AIF-C01 Practice Test 8 — 10 Questions

Question 1

A developer is building a RAG application on Amazon Bedrock. They notice that the model sometimes generates answers that are not supported by the retrieved documents. To reduce this, they want to enforce that the model only uses the provided context. Which Bedrock feature should they use?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Enable 'grounding' in Bedrock Guardrails. Bedrock Guardrails' grounding check ensures the model's response is grounded in the provided source documents, reducing hallucinations. The other options do not enforce grounding.

Answer

Increase the number of retrieved documents

Answer

Use a larger context window model

Answer

Adjust the temperature parameter to 0

Question 2

A developer is building an agent using Amazon Bedrock Agents to handle customer support inquiries. The agent needs to look up order status from a database and escalate complex issues to a human. Which THREE components are essential for this agent?

Accepted Answer

A Lambda function that connects to the database and returns results. An action group to query the database, a Lambda function to execute the query, and a knowledge base are essential. IAM roles are needed but not listed here. The other options are not required: fine-tuning is not needed, and custom models are optional.

Answer

A fine-tuned foundation model specific to the support domain

Answer

A custom model trained on historical support tickets

Question 3

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

Answer

Model Evaluation on Amazon Bedrock

Answer

Guardrails for Amazon Bedrock

Question 4

A startup is using Amazon Bedrock to power a virtual assistant. They need to ensure that personally identifiable information (PII) is not included in the model's responses. Which feature should they enable?

Accepted Answer

Enable PII redaction in the Bedrock guardrails.. Amazon Bedrock Guardrails provide a configurable content filtering and PII redaction feature that can automatically detect and mask personally identifiable information (PII) in model inputs and outputs. By enabling PII redaction within guardrails, the startup can ensure that sensitive data like names, addresses, or credit card numbers are removed or obfuscated before the virtual assistant's responses reach the user. This is the direct and intended mechanism for preventing PII leakage in model responses.

Answer

Enable model invocation logging.

Answer

Configure a VPC endpoint.

Answer

Enable data encryption at rest.

Question 5

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 6

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 7

A data scientist runs the SageMaker Clarify job shown in the exhibit for a credit risk model. After reviewing the results, they find a high bias metric for the gender facet. Which action is most consistent with responsible AI?

Accepted Answer

Investigate the root cause and retrain with balanced data. Responsible AI requires understanding and mitigating bias at its source, not just masking it. Investigating the root cause (e.g., data collection bias, labeling bias, or proxy features) and retraining with balanced data directly addresses the high bias metric detected by SageMaker Clarify, aligning with AWS's principle of fairness. Simply removing the gender attribute may not eliminate bias if other features act as proxies, and increasing the threshold does not fix the underlying model bias.

Answer

Proceed with deployment because the model is already in production

Answer

Remove the gender attribute from the training data and retrain

Answer

Increase the acceptance threshold for the model

Question 8

A data scientist is training a model using Amazon SageMaker and notices the training loss is decreasing but validation loss starts increasing after a few epochs. Which technique should they apply to address this?

Accepted Answer

Add regularization (e.g., L1 or L2). The scenario describes overfitting, where the model memorizes training data but fails to generalize to validation data. Adding regularization (L1 or L2) penalizes large weights, reducing model complexity and improving generalization. This is a standard technique in SageMaker training jobs, often configured via the `regularizer` hyperparameter in frameworks like TensorFlow or MXNet.

Answer

Increase batch size

Answer

Increase the learning rate

Answer

Add more training data

Question 9

A company uses a diffusion model on Amazon Bedrock to generate marketing images. They notice that the generated images often contain artifacts and lack fine details, especially when the prompt is complex. The team wants to improve image quality without increasing inference time significantly. Which parameter adjustment is MOST likely to help?

Accepted Answer

Increase the number of inference steps. Increasing the number of inference steps allows the diffusion model to iteratively refine the generated image, reducing artifacts and improving fine details. This directly addresses the problem of complex prompts producing lower-quality outputs without a significant increase in inference time, as the trade-off is typically sub-linear.

Answer

Increase the seed to introduce randomness

Answer

Decrease the output resolution to reduce artifacts

Answer

Reduce the guidance scale to allow more creative freedom

Question 10

A company has deployed a model on Amazon SageMaker and enabled Model Monitor. They notice that the model's prediction accuracy has declined over time. Which type of drift is this, and what should they do?

Accepted Answer

This is concept drift; they should retrain the model with new data. Model drift refers to the degradation of model performance over time due to changes in data or relationships. SageMaker Model Monitor can detect data drift, but model drift (accuracy decline) requires retraining or updating the model.

Answer

This is bias drift; they should run SageMaker Clarify

Answer

This is model drift; they should delete and redeploy the model

Answer

This is data drift; they should update the training data