AI0-001 AI Concepts and Foundations Practice Test 8 — 15 Questions

Question 1

A natural language processing team is building a sentiment analysis model for customer reviews. They want to ensure the model generalizes well to new, unseen reviews and does not simply memorize the training data. Which TWO techniques are most appropriate to achieve this goal? (Choose two.)

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

Accepted Answer

Apply L2 regularization to the model's weights.. L2 regularization penalizes large weights, encouraging simpler models that generalize better, while early stopping halts training when validation performance degrades, preventing overfitting. Both directly target the goal of avoiding memorization of training data. Increasing epochs, larger batch sizes, and stop word removal do not reliably improve generalization and may even hurt performance.

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Remove all stop words from the reviews before training.

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Use a larger batch size during training.

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Increase the number of training epochs until training loss approaches zero.

Question 2

A data science team is preparing a dataset of loan applications. Each row contains income, credit score, employment length, and a loan amount. Before training a model, the team wants to reduce the influence of income, which is measured in dollars and ranges into the hundreds of thousands, compared with credit score, which ranges from 300 to 850. Which technique should the team apply?

Accepted Answer

Feature scaling. Income and credit score differ by orders of magnitude, so algorithms that rely on distances or gradients will let income dominate. Feature scaling, whether min-max normalization or standardization, brings numeric features onto comparable ranges and is the standard preprocessing remedy. Encoding, dimensionality reduction, and augmentation change the data in ways that do not resolve the scale disparity.

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One-hot encoding

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Data augmentation

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Principal component analysis

Question 3

A software company wants to add a feature that automatically transcribes customer support phone calls into text for analysis. Which type of AI technology is best suited for this task?

Accepted Answer

Automatic speech recognition. Automatic speech recognition (ASR) is the AI technology that converts spoken language into text. It is the foundational component for transcribing phone calls, enabling subsequent analysis. Computer vision handles images, NLP processes text after transcription, and reinforcement learning is for sequential decision-making, so none of those directly perform the speech-to-text conversion required.

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Computer vision

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Natural language processing

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Reinforcement learning

Question 4

A support team wants an AI assistant that can answer employee questions by retrieving passages from the company's internal policy documents and generating a response grounded in those passages. The documents change weekly. Which approach should the team implement?

Accepted Answer

Retrieval-augmented generation. Retrieval-augmented generation pairs a document retriever with a generative model, so the assistant answers from the latest policy passages instead of relying on memorized weights. This keeps responses current as documents change weekly, supports citation, and reduces hallucination. Fine-tuning, training from scratch, and ungrounded prompting all fail to keep pace with changing content or provide grounding.

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Training a model from scratch on the policy corpus

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Fine-tuning the base model on all policy documents

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Prompting the model with only the user's question

Question 5

A financial analyst is using a linear regression model to predict housing prices based on square footage. The model's predictions are consistently off by a large margin for both very small and very large houses, while performing well for average-sized houses. Which phenomenon is most likely occurring?

Accepted Answer

Non-linearity in the relationship. The model's errors are systematic at the extremes of the predictor range, which is a hallmark of assuming a linear relationship when the true relationship is non-linear. A linear regression cannot bend to fit curved patterns, so it underfits the tails. Overfitting would show high variance, underfitting would show poor fit everywhere, and multicollinearity requires multiple correlated predictors.

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Underfitting

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Multicollinearity

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Overfitting

Question 6

An AI governance committee is reviewing a resume-screening model. The model's accuracy is high overall, but its false negative rate is much higher for applicants from one demographic group than for others. The committee wants to address this disparity. Which action best targets the problem?

Accepted Answer

Measure and mitigate bias across demographic groups, including error rates and representation. The scenario describes unequal false negative rates, a fairness problem that requires measuring performance by demographic group and mitigating the disparity through data or threshold adjustments. Adding data, removing protected attributes, or switching to a simpler algorithm may change accuracy or transparency but does not directly close the group-level error gap.

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Increase the model's overall accuracy by adding more training data

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Remove all demographic attributes from the training data

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Replace the model with a simpler algorithm that is inherently interpretable

Question 7

A hospital's AI governance committee is reviewing a diagnostic model that performs well on the general population but poorly on a rare disease subgroup. The committee wants to determine whether the model's poor performance on this subgroup is due to a data problem or a model problem. Which action should the committee take FIRST to make this determination?

Accepted Answer

Analyze the distribution of the rare disease subgroup in the training data and compare it to the model's error rates on that subgroup.. The correct action is to analyze subgroup representation and error rates because it directly tests whether the poor performance stems from insufficient or unrepresentative training data. If the subgroup is rare in the data, the model may not learn its patterns; if the subgroup is well represented but errors remain high, the issue may be model-related. This diagnostic step guides subsequent fixes.

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Replace the model with a more complex neural network architecture that can capture more intricate patterns.

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Retrain the model on the full dataset with a higher learning rate to improve overall accuracy.

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Deploy the model only for the general population and exclude the rare disease subgroup from its use.

Question 8

A startup is designing an AI assistant that must handle a wide range of requests, including summarizing text, answering questions, and translating languages. The team is selecting a foundation model approach. Which two characteristics are typical of foundation models? (Choose two.)

Accepted Answer

They can be adapted through techniques such as fine-tuning, prompting, or retrieval augmentation. Foundation models are pretrained on broad data and adapted to many tasks through fine-tuning, prompting, or retrieval augmentation, which is exactly what a multi-purpose assistant needs. They do not require task-specific labels before producing output, they are not single-task tools, and they do not permanently encode every interaction in their weights.

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They require labeled data for every task before they can produce any useful output

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They are purpose-built for a single narrow task and cannot be reused elsewhere

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They store a permanent, unchangeable record of every user interaction in their parameters

Question 9

A financial services firm is deploying a credit-scoring model that uses alternative data such as utility payments and rental history. The model shows high accuracy but the firm is concerned about regulatory compliance and explainability. The firm must provide adverse action notices to applicants who are denied credit. Which approach best satisfies the need for explainability while maintaining model performance?

Accepted Answer

Use a gradient boosting model with SHAP values to generate per-applicant reason codes for denials.. Gradient boosting with SHAP values is the best choice because SHAP provides per-applicant, locally faithful explanations that can be directly used in adverse action notices. It preserves the model's ability to leverage alternative data for accuracy while delivering the transparency regulators demand. Other options either sacrifice performance, fail to provide specific reasons, or rely on approximations that may not be reliable.

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Use a black-box deep learning model and provide only the top-level feature importances from a surrogate model.

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Replace the model with a simple logistic regression using only traditional credit bureau data.

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Deploy the model as a black box but disclose the general factors that influence credit decisions in the application terms.

Question 10

A research team is developing an AI system to predict patient outcomes from electronic health records. The team must ensure the system adheres to ethical AI principles. Which TWO practices best align with the principle of transparency and explainability? (Choose two.)

Accepted Answer

Document the model's intended use, limitations, and performance across demographic subgroups in a model card.. The two practices that best align with transparency and explainability are documenting model details in a model card and implementing feature attribution for individual predictions. A model card communicates intended use, limitations, and subgroup performance, while feature attribution explains specific outputs. Together, they enable stakeholders to understand and scrutinize the model, fostering trust and accountability.

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Restrict access to the model's source code and training data to a small group of developers.

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Focus solely on maximizing predictive accuracy, as ethical concerns are secondary to clinical outcomes.

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Use a complex ensemble model and provide only the overall accuracy metric to clinicians.

Question 11

A hospital's AI governance committee is reviewing a sepsis-prediction model before deployment. The model was trained on five years of historical ICU data in which patients who received early antibiotics had better outcomes, and the model learned to recommend antibiotics for nearly every patient with any fever. The committee wants to determine whether the model has learned a spurious correlation rather than a true clinical signal. Which action best evaluates this concern?

Accepted Answer

Perform a feature-ablation study, removing fever and antibiotic-administration variables, and measure the change in predictive performance and decision patterns.. Feature ablation is the most direct way to test whether a model depends on suspected spurious inputs. By removing fever and antibiotic-administration variables and observing changes in performance and recommendation behavior, the committee can determine whether the model's decisions hinge on those confounded features rather than on genuine clinical indicators of sepsis.

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Increase the size of the test set by repartitioning the existing data and recompute the AUC.

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Deploy the model in shadow mode and compare its alerts against clinician judgment for one month.

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Retrain the model with a larger learning rate and compare training loss across epochs.

Question 12

A small e-commerce company wants to implement a chatbot to handle customer inquiries about order status and returns. The company has limited historical chat data and wants a solution that can be deployed quickly without extensive training. Which type of AI solution is most appropriate?

Accepted Answer

Fine-tune a pre-trained language model on the company's limited chat data and product information.. Fine-tuning a pre-trained language model is the most appropriate because it uses transfer learning to adapt a general model to the company's domain with minimal data. It provides natural language understanding and can be deployed quickly. Other options either require excessive resources, lack flexibility, or are too complex for the scenario.

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Train a custom large language model from scratch using the company's product catalog.

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Use a rule-based chatbot with predefined scripts for common inquiries.

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Implement a reinforcement learning agent that learns from customer interactions in real time.

Question 13

A logistics company is building a model to estimate delivery times. The team has a dataset with 120,000 labeled historical deliveries, but the labels for arrival times are noisy because some drivers manually entered them hours later. The team wants to improve label quality without discarding the dataset. Which approach best addresses the noisy-label problem?

Accepted Answer

Model the label noise explicitly, for example with a noise-robust loss or a probabilistic noise model, and train with the noisy labels.. Noise-robust training methods explicitly account for corrupted labels, allowing the team to use the full dataset while reducing the impact of late manual entries. Unlike trimming or self-relabeling, this approach models the noise process and preserves legitimate variation, which is essential for accurate delivery-time estimation.

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Remove all records whose arrival times fall outside two standard deviations from the mean.

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Train a model on the noisy labels, then use its predictions to relabel the most confident examples and retrain.

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Apply a clustering algorithm to the delivery records and relabel each cluster with its centroid value.

Question 14

A self-driving car company is testing a perception model that detects pedestrians. The model achieves 99% accuracy on the test set but fails to detect pedestrians wearing dark clothing at night. The company wants to improve the model's robustness. Which action should the team take to best address this specific weakness?

Accepted Answer

Collect and annotate more nighttime images of pedestrians wearing dark clothing and add them to the training set.. Collecting and annotating more nighttime images of pedestrians in dark clothing is the most direct way to address the specific weakness. The model's failure indicates a gap in the training distribution, so adding representative data will help it learn the necessary visual cues. Other options either do not target the root cause or introduce trade-offs without improving fundamental recognition.

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Increase the model's depth by adding more layers to capture more complex patterns.

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Apply data augmentation by randomly rotating and flipping existing daytime images.

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Reduce the model's confidence threshold for pedestrian detection to increase sensitivity.

Question 15

A financial services firm is deploying a credit-scoring model that uses alternative data such as utility payments and rental history. The compliance team is concerned about fairness and transparency. Which TWO practices best support responsible AI deployment in this scenario? (Choose two.)

Accepted Answer

Provide adverse-action explanations for declined applicants that identify the principal factors influencing the decision.. Disparate-impact testing with documentation and adverse-action explanations directly address fairness and transparency in credit scoring. Testing detects discriminatory outcomes across protected groups, while explanations make individual decisions understandable and contestable. Together they satisfy core responsible-AI obligations when alternative data is used.

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Use only the most predictive features and remove any feature that reduces overall accuracy.

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Retrain the model monthly on all new applications without human review to keep pace with changing data.

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Encrypt the training data at rest and restrict access to the data science team.