AI0-001 Flashcards — Free CompTIA AI+ AI0-001 Study Cards

Apply resampling techniques such as SMOTE or random undersampling

The dataset is severely imbalanced (95% negative vs. 5% positive), which causes classifiers to favor the majority class and produce biased predictions. Resampling techniques such as SMOTE (Synthetic Minority Over-sampling Technique) generate synthetic minority-class samples, while random undersampling reduces majority-class samples, rebalancing the class distribution so the model learns both classes effectively.

Data preparation

Data preparation includes splitting the data to evaluate model performance and prevent leakage.

Demographic parity

Demographic parity (also called statistical parity) directly measures whether the positive prediction rate is equal across groups. It is the fairness metric that compares the proportion of positive outcomes for each protected group, which aligns with the requirement to measure equal positive prediction rates.

Decrease temperature to 0.2

Lowering the temperature makes the model's probability distribution sharper, so it favors the highest-probability tokens and produces more deterministic, on-topic output. A value of 0.2 still allows some variation, preserving a degree of creativity while reducing off-topic or nonsensical responses.

Overfitting

(Overfitting) is correct because the model learned patterns specific to the historical data from the original factory, including noise and factory-specific nuances, rather than generalizable features. When applied to new data from a different factory, those learned patterns do not hold, causing poor performance. This is the classic symptom of overfitting: high accuracy on training data but low accuracy on unseen data.

Adversarial examples

Adversarial examples are inputs deliberately perturbed with small, often imperceptible changes to cause a machine learning model to misclassify. This matches the description of tiny changes to images leading to misclassification. The attack exploits the model's sensitivity to high-dimensional input spaces.

Use a cluster of GPUs with data parallelism

Training large transformer models is computationally intensive, and data parallelism across a cluster of GPUs allows the model to process multiple batches simultaneously, dramatically reducing training time. Each GPU holds a full copy of the model and processes a different subset of the data, with gradients synchronized across devices. This approach scales well and maintains accuracy as long as the effective batch size is tuned appropriately.

Token pricing of the AI model

Token pricing directly determines the cost of each API call because AI models charge based on the number of input and output tokens processed. Since the summarization feature will make frequent API calls, token pricing is the primary cost driver. Other factors like authentication, rate limits, and latency affect security, throughput, and performance, but not the direct cost per request.

Model quantization to INT8 and pruning of low-weight connections

INT8 quantization reduces each weight from 32-bit float to 8-bit integer, cutting model size ~4x and enabling faster integer arithmetic that meets the sub-10ms latency target. Pruning removes low-magnitude weight connections, further shrinking the 50MB footprint and reducing compute. Together they are the standard edge-optimization pairing for latency- and memory-constrained inference.

Normalize or standardize the features

Gradient descent-based models are sensitive to the scale of input features because they update weights proportionally to the gradient, which is influenced by feature magnitudes. With square footage ranging 500–10,000 and bedrooms 1–5, the larger feature will dominate the gradient, causing slow or unstable convergence. Normalizing or standardizing (e.g., Z-score or min-max scaling) ensures all features contribute equally, leading to faster and more reliable training.

F1 score

In highly imbalanced datasets like fraud detection (1% positive class), accuracy is misleading because a model that predicts all transactions as legitimate would achieve 99% accuracy yet fail to detect any fraud. The F1 score (harmonic mean of precision and recall) is the most effective metric because it balances both false positives and false negatives, providing a single score that reflects the model's ability to correctly identify the minority class without being skewed by class imbalance.

Schedule regular bias audits using fairness metrics.

Regular bias audits using fairness metrics (Option B) are the correct governance practice because they provide a systematic, quantitative method to detect and measure disparate impact across protected groups. Unlike simply collecting more data, audits directly evaluate model outputs for statistical parity, equal opportunity, or other fairness definitions, enabling the institution to identify and remediate bias proactively. This aligns with regulatory expectations for ongoing monitoring and accountability in AI governance.

The synthetic data should accurately represent the distribution and features of real rare events.

Synthetic data must faithfully replicate the distribution and feature space of real rare events to enable the model to learn meaningful decision boundaries. If the synthetic data does not capture the true underlying patterns—such as specific sensor readings or transaction anomalies—the model will fail to generalize to actual rare events, defeating the purpose of augmentation.

Raise the anomaly score threshold for triggering alerts.

Raising the anomaly score threshold (Option D) directly reduces false positives by requiring a higher deviation from normal behavior before an alert is triggered. In AIOps platforms, the anomaly score is a numeric value (e.g., 0–100) that quantifies how unusual a metric is; a higher threshold means only more extreme deviations generate alerts, filtering out minor fluctuations that were incorrectly flagged.