AI0-001 Practice Test 32 — 15 Questions

Question 1

An AI engineer is tuning a large language model for a summarization task. The output summaries are too verbose and include irrelevant details. Which technique should be applied to encourage concise outputs?

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

Accepted Answer

Provide a few-shot example with concise summaries. Providing a few-shot example with concise summaries (Option A) directly demonstrates the desired output format to the model, leveraging in-context learning to bias generation toward brevity and relevance. This is the most effective technique for controlling output style without altering the model's underlying parameters.

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Use chain-of-thought prompting

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Decrease the top-k value

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Increase the temperature

Question 2

A machine learning team is deploying a model that predicts loan default probabilities. The model outputs a probability score, and the team wants to convert it into a binary decision (default/no default). The costs of false positives and false negatives are not equal; a false negative (predicting no default when the customer defaults) is five times more costly than a false positive. Which approach best optimizes the decision threshold?

Accepted Answer

Choose a threshold that minimizes the expected cost, weighting false negatives five times more than false positives.. The optimal decision threshold minimizes expected cost, which requires weighting false negatives according to their higher cost. Lowering the threshold increases the number of predicted defaults, reducing expensive false negatives at the expense of more false positives. This cost-sensitive approach aligns model decisions with business objectives, unlike accuracy maximization or arbitrary thresholds.

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Set the threshold to 0.5 to balance false positives and false negatives equally.

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Use the threshold that maximizes overall accuracy on the validation set.

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Set the threshold to the prevalence of defaults in the training data.

Question 3

A team trains a model to predict whether loan applicants will default. On the holdout set the model achieves 0.86 AUC, but when audited, applicants over 60 receive systematically higher risk scores than equally qualified younger applicants. The team must reduce this disparity while preserving predictive performance. Which action should they take first?

Accepted Answer

Measure disparity across protected groups and inspect whether age-correlated features drive the scores.. Bias mitigation should begin with measurement, because the source of disparity determines the right remedy. Group-level fairness metrics and feature attribution reveal whether age itself, a correlated proxy, or biased labels drive the scores. Only after diagnosing the cause can the team choose an appropriate intervention, such as reweighting, proxy removal, or post-processing.

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Remove the age column from the training data and retrain the model.

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Increase model complexity so it can learn finer distinctions between applicants.

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Lower the overall decision threshold to approve more applicants.

Question 4

An AI team is preparing a support-vector machine to classify handwritten digits. Before training, they want to apply preprocessing steps that help the linear kernel separate the classes more effectively and improve generalization. Which two steps are most appropriate? (Choose two.)

Accepted Answer

Tune the C regularization parameter with cross-validation. SVMs are sensitive to feature scale and to the C hyperparameter, so scaling pixel features and tuning C via cross-validation are the two steps that most directly improve linear-kernel separation and generalization. Removing constant pixels is trivial cleanup, inflating support vectors is a misunderstanding, and switching kernels abandons the linear setup the team specified. Together, scaling and C tuning form the standard SVM preparation workflow.

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Remove all pixels with zero variance across the dataset

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Increase the number of support vectors by loosening the margin

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Apply a nonlinear kernel such as RBF while claiming to keep the linear kernel

Question 5

A data scientist is building a model to predict the likelihood of a patient having a rare disease. The dataset is highly imbalanced, with only 2% of patients having the disease. The data scientist trains a logistic regression model and achieves 98% accuracy, but the model predicts 'no disease' for all patients. Which evaluation metric should the data scientist use to better assess the model's performance?

Accepted Answer

Precision-Recall AUC (Area Under the Curve). Precision-Recall AUC is designed for imbalanced classification problems, focusing on the performance of the positive class. It provides a more informative picture than accuracy, which can be misleadingly high when the negative class dominates. This metric helps the data scientist understand the trade-off between precision and recall for the rare disease detection.

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Mean Squared Error (MSE)

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Accuracy

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R-squared (R²)

Question 6

A data engineer is designing a data pipeline for a machine learning model that predicts equipment failure in a manufacturing plant. The pipeline ingests sensor data every second, and the model must be retrained daily with the latest data. The team needs to ensure that the training data is representative of the current operating conditions and that the model does not become stale. Which strategy is most appropriate for maintaining the training dataset?

Accepted Answer

Implement a sliding window approach where the training dataset includes only the most recent 30 days of sensor data.. A sliding window of recent data is most appropriate because it keeps the training set aligned with current operating conditions, mitigating concept drift. This ensures the model learns from the most relevant examples, which is essential for predictive maintenance where equipment behavior evolves over time.

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Randomly sample 10% of all historical data for each daily retraining to reduce computational load.

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Use the entire historical dataset from the plant's inception to train the model, ensuring maximum data volume.

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Manually curate a fixed dataset of known failure events and use it for all retrainings.

Question 7

A machine learning engineer is preparing a dataset for a model that predicts whether a customer will click on an ad. The dataset contains a feature 'time_since_last_purchase' measured in hours, which has a highly skewed distribution with a long tail. The engineer decides to apply a logarithmic transformation to this feature. Which statement BEST describes the effect of this transformation?

Accepted Answer

It reduces the impact of extreme values and makes the distribution more symmetric, which can help linear models.. Applying a logarithmic transformation to a skewed feature like time since last purchase compresses the long tail and reduces skewness. This makes the feature more symmetric, which can improve the performance of models that assume normality or are sensitive to outliers, such as linear models. It does not normalize to a fixed range, bin the feature, or standardize it; those are separate preprocessing steps.

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It converts the feature into a categorical variable by binning values into logarithmic intervals.

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It normalizes the feature to a 0-1 range, ensuring all features contribute equally to distance calculations.

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It eliminates the need for feature scaling because the transformed values are already standardized.

Question 8

A data scientist is building a model to predict the likelihood of a customer defaulting on a loan. The dataset contains a feature 'debt_to_income_ratio' that is highly skewed with a long right tail. The scientist decides to apply a logarithmic transformation to this feature. Which statement best describes the effect of this transformation?

Accepted Answer

It reduces the impact of outliers and makes the distribution more symmetric, which can help linear models perform better.. Log transformation is commonly used to handle right-skewed data. It reduces skewness, stabilizes variance, and makes the distribution more symmetric, which can improve the performance of linear models and neural networks that are sensitive to feature scaling. It does not increase variance, convert to categorical, or normalize to a fixed range.

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It normalizes the feature to a range between 0 and 1, which is required for all machine learning algorithms.

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It converts the feature into a categorical variable, which is useful for tree-based models.

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It increases the variance of the feature, allowing the model to capture more complex patterns.

Question 9

A data scientist is building a model to detect anomalies in server logs. The dataset contains millions of log entries, each with a timestamp and a message. The scientist wants to create features that capture the frequency of certain keywords (e.g., 'error', 'timeout') over time. Which approach is MOST appropriate for creating these features while avoiding data leakage?

Accepted Answer

Compute keyword frequencies using a sliding window that only includes past log entries relative to each timestamp.. To avoid data leakage when creating time-based features, the computation must only use data available up to each point in time. A sliding window that aggregates past log entries ensures causality, mimicking real-time conditions. This captures temporal dynamics like increasing error rates, which are critical for anomaly detection, without using future information that would not be available in production.

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Compute keyword frequencies over the entire dataset and use them as static features.

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Aggregate keyword frequencies per day and assign the same daily frequency to all entries within that day.

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Use a bag-of-words representation for each log entry individually, ignoring timestamps.

Question 10

A hospital's AI triage assistant summarizes patient notes for clinicians. During post-deployment monitoring, the team notices the model's outputs drift in tone and length after the vendor silently updated the underlying foundation model. The application code did not change. Which action best restores reproducibility and protects against future silent model changes?

Accepted Answer

Pin the model to a specific dated version or snapshot and re-run the evaluation suite before promoting any new version.. Silent foundation-model updates change behavior even when application code is untouched, so the model must be treated as a pinned, versioned dependency. Freezing a dated snapshot and requiring evaluation before promoting any new version restores reproducibility and blocks unevaluated changes from reaching clinicians. Token limits, output templates, and faster monitoring do not control which model version serves traffic.

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Increase the monitoring sample rate so the team detects drift faster on the next update.

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Lower the max tokens parameter so outputs cannot drift in length after the vendor update.

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Add a post-processing step that rewrites every summary into a fixed template before display.

Question 11

A media company is deploying a generative AI assistant that drafts marketing copy. Legal requires that every generated draft be attributable to source material and that the system must not reproduce copyrighted passages verbatim. The team wants to enforce this at generation time rather than only reviewing outputs afterward. Which implementation approach BEST meets these requirements?

Accepted Answer

Retrieve grounding passages from an approved internal corpus, pass them to the model as context, and attach source citations to the generated draft.. Grounding generation in an approved internal corpus with retrieval and attaching citations satisfies both legal requirements simultaneously: attributability and reduced verbatim reproduction. The other approaches either act after generation, rely on sampling randomness, or use fine-tuning, none of which guarantees source attribution or constrains output to licensed material at inference time.

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Add a post-generation plagiarism check that blocks drafts containing long matching n-grams against a public web index.

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Fine-tune the base model on the company's existing marketing copy and rely on the fine-tuned weights to avoid verbatim reproduction.

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Increase the model's temperature setting so the assistant paraphrases source material instead of reproducing it.

Question 12

A hospital is deploying a vision model that flags possible pneumonia on chest radiographs. Radiologists report that the model performs well overall but frequently flags images from a newly installed portable X-ray unit. The images are technically adequate. The team must diagnose the cause before changing the model. Which action should the team take FIRST?

Accepted Answer

Compare the statistical distribution of pixel intensities, resolution, and metadata between images from the new unit and the original training set.. When a model degrades on inputs from a new device, the first step is to characterize the input distribution and compare it with training data. That comparison can reveal distribution shift caused by differences in resolution, exposure, or preprocessing, which must be understood before retraining or adjusting thresholds. Only after the cause is identified can the team choose an appropriate remediation.

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Retrain the model on a larger dataset that includes images from the new portable unit.

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Add a rule that discards any image whose DICOM metadata indicates the portable unit was used.

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Lower the model's classification threshold so fewer images are flagged as positive.

Question 13

A data engineer is building a pipeline to ingest and process data from various sources for an AI model. The pipeline must handle both structured data from relational databases and unstructured text from documents. The engineer needs to ensure data quality and prepare the data for model training. Which TWO actions are MOST appropriate for handling missing values in the structured data? (Choose two.)

Accepted Answer

Impute missing numerical values with the median of the available data.. For structured data with missing numerical values, median imputation is a robust simple method, while KNN imputation leverages feature correlations for more accurate estimates. Both preserve data and avoid the biases of deletion or arbitrary constant filling. These actions are appropriate for preparing data for model training, ensuring quality and completeness without introducing severe distortions.

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Remove all rows with any missing values to ensure a complete dataset.

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Encode missing values as a separate category for numerical features.

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Use a constant value like zero to fill missing numerical values.

Question 14

A data scientist is preparing a dataset for a machine learning model and notices that one feature has a range from 0 to 1,000,000, while another feature ranges from 0 to 1. The model to be used is a k-nearest neighbors (KNN) classifier. Which preprocessing step is MOST important to apply before training?

Accepted Answer

Feature scaling, such as min-max normalization or standardization.. KNN uses distance metrics like Euclidean distance, which are sensitive to feature scales. A feature ranging from 0 to 1,000,000 will have a much larger impact on distance than one ranging from 0 to 1, effectively drowning out the smaller feature. Applying feature scaling, such as min-max normalization or standardization, ensures that all features contribute proportionally to the distance, leading to a more accurate and balanced model.

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Principal component analysis (PCA) to reduce dimensionality.

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One-hot encoding for all features to convert them into binary vectors.

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Removing outliers from the feature with the larger range.

Question 15

A healthcare provider is deploying an AI model to predict patient readmission risk. The model was trained on historical data that includes a feature indicating whether the patient has diabetes. The provider wants to ensure the model does not discriminate based on this feature. Which technique should be used to detect and mitigate bias related to the diabetes feature?

Accepted Answer

Perform a fairness audit by comparing model performance across groups with and without diabetes.. A fairness audit systematically compares model outcomes across groups defined by the sensitive attribute, revealing disparities. This is the essential first step to detect bias. Once identified, mitigation techniques like reweighting or adversarial debiasing can be applied. Removing the feature or changing metrics does not directly address the need to detect and mitigate bias.

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Use a different evaluation metric such as AUC-ROC instead of accuracy to assess model performance.

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Remove the diabetes feature from the dataset and retrain the model.

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Increase the model's complexity by adding more layers to capture subtle patterns related to diabetes.