AIF-C01 Practice Test 39 — 15 Questions

Question 1

A developer wants to quickly test different prompts and models for a text summarization task without writing any code. Which AWS service should they use?

Accepted Answer

Amazon Bedrock Playground. Amazon Bedrock Playground is a no-code interface within the AWS Management Console that allows developers to interactively test different foundation models and prompts for tasks like text summarization. It provides a chat-like environment where you can select models, adjust parameters (e.g., temperature, max tokens), and see responses immediately without writing any code. This makes it the ideal choice for rapid experimentation and prototyping.

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Amazon SageMaker Studio

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Amazon Bedrock Agents

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Amazon Bedrock Knowledge Bases

Question 2

A data scientist is preparing data for a classification model. The dataset contains missing values in several features. Which TWO approaches are appropriate for handling missing data? (Select TWO.)

Accepted Answer

Remove rows with any missing values. Option A (Remove rows with any missing values) is correct because listwise deletion is a standard, valid preprocessing approach when missingness is sparse or completely at random, producing a clean dataset that most classifiers can consume directly. Option E (Impute missing values with the median of the feature) is correct because median imputation is a widely accepted technique that preserves all records and is robust to outliers and skewed distributions, making it suitable for numeric features feeding a classification model. Option B (Set missing values to zero) is not appropriate in general because zero is a real value that can distort the feature's distribution and mislead the model unless zero is a meaningful sentinel. Option C (Ignore missing values during training) is not valid because most classification algorithms cannot natively process NaN values and will error or produce biased results. Option D (Replace missing values with -1) is also not generally appropriate because -1 is an arbitrary constant that can introduce artificial patterns and skew numeric features unless it is a documented missing-value code.

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Set missing values to zero

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Ignore missing values during training

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Replace missing values with -1

Question 3

A company wants to prevent an Amazon Bedrock chatbot from discussing specific prohibited topics like competitor pricing. Which Bedrock feature should they configure?

Accepted Answer

Bedrock Guardrails – topic denial. Amazon Bedrock Guardrails with topic denial is the correct feature because it allows administrators to define a set of prohibited topics (e.g., competitor pricing) and configure the model to refuse to engage in conversations about them. When the model detects a user input or generates a response that falls within a denied topic, Guardrails intercepts the interaction and returns a predefined denial message, effectively blocking the unwanted discussion. This is a content moderation capability specifically designed to enforce policy-based restrictions on model behavior, making it the ideal choice for preventing a chatbot from discussing specific prohibited topics.

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Bedrock Knowledge Base

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Bedrock Agents

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Bedrock Model Evaluation

Question 4

A developer is using the Amazon Bedrock InvokeModel API with the above request to summarize meeting notes. The response is a single word repeated many times. Which parameter is MOST likely causing this issue?

Accepted Answer

temperature set to 0. A temperature of 0 forces the model to always select the highest-probability token at each step, which can lead to repetitive loops if the most likely token repeatedly points back to itself (e.g., the same word). This deterministic behavior eliminates randomness, causing the model to get stuck in a single-word cycle rather than generating diverse or coherent text.

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topP set to 0.9

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stopSequences is empty

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maxTokenCount set to 100

Question 5

Which AWS service is BEST suited for extracting text from scanned PDF documents, such as invoices and receipts?

Accepted Answer

Amazon Textract. Amazon Textract is specifically designed to extract text, handwriting, and data from scanned documents like invoices and receipts. It uses machine learning to recognize and extract printed text, forms, and tables from document images, going beyond simple OCR by understanding the structure of the document.

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Amazon Comprehend

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Amazon Rekognition

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Amazon Transcribe

Question 6

A company wants to use Amazon SageMaker to train a model on sensitive healthcare data. What is the MOST secure way to ensure that the training data is encrypted at rest and in transit, and that only authorized users can access the model artifacts?

Accepted Answer

Enable server-side encryption with AWS KMS managed keys for the S3 buckets, SageMaker notebook instances, and training jobs, and use IAM roles to limit access.. It provides end-to-end encryption for sensitive healthcare data by enabling server-side encryption with AWS KMS managed keys for S3 buckets (data at rest), SageMaker notebook instances (data at rest and in transit via TLS), and training jobs (data at rest on ephemeral storage and in transit between services). IAM roles enforce least-privilege access to model artifacts, ensuring only authorized users can read or write them. This combination meets compliance requirements like HIPAA by encrypting data at rest (AES-256) and in transit (TLS 1.2+), while IAM policies control access.

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Disable encryption for faster training, and rely on IAM policies to restrict access.

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Use client-side encryption for all data, and store encryption keys in AWS Secrets Manager.

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Use an AWS CloudHSM to store encryption keys and configure SageMaker to use it.

Question 7

A company uses Amazon Bedrock to generate code. They want to ensure the code follows security best practices and does not contain vulnerabilities. Which approach is most effective?

Accepted Answer

Use Amazon CodeGuru Security to review generated code.. Amazon CodeGuru Security reviews code for security vulnerabilities and provides recommendations. Using a model trained on secure code may not be sufficient; WAF is for web traffic; training a custom model requires significant effort and may not catch all issues.

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Implement a post-processing step using AWS WAF.

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Train a custom model on the company’s secure code.

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Use a foundation model trained only on secure code.

Question 8

A team is using Amazon Comprehend to analyze customer feedback for sentiment. They want to detect and mitigate potential bias against certain demographic groups. Which TWO approaches should they consider? (Choose TWO.)

Accepted Answer

Use SageMaker Clarify to compute bias metrics on the training data.. Option D is correct because SageMaker Clarify is the AWS service purpose-built to detect bias in datasets and models, computing metrics such as class imbalance (CI) and difference in proportions of labels (DPL) across demographic groups, which directly addresses measuring bias in the training data used for sentiment analysis. Option E is correct because Amazon Comprehend custom classification lets you train a custom model on your own labeled data, and ensuring that training data is balanced across demographic groups reduces the risk that the classifier learns and amplifies skewed associations, thereby mitigating bias in the resulting sentiment predictions. Option A is not appropriate because AWS WAF is a web application firewall that filters HTTP/S traffic against exploits like SQL injection and XSS, not a tool for detecting or mitigating bias in text sentiment. Option B is not appropriate because AWS CloudTrail only records API activity for auditing and governance, and does not analyze or reduce bias. Option C is not appropriate because Amazon Rekognition performs image and video analysis (such as facial detection and moderation), which is unrelated to detecting bias in textual customer feedback sentiment.

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Use AWS WAF to filter out biased comments.

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Use AWS CloudTrail to audit API calls.

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Use Amazon Rekognition to verify images.

Question 9

An organization wants to control which topics their AI chatbot can discuss. For example, they want to block all conversations about investment advice. Which Amazon Bedrock Guardrails feature should they configure?

Accepted Answer

Topic restrictions. Amazon Bedrock Guardrails topic restrictions allow you to define a set of topics that the model should avoid discussing. By configuring topic restrictions, you can block conversations about investment advice by providing a natural language description of the topic and example phrases. This is the specific feature designed to deny-list topics.

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Contextual grounding check

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Content filtering with category-based harmful content filters

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Sensitive information filters

Question 10

A developer wants to test different foundation models quickly without setting up infrastructure. Which AWS service allows interactive prompting and comparison of multiple models?

Accepted Answer

Amazon Bedrock Playground. Amazon Bedrock Playground is a feature within Amazon Bedrock that provides a web-based interface for interactive prompting and side-by-side comparison of multiple foundation models (FMs). It allows developers to test different models quickly without provisioning any infrastructure, making it ideal for rapid experimentation and evaluation.

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Amazon Comprehend

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Amazon Lex

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Amazon SageMaker Studio

Question 11

A company uses Amazon Bedrock and wants to ensure that the model outputs are grounded in a set of provided documents to reduce hallucinations. Which TWO actions should they take? (Select TWO.)

Accepted Answer

Enable the grounding check in Bedrock Guardrails. Option A is correct because Bedrock Guardrails provides a contextual grounding check that evaluates model responses against a provided source (reference) and applies a grounding threshold to filter or flag responses that are not supported by the source, directly reducing hallucinations. Option D is correct because Amazon Bedrock Knowledge Bases ingests the provided documents, chunks them, generates embeddings, and stores them in a vector store so the model can retrieve relevant chunks at inference time via RetrieveAndGenerate, grounding outputs in the actual documents. Option B is wrong because a word filter blocks specific words or phrases and cannot determine whether a statement is factually grounded in the source documents. Option C is wrong because model invocation logging to S3 only records requests and responses for auditing and monitoring; it does not ground outputs or prevent hallucinations. Option E is wrong because fine-tuning on the documents adapts model weights to style and patterns but does not provide retrieval-based grounding or verifiable citations, and it is not the recommended mechanism for grounding against a document set.

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Configure a word filter to block ungrounded phrases

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Enable model invocation logging to S3

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Fine-tune the model on the documents

Question 12

A company wants to build a customer service chatbot that answers questions about their internal policy documents. The documents are updated monthly, and the team cannot afford to retrain a model each time. Which approach is MOST appropriate?

Accepted Answer

Use Retrieval-Augmented Generation (RAG) with the policy documents indexed in a vector store. Retrieval-Augmented Generation (RAG) is the most appropriate approach because it allows the chatbot to answer questions based on the latest policy documents without retraining the model. RAG retrieves relevant document chunks from a vector store at inference time and injects them into the prompt, enabling the model to ground its responses in the most current information. This avoids the cost and latency of monthly fine-tuning or retraining, and it scales efficiently as documents are updated.

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Use a larger foundation model with a longer context window and paste all documents into each prompt

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Train a custom model from scratch on the policy documents each month

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Fine-tune a base LLM on the policy documents monthly

Question 13

A data scientist is building a model to predict housing prices. The dataset contains features such as square footage, number of bedrooms, and location. After training a linear regression model, the RMSE on the test set is significantly higher than on the training set. What is the MOST likely cause?

Accepted Answer

Overfitting. Overfitting. When RMSE on the test set is significantly higher than on the training set, it indicates the model has learned noise and specific patterns from the training data that do not generalize to unseen data. This is the classic symptom of overfitting in linear regression, where the model may have too many features or excessive complexity relative to the data.

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Data leakage from test to training set

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Underfitting

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Insufficient training data

Question 14

A company is deploying a generative AI model that produces text summaries of legal documents. To comply with responsible AI guidelines, which of the following is the most critical to ensure transparency?

Accepted Answer

Informing users that the summaries are generated by AI. Transparency in responsible AI requires that users are clearly informed when they are interacting with AI-generated content, especially in high-stakes domains like legal document summarization. Option A directly addresses this by mandating disclosure, which builds trust and allows users to critically evaluate the output. Without such disclosure, users may mistakenly attribute human-level authority or accountability to the AI system.

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Ensuring the model does not reflect biases from training data

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Achieving high performance on summary quality metrics

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Guaranteeing the summaries are factually accurate

Question 15

An AWS AI practitioner is designing a document processing pipeline using Amazon Textract and Amazon Comprehend. The pipeline must extract text from PDFs, detect entities, and classify documents into categories (e.g., invoice, contract, report). Which THREE steps should be included in the pipeline? (Choose three.)

Accepted Answer

Use Amazon Comprehend to train a custom classifier for document type. Option C is correct because Amazon Textract is the AWS service purpose-built to extract text and structured data from PDFs and scanned documents via synchronous or asynchronous APIs such as DetectDocumentText and StartDocumentTextDetection. Option D is correct because Amazon Comprehend's entity detection (DetectEntities) natively identifies entities like DATE, QUANTITY, and other standard or custom entity types needed to pull dates and amounts from the extracted text. Option A is correct because Amazon Comprehend custom classification lets you train a custom classifier on labeled documents to categorize them into classes such as invoice, contract, or report, which is exactly the classification requirement. Option B is wrong because Amazon Personalize is a recommendation service for personalized user experiences, not document categorization. Option E is wrong because Amazon Rekognition performs image and video analysis (objects, faces, labels) and does not extract document text or classify document types.

Answer

Use Amazon Personalize to recommend document categories

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Use Amazon Rekognition to analyze images in the PDF