AIF-C01 Practice Test 26 — 15 Questions

Question 1

During a SageMaker training job, the data scientist observes that the loss is not decreasing after the initial few epochs. The model is a deep neural network with ReLU activations. Which hyperparameter adjustment is most likely to help?

Accepted Answer

Reduce the learning rate. When loss plateaus after a few epochs with ReLU activations, the model is likely stuck in a region where gradients are small (e.g., near a local minimum or plateau). Reducing the learning rate allows the optimizer to take smaller steps, which can help it navigate out of flat regions and continue decreasing the loss. This is a standard technique to improve convergence when training stalls.

Answer

Increase the number of epochs

Answer

Increase the learning rate

Answer

Decrease the batch size

Question 2

A developer is using Amazon Bedrock's Claude model to summarize long documents. The developer notices that the summaries sometimes miss key points. Which parameter adjustment is most likely to improve summary completeness?

Accepted Answer

Increase the max_tokens parameter.. Increasing max_tokens allows the model to generate longer outputs, which is essential when summarizing long documents because the summary may need more tokens to capture all key points. If max_tokens is too low, the model truncates the response, potentially omitting important details. This directly addresses the issue of missing key points by providing sufficient output length for a complete summary.

Answer

Increase the top_k parameter.

Answer

Increase the temperature parameter.

Answer

Increase the top_p parameter.

Question 3

Refer to the exhibit. A data scientist attaches the above IAM policy to a SageMaker notebook instance role. The notebook is in the same AWS account as the S3 bucket. When trying to read a file from 's3://my-bucket/training/data.csv', the data scientist gets an Access Denied error. What is the most likely cause?

Accepted Answer

The policy does not grant 's3:ListBucket' permission. The error occurs because the IAM policy attached to the SageMaker notebook instance role does not include the 's3:ListBucket' permission. When reading a specific object from S3, the SDK first performs a ListObjects (ListBucket) operation to resolve the object key, especially when using high-level APIs like boto3's `read()` or `get_object`. Without this permission, the request is denied even if 's3:GetObject' is allowed.

Answer

The file name contains spaces

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The S3 bucket is in a different Region

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The policy allows 's3:PutObject' which is not needed

Question 4

Which TWO factors should be considered when choosing between a CPU-based instance and a GPU-based instance for training a machine learning model on Amazon SageMaker? (Choose two.)

Accepted Answer

The size of the dataset. Option C is correct because the size of the dataset directly impacts whether a GPU's parallel processing capabilities are beneficial. GPU instances excel at performing many matrix operations simultaneously, which is critical for large datasets where mini-batch gradient descent can be parallelized. For smaller datasets, the overhead of transferring data to GPU memory may negate the performance gains, making CPU instances more cost-effective.

Answer

The number of layers in the model

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The AWS Region

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The choice of hyperparameter optimizer

Question 5

A data scientist is using Amazon SageMaker to train a large language model from scratch. Which AWS service is most suitable for managing the training infrastructure, including automatic scaling and spot instance recovery?

Accepted Answer

Amazon SageMaker Training job.. Amazon SageMaker Training jobs are the most suitable service for managing training infrastructure because they provide built-in automatic scaling, managed spot instance recovery, and distributed training orchestration. This allows the data scientist to focus on model development rather than provisioning and managing EC2 instances, load balancers, or recovery scripts.

Answer

AWS Lambda function.

Answer

Amazon SageMaker Notebook instance.

Answer

Amazon EC2 with a custom setup.

Question 6

A company needs to store large amounts of unstructured training data (images, videos) in a cost-effective manner while ensuring low-latency retrieval for training jobs running on Amazon SageMaker. Which storage solution should be used?

Accepted Answer

Amazon S3. Amazon S3 is the correct choice because it is designed for cost-effective, scalable storage of unstructured data (images, videos) and integrates natively with Amazon SageMaker for low-latency data retrieval during training jobs. S3 provides high throughput and can be accessed directly from SageMaker training instances without the need for file system mounting, making it ideal for large-scale ML workloads.

Answer

Amazon EFS

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Amazon RDS

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Amazon EBS

Question 7

A developer is creating a generative AI application using Amazon Bedrock and needs to ensure that responses do not include toxic or harmful content. Which feature should be enabled?

Accepted Answer

Amazon Bedrock Guardrails.. Amazon Bedrock Guardrails is the correct feature because it is specifically designed to enforce content policies, filter toxic or harmful content, and block undesirable topics in generative AI responses. It provides configurable thresholds for hate, insults, sexual content, violence, and other harmful categories, ensuring compliance with safety requirements without modifying the underlying model.

Answer

Amazon CloudWatch Logs for prompt logging.

Answer

Amazon Virtual Private Cloud (VPC) for network isolation.

Answer

AWS Identity and Access Management (IAM) policies.

Question 8

An organization is using Amazon Bedrock to power a customer service chatbot. They notice that the chatbot occasionally generates hallucinated information about product specifications. Which strategy should be implemented to reduce hallucinations?

Accepted Answer

Integrate a Retrieval Augmented Generation (RAG) system with the product catalog.. Retrieval Augmented Generation (RAG) grounds the model's responses in authoritative, up-to-date product catalog data, directly reducing hallucinations by ensuring the chatbot references verified facts rather than relying solely on its parametric memory. This is the most effective strategy because it provides a retrieval-based factual foundation that fine-tuning or prompt engineering alone cannot guarantee.

Answer

Fine-tune the model on a dataset of product specification conversations.

Answer

Use more detailed prompts with explicit instructions to avoid speculation.

Answer

Increase the temperature parameter to make outputs more conservative.

Question 9

A company is building a chatbot using Amazon Bedrock. They want to ensure the model's responses are grounded in their internal knowledge base and avoid generating information outside that scope. Which feature should they use?

Accepted Answer

Amazon Bedrock Knowledge Bases. Amazon Bedrock Knowledge Bases is the correct feature because it allows you to connect a foundation model (FM) to your internal data sources, such as documents or databases, and use Retrieval Augmented Generation (RAG) to ground responses in that specific knowledge. This ensures the chatbot only generates information from the provided knowledge base, preventing hallucinations or out-of-scope content.

Answer

Agents for Amazon Bedrock

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Model Evaluation on Amazon Bedrock

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Guardrails for Amazon Bedrock

Question 10

A developer is using the Amazon Bedrock API to generate text. They notice that the model sometimes returns harmful content despite setting safety parameters. What is the BEST way to add an additional layer of content filtering?

Accepted Answer

Configure content filters in Amazon Bedrock Guardrails. Amazon Bedrock Guardrails provides a dedicated, configurable content filtering layer that can block harmful content at inference time, independent of the model's built-in safety parameters. This allows developers to enforce custom policies (e.g., hate speech, violence) without modifying the model itself, making it the best additional safeguard.

Answer

Fine-tune the model on a curated safe dataset

Answer

Improve prompt engineering with more specific instructions

Answer

Use AWS WAF to filter API responses

Question 11

A data scientist is fine-tuning a large language model on Amazon SageMaker for a text summarization task. The training loss decreases steadily but the validation loss starts increasing after a few epochs. What should the scientist do to address this issue?

Accepted Answer

Use early stopping based on validation loss. The validation loss increasing while training loss decreases is a classic sign of overfitting. Early stopping based on validation loss halts training when the validation loss stops improving, preventing overfitting and saving computational resources. This is a standard technique in SageMaker's built-in training algorithms and custom training scripts.

Answer

Reduce the batch size

Answer

Increase the learning rate

Answer

Increase the number of training epochs

Question 12

A startup wants to generate product descriptions from a few keywords using a foundation model. They need a fully managed serverless solution that requires no infrastructure setup. Which AWS service should they use?

Accepted Answer

Amazon Bedrock. Amazon Bedrock is a fully managed serverless service that provides access to foundation models (FMs) from leading AI providers via a simple API, making it ideal for generating product descriptions from keywords without any infrastructure management. It directly supports generative AI tasks like text generation, unlike other AWS services that focus on different ML or NLP capabilities.

Answer

Amazon SageMaker

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Amazon Comprehend

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AWS Lambda

Question 13

A team is deploying a generative AI model for medical report generation. They must ensure patient data privacy and comply with HIPAA. Which AWS service feature is essential for de-identifying protected health information (PHI) before sending data to a foundation model?

Accepted Answer

Amazon Comprehend Medical. Amazon Comprehend Medical is the correct service because it is specifically designed to extract and de-identify protected health information (PHI) from unstructured medical text using natural language processing (NLP). It can detect entities such as patient names, dates, and medical record numbers, and then redact or replace them before the data is sent to a foundation model, ensuring HIPAA compliance.

Answer

AWS CloudHSM

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Amazon Macie

Answer

AWS Key Management Service (AWS KMS)

Question 14

A company is using Amazon Bedrock to generate creative marketing copy. They want to reduce the randomness of the output while maintaining diversity. Which TWO parameters should they adjust?

Accepted Answer

Decrease the top_p value. Decreasing the temperature (Option E) reduces randomness by lowering the probability of sampling lower-ranked tokens, making the model more deterministic. Decreasing top_p (Option D) narrows the cumulative probability threshold for token selection, which also reduces randomness while still allowing some diversity within the narrowed set. Together, these parameters control the trade-off between creativity and determinism in Amazon Bedrock's text generation.

Answer

Increase the temperature

Answer

Increase the max token count

Answer

Increase the top_k value

Question 15

A marketing firm uses Amazon Bedrock to generate ad copy. They notice that the generated text often includes factual inaccuracies about their products. Which technique would most effectively reduce these inaccuracies?

Accepted Answer

Implement Retrieval-Augmented Generation (RAG) with a product knowledge base.. Retrieval-Augmented Generation (RAG) grounds the model's output in a trusted, external knowledge base by retrieving relevant product documents before generating text. This directly addresses factual inaccuracies because the model references authoritative data rather than relying solely on its parametric memory, which may contain outdated or incorrect information.

Answer

Use longer, more detailed prompts.

Answer

Increase the temperature parameter to 0.9.

Answer

Fine-tune the model on a dataset of previous ad copies.