company is building a customer support chatbot using Amazon Bedrock. They need to store conversation history for context across sessions. Which AWS service is best suited for this purpose?

Accepted Answer

Amazon DynamoDB. Amazon DynamoDB is the best choice for storing conversation history because it is a fully managed NoSQL key-value and document database that provides single-digit millisecond latency at any scale. It supports flexible schema, which is ideal for storing variable-length chat sessions, and its Time to Live (TTL) feature can automatically expire old conversations to manage storage costs. DynamoDB also integrates natively with AWS Lambda and Amazon Bedrock for real-time retrieval and update of context across sessions.

Answer

Amazon S3

Answer

Amazon RDS

Answer

Amazon ElastiCache

Question 13

A company uses Amazon Bedrock to generate marketing copy. The summaries are too verbose. Which parameter should be decreased to directly limit the length of the output?

Accepted Answer

max_tokens. The `max_tokens` parameter directly controls the maximum number of tokens (words or subwords) in the generated output. By decreasing this value, you explicitly cap the length of the marketing copy, making it less verbose. This is the most direct way to limit output length in Amazon Bedrock and other LLM APIs.

Answer

temperature

Answer

top_p

Answer

frequency_penalty

Question 14

A company uses Amazon Bedrock to generate product descriptions. They need to ensure outputs do not contain offensive language. Which service should they integrate to filter content?

Accepted Answer

Bedrock Guardrails. Amazon Bedrock Guardrails is the correct choice because it is specifically designed to enforce content policies for foundation model outputs, including filtering for offensive language, hate speech, and other harmful content. It integrates directly with Bedrock to apply customizable safety filters and deny topics without requiring additional services or custom code.

Answer

Amazon Comprehend

Answer

Amazon Rekognition

Answer

AWS WAF

Question 15

A developer is building a RAG-based Q&A bot with Amazon Bedrock Knowledge Bases. They need a managed vector store for document embeddings. Which service should they use?

Accepted Answer

Amazon OpenSearch Serverless. Amazon Bedrock Knowledge Bases requires a vector store to store and query document embeddings for Retrieval-Augmented Generation (RAG). Amazon OpenSearch Serverless provides a managed, scalable vector engine that supports k-NN (k-nearest neighbor) search, making it the correct choice for this use case. It integrates natively with Bedrock Knowledge Bases to handle embedding storage and similarity search without manual infrastructure management.

Answer

Amazon DynamoDB

Answer

Amazon RDS

Answer

Amazon S3