AI0-001 AI Models and Data Engineering Practice Test 5 — 15 Questions

Question 1

An AI team notices that a model's F1 score on the validation set is 0.95, but on the test set it drops to 0.72. Which course of action is most appropriate?

Accepted Answer

Apply regularization.. The F1 score dropping from 0.95 on the validation set to 0.72 on the test set is a classic sign of overfitting, where the model has memorized the training/validation data but fails to generalize to unseen test data. Applying regularization (e.g., L1/L2 weight decay, dropout) is the most appropriate course of action because it penalizes overly complex models, reduces variance, and improves generalization without requiring more data or changing the model architecture.

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Reduce the training dataset size.

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Adjust the train/test split to be more representative.

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Increase model complexity.

Question 2

A data engineer is building a pipeline to ingest streaming data from IoT sensors. Which data storage solution is best suited for real-time analytics on timestamped sensor readings?

Accepted Answer

Time-series database. Time-series databases (TSDBs) are optimized for high-ingest rates of timestamped data and provide efficient downsampling, retention policies, and time-based aggregation functions. For IoT sensor streaming, a TSDB like InfluxDB or TimescaleDB delivers sub-second query performance on time-range scans, which is essential for real-time analytics.

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Data warehouse

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Relational database

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Data lake

Question 3

During feature engineering, a data scientist creates a new feature that is a linear combination of two existing features. What risk does this pose to the model?

Accepted Answer

Multicollinearity. Creating a new feature as a linear combination of two existing features introduces perfect multicollinearity, where the new feature is an exact linear function of the original ones. This violates the assumption of no perfect multicollinearity in linear models, causing the design matrix to become singular and making coefficient estimates unstable or impossible to compute. Even in non-linear models, high multicollinearity can inflate variance and reduce interpretability.

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Data leakage

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Overfitting

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Underfitting

Question 4

A model trained on a dataset with imbalanced classes achieves 98% accuracy but only 50% recall for the minority class. Which technique should be applied first to address the imbalance?

Accepted Answer

Apply cost-sensitive learning. Cost-sensitive learning directly modifies the model's loss function to penalize misclassifications of the minority class more heavily than those of the majority class. This approach addresses the root cause of the imbalance—the model's bias toward the majority class—without altering the dataset distribution, making it the most immediate and effective first step.

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Reduce the majority class size

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Use SMOTE to generate synthetic samples

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Collect more data for the minority class

Question 5

A team is using a pre-trained language model for sentiment analysis. They want to adapt it to a specific domain with limited labeled data. Which approach is most efficient?

Accepted Answer

Fine-tune the pre-trained model on domain data. Fine-tuning a pre-trained language model on domain-specific labeled data is the most efficient approach because it leverages the general language understanding learned from large corpora while adapting to the target domain with minimal additional data. This process uses transfer learning, where only the final layers or a subset of parameters are updated, significantly reducing the amount of labeled data and compute required compared to training from scratch.

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Use the pre-trained model as is

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Train a new model from scratch

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Ensemble multiple pre-trained models

Question 6

An AI model is deployed to a mobile app with limited computational resources. The model is a deep neural network with high latency. Which technique is best to reduce inference time?

Accepted Answer

Quantization. Quantization reduces the precision of the model's weights and activations (e.g., from 32-bit floating point to 8-bit integer), which decreases memory footprint and speeds up computation on resource-constrained devices like mobile phones. This directly lowers inference latency without requiring additional hardware or architectural changes.

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Increase batch size

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Add more layers

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Use a larger model

Question 7

A data scientist is evaluating a logistic regression model for binary classification on highly imbalanced data. Which TWO metrics are most appropriate to assess model performance? (Choose TWO.)

Accepted Answer

Recall. Recall (B) is correct because in highly imbalanced binary classification, the minority class (e.g., fraud or disease) is the focus. Recall measures the proportion of actual positives correctly identified, which is critical when missing a positive has high cost. Precision (C) is correct because it measures the proportion of predicted positives that are truly positive, which is essential when false positives are costly or when the model's positive predictions must be trustworthy.

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Accuracy

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Mean squared error (MSE)

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F1 score

Question 8

A team is using k-fold cross-validation to evaluate a model. They observe high variance in performance scores across folds. Which TWO actions are most likely to reduce this variance? (Choose TWO.)

Accepted Answer

Increase the number of folds. Increasing the number of folds (e.g., from 5 to 10) means each fold contains more training data, which reduces the variance of the performance estimate because the model is trained on a larger portion of the dataset each time. Stratified cross-validation ensures that each fold maintains the same class distribution as the original dataset, which stabilizes performance scores when the dataset is imbalanced, thereby reducing variance across folds.

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Decrease the number of folds

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Shuffle data before splitting

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Use a more complex model

Question 9

A data scientist notices that a binary classification model consistently predicts the majority class. Which data engineering technique should be applied?

Accepted Answer

Oversampling. Oversampling (Option D) is correct because the model's bias toward the majority class indicates a class imbalance problem. By synthetically increasing the number of minority class samples (e.g., using SMOTE or random oversampling), the training data becomes more balanced, allowing the classifier to learn decision boundaries that are not skewed toward the majority class.

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Feature scaling

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Dimensionality reduction

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Polynomial features

Question 10

A team is building a regression model to predict house prices. Which data transformation is most appropriate if the target variable exhibits right skewness?

Accepted Answer

Log transformation. Log transformation is the most appropriate technique for right-skewed target variables because it compresses the long tail, making the distribution more symmetric and closer to Gaussian. This stabilizes variance and often improves the performance of regression models that assume normally distributed errors, such as linear regression.

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Principal component analysis (PCA)

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Standardization (Z-score)

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One-hot encoding

Question 11

A model's training accuracy is 99% but validation accuracy drops to 60%. What is the most likely issue?

Accepted Answer

Overfitting. A training accuracy of 99% with a validation accuracy of only 60% is a classic symptom of overfitting. The model has memorized the training data, including noise and outliers, rather than learning generalizable patterns, causing it to perform poorly on unseen validation data.

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Data leakage

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Multicollinearity

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Underfitting

Question 12

A real-time recommendation system requires low latency. Which data storage strategy is best for serving user profiles and item embeddings?

Accepted Answer

In-memory key-value store (e.g., Redis). An in-memory key-value store like Redis is ideal for serving user profiles and item embeddings in a real-time recommendation system because it provides sub-millisecond read/write latency by keeping data in RAM. This directly meets the low-latency requirement for fetching embeddings and profiles on each request, without the overhead of disk I/O or complex query processing.

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Time-series database (e.g., InfluxDB)

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Relational database with joins

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Data lake in object storage (e.g., Amazon S3)

Question 13

An organization uses a machine learning model to approve loans. The model shows higher false positive rates for a protected group. Which data engineering step should be taken to mitigate this?

Accepted Answer

Use adversarial debiasing technique. Adversarial debiasing is a technique that trains the model to minimize prediction error while simultaneously preventing an adversary from predicting the protected attribute from the model's outputs. This directly reduces disparate impact by forcing the model to learn representations that are uncorrelated with the protected group, thereby lowering false positive rates for that group without simply removing the attribute.

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Remove the protected attribute from training data

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Increase model complexity

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Add synthetic data to balance groups

Question 14

A team is training a deep neural network on a large image dataset. They observe that the training loss decreases smoothly but validation loss oscillates. Which regularization technique should be applied?

Accepted Answer

Dropout. Dropout is the correct regularization technique because it randomly drops neurons during training, which prevents co-adaptation of features and reduces overfitting. This addresses the validation loss oscillation (a sign of overfitting) while allowing the training loss to decrease smoothly, as dropout only applies during training and not during validation.

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Data augmentation

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L1 regularization

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Batch normalization

Question 15

A fraud detection model has high precision but low recall. The cost of false negatives is very high. Which threshold adjustment should be made?

Accepted Answer

Decrease classification threshold. Decreasing the classification threshold makes the model more sensitive, classifying more instances as positive. This increases recall by catching more true positives, directly addressing the high cost of false negatives, even though precision may drop.

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Use class weights during training

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Apply SMOTE to the training data

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Increase classification threshold