AIF-C01 Practice Exam 4 — 20 Questions

Question 1

A developer wants to store and search vector embeddings for a RAG application. Which AWS-managed vector store option is serverless and can be used with Amazon Bedrock?

Accepted Answer

Amazon OpenSearch Serverless. Amazon OpenSearch Serverless is a serverless option for vector search. The others are either not serverless or not fully managed by AWS.

Answer

Amazon RDS for MySQL

Answer

Amazon Redshift

Answer

Amazon DynamoDB

Question 2

A company is building a generative AI application to answer questions from a large set of technical manuals. Which TWO services or features can be used together in a RAG architecture on AWS? (Choose TWO.)

Accepted Answer

Amazon OpenSearch Serverless. A RAG architecture requires a knowledge base for indexing documents and a vector store for storing embeddings. Bedrock Knowledge Bases can use OpenSearch Serverless as a vector store. SageMaker is for training, not retrieval. Guardrails are safety layers. Lambda is for action groups, not core RAG components.

Answer

Amazon SageMaker

Answer

AWS Lambda

Answer

Amazon Bedrock Guardrails

Question 3

A developer implements a RAG pipeline with Amazon Bedrock and Amazon OpenSearch Serverless. Users report that the chatbot sometimes returns off-topic responses. Investigation shows the retrieved chunks are semantically unrelated to the queries. What is the MOST likely cause?

Accepted Answer

The chunking strategy is not optimised for the document type, or the embedding model is not well-suited. Poor embedding quality or chunking strategy can cause irrelevant retrieval. The other options relate to generation or permission issues.

Answer

The LLM used for generation has too few parameters

Answer

The agent's action group has misconfigured Lambda functions

Answer

The vector store index is not encrypted at rest

Question 4

A company wants to build a generative AI application that can automatically classify customer feedback into positive, neutral, or negative sentiments. Which foundation model capability is BEST suited for this task?

Accepted Answer

Text classification. Text classification is a common use case for foundation models, which can be prompted to output a label. Sentiment analysis is a specific type of text classification.

Answer

Translation

Answer

Text generation

Answer

Text summarization

Question 5

A developer is using Bedrock Studio to prototype a summarization application. They want to quickly test different foundation models and prompts without writing code. What should they use?

Accepted Answer

Bedrock Playground. Bedrock Playground is a no-code interface within Bedrock Studio for experimenting with models and prompts. Knowledge Bases, Agents, and Guardrails are not designed for rapid prototyping of prompts and models.

Answer

Bedrock Knowledge Bases

Answer

Bedrock Agents

Answer

Bedrock Guardrails

Question 6

A data scientist wants to quickly build a supervised learning model for binary classification on a tabular dataset with 10,000 rows and 200 features. The dataset has some missing values and requires minimal code. Which AWS service should the data scientist use?

Accepted Answer

Amazon SageMaker Autopilot. Amazon SageMaker Autopilot is the correct choice because it automatically performs data preprocessing (including handling missing values), feature engineering, model selection, and hyperparameter tuning for supervised learning tasks like binary classification. It requires minimal code—users can simply point to a tabular dataset in Amazon S3 and specify the target column, and Autopilot will automatically train and evaluate multiple candidate models, making it ideal for quickly building a binary classifier on a 10,000-row, 200-feature dataset with missing values.

Answer

Amazon SageMaker Studio Lab

Answer

Amazon SageMaker Clarify

Answer

Amazon SageMaker JumpStart

Question 7

A team has created a knowledge base in Amazon Bedrock for a Q&A application. After updating the source documents, they notice that the model still returns old information. What is the MOST likely cause?

Accepted Answer

The knowledge base has not been resynchronized after updating the documents. The vector store must be resynchronized after document updates. Chunking changes, model selection, and guardrails would not cause stale data if the vector store is not refreshed.

Answer

The chunking strategy is incorrect

Answer

The foundation model has a limited context window

Answer

The guardrails are filtering out the new information

Question 8

Which of the following is a primary benefit of using Bedrock Agents for building generative AI applications?

Accepted Answer

Orchestrate multi-step tasks and call external APIs via action groups. Bedrock Agents enable multi-step reasoning and tool use, allowing the model to perform complex sequences of actions. Fine-tuning is not an agent capability. Guardrails are separate. Prompt optimization is done at the prompt level, not by agents.

Answer

Automatically fine-tune the underlying model on new data

Answer

Optimize prompts automatically without any manual tuning

Answer

Guarantee that the model's responses are factually correct

Question 9

A company uses Amazon Bedrock Agents to automate a multi-step customer support workflow. The agent needs to query a customer database and update a ticket system. Which TWO components are required to enable the agent to interact with these external systems?

Accepted Answer

Lambda functions that implement the business logic for each action. Action groups define the functions the agent can call, and Lambda functions implement the business logic to interact with databases and APIs. Knowledge Bases provide static information, no tool use. Guardrails filter content, not execute actions. Vector stores store embeddings.

Answer

A vector store like Amazon OpenSearch Serverless

Answer

Bedrock Guardrails

Answer

Bedrock Knowledge Bases

Question 10

A company uses Amazon Bedrock to generate marketing copy. They want to evaluate the quality of generated text against human-written reference texts using automated metrics. Which metric measures the overlap of n-grams between generated and reference text?

Accepted Answer

ROUGE. ROUGE measures n-gram overlap and is commonly used for summarisation. BLEU is for translation, BERTScore uses embeddings, and Perplexity measures language model confidence.

Answer

Perplexity

Answer

BLEU

Answer

BERTScore

Question 11

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?

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 12

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 13

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 14

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 15

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 16

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 17

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 18

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 19

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 20

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