AIF-C01 Practice Test 4 — 25 Questions

Question 1

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?

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

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.

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Use more detailed prompts with explicit instructions to avoid speculation.

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Increase the temperature parameter to make outputs more conservative.

Question 2

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.

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Proceed with deployment because the model is already in production

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Remove the gender attribute from the training data and retrain

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Increase the acceptance threshold for the model

Question 3

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.

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Increase batch size

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Increase the learning rate

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Add more training data

Question 4

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.

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Increase the seed to introduce randomness

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Decrease the output resolution to reduce artifacts

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Reduce the guidance scale to allow more creative freedom

Question 5

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.

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This is bias drift; they should run SageMaker Clarify

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

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This is data drift; they should update the training data

Question 6

A company deploys a large language model to automatically generate product descriptions. They want to ensure customers are aware that the content is AI-generated, as part of transparency requirements. What should they implement?

Accepted Answer

Include a disclosure statement such as 'This content was generated by AI' in the output. Transparency requirements often mandate disclosing AI-generated content. The simplest approach is to include a clear disclosure in the output. Bedrock Guardrails can be configured to prepend or append a disclosure message. The other options either hide the AI origin or only apply to specific types of content like images.

Answer

Use Amazon Rekognition to add a visible watermark to images only

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Store metadata in the content database indicating AI generation

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Embed an invisible watermark in the generated text

Question 7

A company is deploying a machine learning model for real-time fraud detection. The model must have latency under 100ms. Which infrastructure choice is most appropriate?

Accepted Answer

Amazon SageMaker real-time endpoints. Amazon SageMaker real-time endpoints are designed for low-latency inference, typically in the tens of milliseconds, making them suitable for real-time fraud detection where latency must be under 100ms. They deploy a model behind a persistent HTTPS endpoint that auto-scales to handle incoming requests with minimal delay.

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Amazon EC2 with Deep Learning AMI

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Amazon SageMaker batch transform

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Amazon SageMaker notebook instance

Question 8

A data scientist is using SageMaker to train a model on a dataset with many features. They suspect some features are redundant. Which feature engineering technique would help?

Accepted Answer

Principal Component Analysis (PCA). Principal Component Analysis (PCA) is a dimensionality reduction technique that transforms the original correlated features into a smaller set of uncorrelated principal components, effectively removing redundancy while preserving most of the variance in the data. In SageMaker, PCA can be applied via the built-in PCA algorithm or as a preprocessing step in a scikit-learn container to reduce feature space and eliminate multicollinearity.

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Feature scaling

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One-hot encoding

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Polynomial features

Question 9

A healthcare AI system predicts patient diagnoses. The data collection process primarily samples from urban hospitals, leading to underrepresentation of rural populations. Which type of bias is this, and what is the most effective mitigation strategy?

Accepted Answer

Representation bias; collect additional data from rural hospitals. Representation bias occurs when a dataset does not adequately represent the target population. The best mitigation is to collect additional data from rural areas to balance representation. Weighting can help but is less effective than obtaining representative data. Historical bias is about past societal biases, not sampling. Measurement bias is about how features are measured. Regularization does not fix data imbalance.

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Historical bias; reweight urban samples to reduce their influence

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Aggregation bias; use regularization to simplify the model

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Measurement bias; apply data augmentation to rural records

Question 10

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 refers to an AI system's ability to handle unexpected inputs and maintain performance, ensuring reliability.

Answer

Safety

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Veracity

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Fairness

Question 11

A data science team is using SHAP values to explain a complex model. They notice that for a particular prediction, the SHAP value for feature 'age' is +0.3. What does this indicate?

Accepted Answer

The age feature increased the prediction by 0.3 units from the baseline. SHAP values show the contribution of each feature to the prediction, measured in the output space. A positive SHAP value increases the prediction from the baseline.

Answer

The age feature is the most important feature globally

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The age feature has a negative impact on the prediction

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The age feature is causing bias in the model

Question 12

A social media platform uses an AI system to moderate content. They want to ensure that human reviewers can review decisions when the AI is uncertain. Which AWS service can be used to set up a human review workflow for AI predictions?

Accepted Answer

Amazon Augmented AI (A2I). Amazon Augmented AI (A2I) enables easy integration of human review workflows into ML applications, allowing humans to review low-confidence predictions or edge cases.

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Amazon Mechanical Turk

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AWS Lambda

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Amazon SageMaker Ground Truth

Question 13

An organization wants to document key information about their machine learning model, including intended use, performance metrics, training data, and ethical considerations. Which tool or practice should they adopt?

Accepted Answer

Model cards. Model cards are structured documentation templates that capture relevant model details, promoting transparency and responsible AI practices. They are recommended by responsible AI guidelines.

Answer

SageMaker Model Registry

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Data sheets

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AWS CloudTrail logs

Question 14

A company is developing an AI system that screens job applications. To comply with regulatory requirements, they need to provide explanations for each automated decision. Which explainability technique provides global feature importance across the entire dataset?

Accepted Answer

SHAP (SHapley Additive exPlanations). SHAP provides both local and global feature importance values based on game theory. It can show the overall impact of each feature across all predictions.

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Partial dependence plots

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LIME (Local Interpretable Model-agnostic Explanations)

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Permutation feature importance

Question 15

A bank is deploying a credit scoring model and must comply with regulatory requirements that decisions can be explained to customers. The model is a gradient boosting machine with hundreds of features. Which explainability technique should the team use to provide local explanations for individual loan decisions?

Accepted Answer

SHAP values. SHAP values provide a unified measure of feature importance for individual predictions, satisfying local explainability requirements. LIME is also local but is less stable and not game-theoretic. Global feature importance does not explain individual decisions.

Answer

Global feature importance based on permutation

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Partial dependence plots

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LIME

Question 16

Which of the following is a key principle of inclusive design in AI?

Accepted Answer

Ensure the AI system is usable by people with diverse abilities and backgrounds. Inclusive design aims to create products that are usable by people with diverse abilities and backgrounds, considering accessibility and user needs.

Answer

Deploy AI systems without user testing

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Use only data from a single demographic group

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Maximize model complexity for better performance

Question 17

A company uses an LLM to generate medical advice. They are concerned about hallucinations and want to implement safeguards. Which TWO techniques should they prioritize?

Accepted Answer

Implement Retrieval-Augmented Generation (RAG) using a verified medical knowledge base. RAG grounds the model in verified medical documents, and human-in-the-loop (A2I) provides oversight for high-risk outputs. Fine-tuning on general data is not sufficient to prevent hallucinations on specific medical topics. Temperature adjustment is a weak control. Stricter content filtering may help but is not specifically designed to address factual accuracy.

Answer

Reduce the temperature parameter to 0

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Fine-tune the LLM on a general dataset of medical texts

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Use Bedrock Guardrails with a content filter for medical topics

Question 18

A company is developing an AI system to screen job applications. They want to ensure the system does not discriminate against candidates based on gender. The dataset used contains historical hiring decisions that may reflect past biases. Which type of bias is MOST likely present in this scenario?

Accepted Answer

Historical bias. Historical bias occurs when the data reflects past societal biases, such as discriminatory hiring practices. This is the most likely bias given the context.

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Measurement bias

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Representation bias

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Aggregation bias

Question 19

A company is using Amazon Rekognition to detect objects in images. They need to detect custom objects that are specific to their domain. What should they do?

Accepted Answer

Use Amazon Rekognition Custom Labels. Amazon Rekognition Custom Labels allows you to train a custom model using your own labeled images to detect domain-specific objects that are not covered by Rekognition's built-in labels. This is the correct service for custom object detection without needing to build a model from scratch.

Answer

Use Amazon Rekognition's built-in labels

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Use Amazon SageMaker Object Detection algorithm

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Use Amazon Comprehend

Question 20

A company wants to document their machine learning model's intended use, limitations, and ethical considerations. Which TWO practices should they adopt? (Choose two.)

Accepted Answer

Create a model card that describes the model's purpose, performance, and fairness metrics. Model cards provide structured documentation of model details, including intended use and limitations. SageMaker Model Cards support this. Data sheets document datasets. The other options are not documentation practices.

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Automatically retrain the model weekly

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Set up Amazon CloudWatch alarms to monitor model accuracy

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Conduct A/B testing between different model versions