AI0-001 Practice Test 31 — 15 Questions

Question 1

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?

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

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 2

A financial institution is deploying an AI model that predicts loan default risk. The model is trained on historical data that includes sensitive attributes like zip code and marital status. The compliance team is concerned about disparate impact. Which technique should be applied during model training to mitigate bias while maintaining predictive performance?

Accepted Answer

Apply adversarial debiasing during training to minimize the model's ability to predict the sensitive attribute.. Adversarial debiasing is a training-time technique that reduces the model's reliance on sensitive attributes by pitting the predictor against an adversary. This mitigates disparate impact while often maintaining accuracy. Removing sensitive attributes is insufficient due to proxies, oversampling addresses class imbalance rather than bias, and post-processing occurs after training, which does not meet the scenario's requirement for an in-training method.

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Post-process predictions by adjusting thresholds for different demographic groups.

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Remove all sensitive attributes from the training dataset.

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Oversample the minority group in the training data to balance representation.

Question 3

A financial services company trains a gradient-boosted classifier on customer transaction data to flag fraudulent purchases. The training set includes a rare subset of private banking clients whose transaction patterns are highly distinctive. A red-team exercise shows that an attacker with black-box API access can determine whether a specific private banking client's record was in the training set with 85% accuracy. Which technique should the security team prioritize to reduce this specific risk while preserving most model utility?

Accepted Answer

Apply differential privacy during training by adding calibrated noise to the gradient updates.. The scenario describes membership inference enabled by distinctive training records, so the fix must alter the training process itself. Differential privacy during training directly limits per-record influence, which is the mechanism the attacker exploits. Controls on storage, transport, or query volume do not change the statistical relationship between the model's outputs and individual training examples, so they cannot reduce the measured inference accuracy.

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Retrain the model using only synthetic transaction records generated by a GAN.

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Encrypt the model weights at rest using AES-256 and restrict API access with mutual TLS.

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Add rate limiting and query logging to the prediction API to throttle suspicious enumeration.

Question 4

A hospital deploys a computer vision model that detects pneumonia from chest X-rays. Before release, the security team runs a test where they slightly perturb pixel values in images from a different scanner vendor, causing the model to misclassify pneumonia as normal in 40% of cases, while the images remain visually identical to radiologists. Which threat does this test most directly demonstrate?

Accepted Answer

An evasion attack using adversarial perturbations crafted against the deployed model.. The described test modifies inputs at inference time with small, visually imperceptible changes that cause misclassification, which is the defining behavior of an adversarial evasion attack. Because the model still performs well on unperturbed images and the perturbation is applied after training, poisoning, backdoor, and inversion explanations do not match the observed evidence. The cross-vendor shift also shows how domain variation can amplify adversarial fragility.

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A data poisoning attack introduced through the hospital's image labeling pipeline.

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A backdoor triggered by a specific pixel pattern inserted during model training.

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A model inversion attack that reconstructs training images from the model's confidence scores.

Question 5

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 6

A software vendor ships an on-device ML model that performs optical character recognition on scanned contracts. The model file is distributed inside the installer. A security architect worries that an attacker could replace the model file with a trojaned version that subtly alters recognized text. Which control best ensures the device only loads a model that the vendor actually produced?

Accepted Answer

Verify a vendor-signed detached signature over the model file using a public key pinned in the application before loading it.. Ensuring the loaded model was produced by the vendor requires cryptographic authenticity, not just change detection or obscurity. A detached signature verified with a public key pinned in the application proves origin and integrity, so a substituted model fails verification. Local hashes, obfuscation, and disk encryption do not establish that the vendor authored the file and cannot reliably block a trojaned replacement.

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Obfuscate the model file with a proprietary packer so its internal structure is harder to reverse engineer.

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Enable full-disk encryption on the device so the model file cannot be modified while at rest.

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Compute the model file's SHA-256 hash at install time and store it in a local text file for later comparison.

Question 7

Which THREE are common data preprocessing steps in a machine learning pipeline? (Choose 3)

Accepted Answer

Encoding categorical variables. Encoding categorical variables (B) is a standard preprocessing step because algorithms require numeric input, so techniques like one-hot encoding or label encoding convert strings into numbers. Scaling numeric features (D) is also preprocessing, since methods such as standardization (z-score) or min-max normalization bring features to comparable ranges, which helps distance-based and gradient-based models. Handling missing values (E) is preprocessing too, using imputation (mean, median, mode) or deletion to ensure the dataset is complete before training. Hyperparameter tuning (A) and model evaluation (C) are not preprocessing; they occur later in the pipeline, during model selection/training and after training, respectively.

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Hyperparameter tuning

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Model evaluation

Question 8

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 9

A bank is deploying an LLM-based assistant that drafts responses to customer complaints. The assistant retrieves relevant policy passages from an internal vector database and includes them in the prompt. The security team wants to reduce the risk that an attacker can cause the assistant to reveal the full system prompt or internal policy text that the customer should not see. (Choose two.)

Accepted Answer

Apply the principle of least privilege so the assistant only retrieves policy passages relevant to the specific customer complaint.. Reducing prompt and policy leakage in a retrieval-augmented assistant requires limiting what sensitive content enters the context and inspecting what leaves it. Filtering input and output catches extraction attempts and redacts restricted text, while least-privilege retrieval minimizes the sensitive passages available to the model in the first place. Together they shrink both the likelihood and the impact of disclosure, whereas storage encryption, full fine-tuning, and temperature changes do not close the generation channel.

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Increase the model's temperature setting so responses vary and attackers cannot reliably reproduce extracted content.

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Fine-tune the model on the bank's complete policy manual so it no longer needs retrieval at inference time.

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Store the system prompt and policy passages in a separate encrypted database and grant the LLM read access only during inference.

Question 10

A hospital's radiology AI triage model was validated at 94% sensitivity on a curated research dataset. After six months in production, clinicians report that it misses many positive cases on images from a newly installed scanner. The data science team confirms the model has not been retrained. Which action should the team take FIRST to diagnose and correct the problem?

Accepted Answer

Compare the statistical distribution of production images from the new scanner against the training data to detect covariate shift.. Performance collapse on images from new hardware, with no retraining, points to a change in the input distribution rather than a change in the label relationship. Detecting and quantifying that shift through distribution comparison establishes the root cause before any intervention. Threshold changes, targeted fine-tuning, and full retraining are all remedies that presuppose a cause and can waste effort or introduce new bias if applied blindly.

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Increase the model's classification threshold so that more cases are flagged as positive.

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Fine-tune the model on the most recent production images that clinicians have labeled as missed positives.

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Replace the triage model with a larger architecture trained from scratch on a combined dataset of all available scanners.

Question 11

A computer vision engineer is building a model to detect defects on a manufacturing line. Defects are rare, occurring in only 0.5% of images. The engineer trains a convolutional neural network and achieves 99.5% accuracy, but the model never predicts a defect. The engineer wants to address the underlying issue. Which approach is MOST appropriate?

Accepted Answer

Apply class weighting or resampling techniques to emphasize the minority defect class. The model achieves high accuracy by always predicting the majority class, a classic symptom of severe class imbalance. Class weighting or resampling techniques adjust the training process so that minority-class errors carry more weight or appear more frequently, compelling the model to learn defect patterns. The other options do not target the imbalance and are unlikely to resolve the failure to detect rare defects.

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Switch from accuracy to mean squared error as the evaluation metric

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Increase the learning rate to help the model escape the majority-class solution

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Add more convolutional layers to increase model capacity

Question 12

A hospital wants an AI system to classify chest X-rays as normal or showing pneumonia. Radiologists have labeled 12,000 images, but only 900 show pneumonia. The team must choose a modeling approach that handles this class imbalance. Which approach is most appropriate?

Accepted Answer

Train with a weighted loss function that assigns higher cost to the minority class.. Severe class imbalance causes standard training to favor the majority class because minimizing overall loss is easiest when normal images are predicted correctly. A class-weighted loss raises the cost of minority-class errors, pushing the optimizer to learn pneumonia features. It keeps all data, requires no synthetic generation, and is a widely accepted practice in medical imaging.

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Train a regression model to output a continuous pneumonia severity score.

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Remove 8,000 normal images so the classes are closer in size.

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Report only accuracy because the dataset is large.

Question 13

A retail analytics team is preparing a dataset of product reviews for a sentiment classification model. The dataset contains 50,000 reviews, but only 2,000 are labeled as positive or negative. The team wants to use the unlabeled reviews to improve model performance. Which approach best leverages the unlabeled data?

Accepted Answer

Apply semi-supervised learning using a self-training algorithm that iteratively labels high-confidence unlabeled examples and retrains the model.. Semi-supervised learning, specifically self-training, is designed to exploit a small labeled set alongside a large unlabeled set. By iteratively adding high-confidence pseudo-labels from the unlabeled reviews, the model can learn from the broader data distribution, improving generalization. The other options either ignore the unlabeled data or use techniques that are not appropriate for text sentiment classification with limited labels.

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Apply principal component analysis (PCA) to reduce the dimensionality of the unlabeled reviews and then train a classifier on the reduced features.

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Perform data augmentation by generating synthetic reviews using a generative adversarial network (GAN) trained on the labeled data.

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Use transfer learning by fine-tuning a pre-trained language model on the 2,000 labeled reviews only, ignoring the unlabeled data.

Question 14

A data science team is preparing a gradient boosting model to predict equipment failure from sensor data. They want to tune hyperparameters that primarily control model complexity and reduce overfitting. Which two hyperparameters should they focus on? (Choose two.)

Accepted Answer

Learning rate. Learning rate and maximum tree depth are structural hyperparameters that govern how much each tree contributes and how complex each tree can become. Lowering them constrains the ensemble's capacity, which reduces variance and overfitting. The other choices affect runtime, reproducibility, or file storage rather than the bias-variance tradeoff.

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Random seed for data shuffling

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Output directory for saved model artifacts

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Number of CPU threads used during training

Question 15

A retail analytics team has a labeled dataset of 50,000 customer transactions where each record is tagged as either 'fraudulent' or 'legitimate.' They need a supervised learning approach that outputs a probability between 0 and 1 for the fraudulent class so it can be compared against a business threshold. Which algorithm is most appropriate for this task?

Accepted Answer

Logistic regression. The task is supervised binary classification with a need for a 0-to-1 probability, which points to logistic regression because its sigmoid output is directly comparable to a decision threshold. The other techniques either lack supervision (K-means, Apriori) or do not model the target label (PCA). Logistic regression also scales well to 50,000 records and provides coefficients that help explain which transaction features drive fraud risk.

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K-means clustering

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Apriori association rule mining

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Principal component analysis