AIF-C01 Practice Test 2 — 25 Questions

Question 1

A machine learning engineer notices that a SageMaker training job failed due to insufficient permissions to access a KMS-encrypted S3 bucket. The training job's IAM role has S3 access permissions. What should be done to resolve the issue?

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

Accepted Answer

Add a kms:Decrypt permission to the IAM role for the specific KMS key used to encrypt the S3 bucket. The training job failed because the IAM role lacks permission to decrypt the KMS key used for S3 server-side encryption. Even with S3 access permissions, SageMaker cannot read encrypted objects without the kms:Decrypt action on the specific KMS key. Adding kms:Decrypt to the role's policy for that key resolves the issue.

Answer

Create a new KMS key and re-encrypt the data

Answer

Assign the SageMakerFullAccess policy to the role

Answer

Change the S3 bucket's default encryption to S3-managed keys (SSE-S3)

Question 2

A company uses Amazon Bedrock to generate marketing content. They want to reduce costs while maintaining response quality. Which action is most effective?

Accepted Answer

Select a smaller foundation model that still meets accuracy requirements.. The most effective cost-reduction strategy because smaller foundation models (FMs) have fewer parameters, resulting in lower compute and inference costs per request. If the smaller model still meets the required accuracy benchmarks for the marketing content task, it directly reduces operational expenditure without sacrificing quality. Amazon Bedrock offers a range of FMs (e.g., from large models like Claude 3 Opus to smaller ones like Claude 3 Haiku), allowing you to match model size to task complexity.

Answer

Fine-tune a larger model to improve accuracy and reduce retries.

Answer

Increase the temperature parameter to get shorter responses.

Answer

Cache previous responses to reuse for similar prompts.

Question 3

A company is using Amazon Bedrock to generate personalized marketing emails. They notice that the model sometimes produces outputs that are off-brand or contain factual errors about their products. Which TWO prompt engineering techniques would be MOST effective to address these issues? (Choose TWO.)

Accepted Answer

Include few-shot examples in the prompt that demonstrate correct brand tone and factual accuracy. Option A is correct because few-shot examples embed concrete demonstrations of the desired brand tone and accurate product facts directly in the prompt, letting the model imitate those patterns and reducing off-brand or incorrect outputs. Option C is correct because a system prompt sets persistent instructions and context—such as brand guidelines and authoritative product details—that condition every response, which directly constrains tone and factual grounding. Chain-of-thought prompting (B) mainly improves multi-step reasoning and does not supply brand or factual grounding, so it does not address these issues. Zero-shot prompting (D) removes the very examples that would steer tone and accuracy, making it counterproductive. Lowering temperature to 0.0 (E) only reduces sampling randomness; it cannot correct a model that lacks brand or product knowledge, so it does not fix off-brand or factually wrong content.

Answer

Apply chain-of-thought prompting to encourage reasoning

Answer

Use zero-shot prompting without any examples

Answer

Decrease the temperature to 0.0 to eliminate randomness

Question 4

A data scientist trains a linear regression model to predict housing prices. The model achieves a low training error but a high test error. Which concept does this BEST illustrate?

Accepted Answer

Overfitting. The scenario describes a model that performs well on training data but poorly on unseen test data, which is the classic definition of overfitting. Overfitting occurs when the model learns noise and specific patterns in the training set rather than the underlying generalizable relationship, leading to high variance and poor test performance.

Answer

Bias-variance tradeoff

Answer

Regularization

Answer

Underfitting

Question 5

Which of the following is a benefit of using cross-validation during model training?

Accepted Answer

It provides a more reliable estimate of model performance on unseen data. Cross-validation partitions the dataset into multiple folds, training on some and validating on others iteratively. This process yields a more reliable estimate of model performance on unseen data by reducing the variance associated with a single train-test split and ensuring the model is evaluated across different subsets of the data.

Answer

It reduces training time by using all data for training simultaneously

Answer

It eliminates the need for feature engineering

Answer

It guarantees that the model will achieve high accuracy on the test set

Question 6

A company is using Amazon Bedrock's Converse API to build a conversational agent. They want the agent to maintain context across multiple turns. The agent should also be able to call external APIs to retrieve real-time data when needed. Which combination of features should they use?

Accepted Answer

Use the Converse API with system prompts and tool configuration. The Converse API is designed for multi-turn conversations and natively supports system prompts to set agent behavior and tool configuration for invoking external APIs. Option C correctly combines these features to maintain context across turns and enable real-time data retrieval via tool calls, which is exactly what the scenario requires.

Answer

Use the Converse API with streaming enabled and set max tokens to 4096

Answer

Use the InvokeModel API with a conversational prompt template and tool use configuration

Answer

Use the InvokeModel API with a system prompt and store conversation history client-side

Question 7

Which of the following is a key advantage of using a diffusion model for image generation compared to a GAN?

Accepted Answer

Diffusion models produce more diverse and higher-quality images with stable training. Diffusion models offer a key advantage over GANs because they are trained with a stable, non-adversarial objective—denoising score matching—which avoids the mode collapse and training instability common in GANs. This leads to more diverse outputs and, with sufficient steps, higher-quality images that can rival or exceed GANs, especially in large-scale text-to-image tasks.

Answer

Diffusion models generate images faster than GANs during inference

Answer

Diffusion models are inherently conditional and do not require labels

Answer

Diffusion models require less training data than GANs

Question 8

What is the main purpose of a system prompt in a large language model?

Accepted Answer

To define the high-level instructions that set the model's behavior and persona. The system prompt in a large language model (LLM) defines the high-level instructions that set the model's behavior, persona, and constraints for the entire conversation. Unlike user prompts, which are task-specific, the system prompt establishes the model's role (e.g., 'You are a helpful assistant') and governs how it interprets all subsequent interactions, ensuring consistent and aligned outputs.

Answer

To increase the temperature for more creative responses

Answer

To provide an example of the desired output format

Answer

To list all possible tokens to be used in the response

Question 9

A data scientist is using Amazon Bedrock to generate product descriptions. They notice the output is often repetitive and lacks creativity. Which combination of parameter adjustments is MOST likely to produce more diverse and less repetitive output?

Accepted Answer

Increase temperature and increase top-p. Increasing temperature raises the probability of sampling lower-probability tokens, which increases randomness and diversity. Increasing top-p (nucleus sampling) expands the set of tokens considered for sampling, further reducing repetitiveness. Together, these adjustments encourage the model to explore a wider range of possible continuations, producing more creative and less repetitive output.

Answer

Decrease temperature and decrease top-p

Answer

Decrease temperature and increase top-p

Answer

Increase temperature and decrease top-p

Question 10

A developer needs to ensure that a generative AI application on Amazon Bedrock does not produce harmful or inappropriate content. Which feature should they configure?

Accepted Answer

Guardrails for Amazon Bedrock. Guardrails for Amazon Bedrock is the correct feature because it allows developers to define policies that filter and block harmful or inappropriate content in both user inputs and model outputs. This includes configurable thresholds for topics, content filters (e.g., hate, insults, sexual, violence), and denied topics, ensuring the generative AI application adheres to safety and responsible AI requirements.

Answer

Provisioned Throughput

Answer

Model Invocation Logging

Answer

Knowledge Bases for Amazon Bedrock

Question 11

Refer to the exhibit. A developer receives an error when trying to invoke the Claude Instant model from an application. The application uses the IAM role 'MyAppRole'. Which IAM policy statement should be added to the role to resolve the error?

Accepted Answer

{"Effect":"Allow","Action":"bedrock:InvokeModel","Resource":"arn:aws:bedrock:us-east-1::foundation-model/anthropic.claude-instant-v1"}. The error occurs when the application tries to invoke the Claude Instant model via the Bedrock InvokeModel API, and the IAM role 'MyAppRole' lacks the necessary permission. The required action is 'bedrock:InvokeModel', and the resource must be scoped to the specific foundation model ARN, which for Claude Instant in us-east-1 is 'arn:aws:bedrock:us-east-1::foundation-model/anthropic.claude-instant-v1'. This grants least-privilege access to invoke exactly that model.

Answer

{"Effect":"Allow","Action":"bedrock:GetFoundationModel","Resource":"*"}

Answer

{"Effect":"Allow","Action":"bedrock:InvokeModel","Resource":"arn:aws:bedrock:us-east-1::foundation-model/*"}

Answer

{"Effect":"Allow","Action":"bedrock:*","Resource":"*"}

Question 12

Which TWO practices help ensure responsible AI when deploying generative AI applications? (Select TWO.)

Accepted Answer

Implement guardrails to filter harmful or inappropriate content. Option D is correct because implementing guardrails—such as content filters, prompt shields, and safety classifiers—directly mitigates the risk of generative AI producing harmful, offensive, or inappropriate content, which is a core requirement of responsible AI deployment. Option E is correct because continuously monitoring model outputs for bias and drift ensures that the system remains fair and accurate as data distributions and user behavior change over time, enabling timely remediation. Option A is incorrect because removing content filters increases the risk of harmful outputs and violates responsible AI principles rather than ensuring them. Option B is incorrect because increasing model size at the cost of interpretability reduces transparency and explainability, which are key responsible AI goals. Option C is incorrect because using only synthetic data does not by itself guarantee privacy or fairness and can introduce its own biases and quality issues.

Answer

Deploy the model without any content filters to maximize creativity

Answer

Increase model size to improve accuracy at the expense of interpretability

Answer

Use only synthetic data for training to avoid privacy issues

Question 13

A developer is building a chatbot using Amazon Bedrock and Claude. They notice that the model sometimes generates harmful or biased responses. Which AWS service can they use to implement guardrails?

Accepted Answer

Amazon Bedrock Guardrails. Amazon Bedrock Guardrails is the correct choice because it is a native feature of Amazon Bedrock designed specifically to implement safety controls, content filters, and topic policies for foundation models like Claude. It allows developers to define denied topics, filter harmful content (e.g., hate speech, violence), and redact sensitive information, directly addressing the need to prevent harmful or biased responses in a chatbot built on Bedrock.

Answer

AWS WAF

Answer

Amazon GuardDuty

Answer

AWS Shield

Question 14

A financial services company is deploying a generative AI application using Amazon Bedrock. They need to ensure that the model does not generate responses containing personally identifiable information (PII) such as credit card numbers or Social Security numbers. The company also wants to block certain topics like investment advice. Which feature should they configure?

Accepted Answer

Amazon Bedrock Guardrails. Amazon Bedrock Guardrails is the correct feature because it provides configurable safeguards to filter and block undesirable content in foundation model responses, including PII (e.g., credit card numbers, Social Security numbers) and specific topics like investment advice. It operates at the application layer within Bedrock, allowing you to define denied topics and sensitive information filters that are enforced during inference, without requiring separate infrastructure or manual post-processing.

Answer

AWS WAF

Answer

Amazon Macie

Answer

AWS Identity and Access Management (IAM) policies

Question 15

Which Amazon Titan model is specifically designed to convert text into numerical vectors for use in semantic search and Retrieval-Augmented Generation (RAG)?

Accepted Answer

Amazon Titan Embeddings. Amazon Titan Embeddings is the correct model because it is specifically designed to convert text into numerical vectors (embeddings) that capture semantic meaning. These vectors are essential for semantic search and Retrieval-Augmented Generation (RAG), where they enable similarity comparisons and efficient retrieval of relevant documents from a vector database.

Answer

Amazon Titan Text Express

Answer

Amazon Titan Multimodal Embeddings

Answer

Amazon Titan Image Generator

Question 16

A company is using Amazon SageMaker to manage the lifecycle of their machine learning models. They need to implement a governance framework that includes model versioning, monitoring for drift, and decommissioning of outdated models. Which THREE AWS services or features should they use together to meet these requirements? (Select THREE.)

Accepted Answer

SageMaker Pipelines. SageMaker Model Registry (C) is the correct service for model versioning and governance, as it maintains a catalog of model versions with metadata, approval statuses, and lineage, enabling tracking and controlled promotion of models through their lifecycle. SageMaker Model Monitor (E) is correct because it continuously monitors deployed endpoints for data drift, model quality drift, bias drift, and feature attribution drift, alerting when the model deviates from baseline behavior. SageMaker Pipelines (B) is correct because it provides CI/CD orchestration to automate the ML workflow, including training, evaluation, registration to the Model Registry, and deployment, which supports governance and decommissioning through repeatable, versioned pipeline executions. AWS CloudTrail (A) only records API activity for auditing and does not provide model versioning, drift monitoring, or lifecycle decommissioning, so it does not meet the requirements. SageMaker Role Manager (D) merely helps create and manage IAM roles and permissions for SageMaker personas; it does not provide versioning, drift detection, or model decommissioning capabilities.

Answer

AWS CloudTrail

Answer

SageMaker Role Manager

Question 17

A data scientist is designing a RAG pipeline using Amazon Bedrock Knowledge Bases. They need to store embeddings of document chunks and perform similarity searches. Which vector store is a serverless option that integrates directly with Bedrock Knowledge Bases?

Accepted Answer

Amazon OpenSearch Serverless. Amazon Bedrock Knowledge Bases natively integrates with Amazon OpenSearch Serverless as a vector store for storing embeddings and performing similarity searches. OpenSearch Serverless is a fully serverless option that automatically scales and requires no infrastructure management, making it the correct choice for a serverless vector store that works directly with Bedrock Knowledge Bases.

Answer

Amazon Aurora with pgvector

Answer

MongoDB Atlas

Answer

Pinecone

Question 18

A data scientist is prototyping a text summarisation application using Amazon Bedrock. They want to quickly test different foundation models and prompts without writing code. Which tool should they use?

Accepted Answer

Amazon Bedrock Playground. Amazon Bedrock Playground is the correct tool because it provides a no-code, web-based interface for interactively testing and comparing different foundation models and prompts directly within the AWS Management Console. This allows the data scientist to quickly iterate on prompt engineering and model selection without writing any code, which perfectly matches the requirement to prototype a text summarization application.

Answer

Amazon SageMaker Studio

Answer

Amazon Bedrock Agents

Answer

Amazon Bedrock Model Evaluation

Question 19

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 20

A company wants to build a system that automatically routes support tickets to the appropriate department based on the ticket text. The system must handle new categories that emerge over time without retraining. Which TWO approaches should the company combine to achieve this? (Select TWO.)

Accepted Answer

Use Amazon Comprehend for custom classification. Option B is correct because Amazon Comprehend custom classification can categorize ticket text into departments, providing the routing decision. Option E is correct because Amazon Kendra indexes support documents and uses semantic search to match ticket text to the most relevant department knowledge base, allowing new or emerging categories to be handled through indexed content without retraining the classifier. Together, Comprehend custom classification and Kendra enable routing that adapts to new categories by using Kendra to surface relevant department knowledge for unfamiliar tickets. Option A is incorrect because Amazon Forecast predicts time-series values like ticket volume, not ticket content or routing. Option C is incorrect because Comprehend topic modeling and entity detection discover topics and entities but do not directly route tickets to departments. Option D is incorrect because Amazon Rekognition analyzes images and video, not support ticket text.

Answer

Use Amazon Forecast to predict ticket volume

Answer

Use Amazon Comprehend for topic modeling and entity detection

Answer

Use Amazon Rekognition to analyze ticket images