AIF-C01 Practice Test 23 — 15 Questions

Question 1

A financial services firm must build a model that flags potentially fraudulent card transactions in under 200 milliseconds while keeping all data inside its own Amazon VPC. The fraud team has thousands of labeled historical transactions and the pattern changes slowly over months. Which approach best balances latency, data residency, and the need for periodic retraining?

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

Accepted Answer

Train a supervised classification model with Amazon SageMaker, deploy it to a real-time endpoint inside the VPC, and schedule periodic retraining jobs.. A supervised classification model trained on the labeled history and deployed to a SageMaker real-time endpoint inside the VPC meets the latency and residency constraints, while scheduled retraining handles gradual fraud pattern drift. Purpose-built supervised learning uses the available labels, and in-VPC endpoints keep transaction data within the controlled network. Batch, unsupervised, or general foundation-model approaches each miss at least one hard requirement.

Answer

Deploy a pre-trained foundation model from Amazon Bedrock and prompt it to classify each transaction.

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Build an unsupervised anomaly detection model on unlabeled data and run batch transform once per day.

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Use Amazon Fraud Detector in evaluation mode and export predictions to an S3 bucket outside the VPC for scoring.

Question 2

A machine learning team is building a model to predict house prices. They have a dataset with 50 features, including square footage, number of bedrooms, and neighborhood. They want to reduce overfitting and improve model interpretability. Which technique should they apply?

Accepted Answer

Feature selection using correlation analysis. Feature selection using correlation analysis reduces overfitting by removing redundant or irrelevant features, which simplifies the model and improves interpretability. With 50 features, many may be correlated (e.g., square footage and number of bedrooms), and eliminating those reduces noise and the risk of learning spurious patterns.

Answer

Increase the number of training epochs

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Use k-fold cross-validation only

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Principal Component Analysis (PCA)

Question 3

A healthcare company uses Amazon Bedrock to generate patient summaries. They need to ensure no protected health information (PHI) is leaked in the output. Which AWS service can they use to detect and mask PHI in text?

Accepted Answer

Amazon Comprehend Medical. Amazon Comprehend Medical is specifically designed to extract and identify protected health information (PHI) from unstructured medical text using natural language processing (NLP). It can detect entities such as patient names, dates, medical conditions, and medications, and provides APIs to mask or redact that PHI before output. This makes it the correct choice for the healthcare company's requirement to prevent PHI leakage in patient summaries generated by Amazon Bedrock.

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Amazon Macie

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AWS Glue

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Amazon Rekognition

Question 4

A company needs to build a system that converts text-based customer reviews into audio files for accessibility. Which AWS service should be used?

Accepted Answer

Amazon Polly. Amazon Polly is a text-to-speech (TTS) service that converts text into lifelike speech, making it the correct choice for generating audio files from text-based customer reviews. It supports multiple languages and voices, and can output audio in formats like MP3 or OGG, which is ideal for accessibility use cases. The other listed services do not convert text to audio: Amazon Transcribe converts speech to text, Amazon Translate translates text, Amazon Rekognition analyzes images and video, and Amazon Comprehend performs natural language processing. Therefore, only one correct answer exists among the options.

Answer

Amazon Transcribe

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Amazon Translate

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Amazon Rekognition

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Amazon Comprehend

Question 5

An organization wants to prototype a new generative AI application and allow multiple team members to collaborate on prompt engineering and model selection without writing code. Which tool should they use?

Accepted Answer

Amazon Bedrock Studio. Amazon Bedrock Studio is a web-based, no-code environment specifically designed for teams to collaboratively prototype generative AI applications, experiment with prompts, compare foundation models, and share work — all without writing code. It provides a shared workspace with prompt engineering tools and model selection, which matches the requirement exactly.

Answer

Amazon SageMaker Canvas

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Amazon Bedrock Playground

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Amazon CodeWhisperer

Question 6

An ML engineer is training a linear regression model and notices that adding more features increases training error. What is the most likely cause?

Accepted Answer

Irrelevant features. Adding more features increases training error because irrelevant features introduce noise that the model tries to fit, degrading its ability to capture the true underlying pattern. In linear regression, irrelevant features can cause the model to learn spurious correlations, increasing the residual sum of squares (RSS) on the training data. This is a classic sign of feature pollution, not overfitting or underfitting.

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Underfitting

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Multicollinearity

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Overfitting

Question 7

A company wants to use a pre-trained foundation model for sentiment analysis without any customization. Which Amazon Machine Learning service provides access to foundation models via API?

Accepted Answer

Amazon Bedrock. Amazon Bedrock is a fully managed service that provides access to a wide range of pre-trained foundation models (FMs) from leading AI providers like Anthropic, Meta, and Amazon via a unified API. For sentiment analysis, you can invoke an FM such as Anthropic Claude or Meta Llama directly through the Bedrock API without any customization, making it the correct choice for this use case.

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Amazon Textract

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Amazon Comprehend

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Amazon Rekognition

Question 8

Which pricing model does Amazon Bedrock use for foundation model inference?

Accepted Answer

Per-token. Amazon Bedrock charges for foundation model inference based on the number of tokens processed, which includes both input and output tokens. Each model has a specific per-token price, and the total cost is calculated by multiplying the token count by the model's rate. This is the standard pricing model for generative AI inference services like Bedrock.

Answer

Per-request

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Per-hour instance

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Per-GB storage

Question 9

A developer wants to use Amazon Bedrock to build a chatbot that can maintain context across multiple turns of conversation. Which API should they use to simplify multi-turn interactions?

Accepted Answer

Converse API. The Converse API (option C) is specifically designed to simplify multi-turn conversations by automatically managing conversation history and context. Unlike InvokeModel, which requires manual handling of message history, the Converse API accepts a list of messages and maintains session state, making it ideal for chatbot development.

Answer

ListFoundationModels

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InvokeModelWithResponseStream

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InvokeModel

Question 10

An AI practitioner is deploying a large language model (LLM) for a customer support application. They are concerned about hallucinations, where the model generates plausible but incorrect information. Which combination of techniques would be MOST effective to mitigate hallucinations?

Accepted Answer

Use Retrieval-Augmented Generation (RAG) and enable Bedrock Guardrails. Grounding the model with RAG and using Bedrock Guardrails are effective techniques to reduce hallucinations by providing context and enforcing constraints.

Answer

Disable human review and increase max tokens

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Increase model temperature and use top-k sampling

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Fine-tune the model on a small dataset and reduce context length

Question 11

A practitioner is using Amazon Bedrock to invoke Anthropic Claude for a text generation task. They need the model to output a JSON object with specific keys, and they have observed that the model occasionally produces malformed JSON. Which parameter adjustment is MOST likely to improve JSON formatting consistency?

Accepted Answer

Decrease the temperature parameter. Decreasing the temperature parameter reduces the randomness of the model's output, making it more deterministic and less likely to deviate from the expected JSON structure. Lower temperature values (e.g., 0.1–0.3) encourage the model to choose higher-probability tokens, which improves formatting consistency for structured outputs like JSON.

Answer

Increase the top_k parameter

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Increase the max_tokens parameter

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Add a stop sequence of '}'

Question 12

A data scientist is using SageMaker Clarify to analyze a binary classification model for gender bias. The dataset has 80% male and 20% female applicants. The model predicts positive outcomes for 60% of males and 30% of females. Which fairness metric would directly capture this disparity in prediction rates?

Accepted Answer

Demographic parity. Demographic parity (also called statistical parity) measures whether the probability of a positive prediction is equal across groups. Here, 60% of males versus 30% of females receive positive outcomes, a clear disparity in prediction rates, which is exactly what demographic parity captures. It compares selection rates directly without conditioning on actual labels.

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Equalized odds

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Disparate impact

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Accuracy difference

Question 13

A company wants to extract text and data from scanned PDF invoices for automated processing. Which AWS service is MOST appropriate for this task?

Accepted Answer

Amazon Textract. Amazon Textract is specifically designed to extract text, handwriting, and data from scanned documents like PDF invoices. It uses machine learning to recognize and extract key-value pairs, tables, and form data, making it the most appropriate service for automated invoice processing.

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Amazon Rekognition

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Amazon Transcribe

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Amazon Comprehend

Question 14

According to AWS's responsible AI principles, which principle focuses on the idea that AI systems should produce consistent and reliable results even under unexpected conditions?

Accepted Answer

Robustness. Robustness in AWS's responsible AI framework refers to an AI system's ability to maintain consistent, reliable performance even when inputs are unexpected, adversarial, or drawn from edge cases outside the training distribution. It encompasses resilience to noise, distributional shift, and adversarial manipulation. This is distinct from safety, which focuses on preventing harm, and veracity, which concerns truthfulness of outputs.

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Safety

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Veracity

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Fairness

Question 15

A team is deploying a regression model for loan approval. To ensure transparency for regulators, they need to explain individual predictions. Which interpretability method can provide local explanations by approximating the model with a simpler surrogate?

Accepted Answer

LIME. LIME (Local Interpretable Model-agnostic Explanations) is the correct choice because it generates local explanations by fitting a simpler, interpretable surrogate model (e.g., linear regression or decision tree) around a single prediction. This allows the team to explain why a specific loan application was approved or rejected, meeting regulatory transparency requirements without needing access to the original model's internals.

Answer

SHAP values

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Partial dependence plots

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Permutation feature importance