AI0-001 Practice Test 43 — 15 Questions

Question 1

Which TWO techniques are specifically designed to protect individual privacy when training AI models?

Accepted Answer

Differential privacy. Differential privacy (C) is a technique that adds calibrated noise to training data or model updates, ensuring that the output of the model does not reveal whether any specific individual's data was included. This provides a formal mathematical guarantee of privacy, quantified by the epsilon parameter, making it a direct privacy-preserving method for AI training.

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Dropout

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Model pruning

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Regularization

Question 2

An AI system misclassifies rare but critical events. The team considers using synthetic data. Which consideration is MOST important for ensuring the synthetic data improves performance on real rare events?

Accepted Answer

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.

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The synthetic data should include a wide variety of events, even if not realistic.

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The synthetic data should be generated using an unsupervised generative model.

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The synthetic data should be as large as possible to cover all possibilities.

Question 3

A research lab trains a language model using DP-SGD. What primary privacy risk does this technique mitigate?

Accepted Answer

Membership inference attacks. DP-SGD (Differentially Private Stochastic Gradient Descent) mitigates membership inference attacks by adding calibrated noise to gradients during training, which bounds the influence any single training example can have on the final model. This differential privacy guarantee makes it difficult for an adversary to determine whether a specific data point was included in the training set, directly addressing the core risk of membership inference.

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Data poisoning attacks

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Adversarial patch attacks

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Model inversion attacks

Question 4

A company wants to deploy an AI model for real-time inference on edge devices with limited computational resources. Which model architecture would be MOST suitable?

Accepted Answer

MobileNet. MobileNet is specifically designed for mobile and edge devices using depthwise separable convolutions, which drastically reduce the number of parameters and computational cost while maintaining acceptable accuracy. This makes it the most suitable choice for real-time inference on resource-constrained edge hardware.

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YOLOv4

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ResNet-152

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BERT

Question 5

A retail company uses a machine learning model to predict daily sales. The model takes features like past sales, promotions, holidays, and weather data. Recently, the model's accuracy dropped significantly. The data engineer checks the data pipeline and finds that the weather data source changed from a free API to a new paid API that provides more detailed data. The new data includes additional attributes like humidity and wind speed, but the existing pipeline only ingests temperature and precipitation. Also, the time zone format changed from UTC to local time. The model was trained on the old format. Which action should the engineer take first to restore model performance?

Accepted Answer

Transform the new data to match the old format (time zone and selected features) and retrain the model.. The immediate problem is the time zone change causing misalignment between training and inference data. Transforming the new data to match the old format ensures consistency. Retraining with all new features may introduce drift; reverting may not be possible; merging data without alignment causes inconsistency.

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Add a new step to merge old and new weather data before feeding to the model.

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Revert to the old weather API.

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Retrain the model with the new data including all new features.

Question 6

A data scientist submits a model training job to a cloud ML platform. The job fails with an error: "Out of memory: Killed process." The training code is proven to work on the developer's local machine with 16GB RAM. The cloud instance has 32GB RAM. What is the most likely cause?

Accepted Answer

The training code is loading the entire dataset into memory without batch processing. The error 'Out of memory: Killed process' occurs when the operating system's OOM killer terminates a process that exceeds available RAM. Even though the cloud instance has 32GB RAM, if the training code loads the entire dataset into memory without batch processing (e.g., using pandas.read_csv() without chunking), it can consume far more memory than the dataset size due to Python object overhead, temporary copies, and intermediate tensors. The local machine with 16GB RAM may have worked due to a smaller dataset or different memory pressure, but the cloud instance's 32GB is insufficient if the code is not memory-efficient.

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The cloud instance does not have enough memory

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The training model is too large for the GPU memory

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The cloud instance ran out of disk space

Question 7

A model's predicted probabilities are well-calibrated overall but the model systematically assigns higher probabilities to one demographic group even when the actual outcome likelihood is the same. Which fairness issue is present?

Accepted Answer

Calibration disparity. Calibration fairness requires that for each predicted probability, the actual outcome rate is the same across groups. The scenario describes a calibration disparity. Demographic parity would involve different selection rates. Equalised odds involves equal error rates. Individual fairness is about treating similar individuals similarly.

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Demographic parity violation

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Equalised odds violation

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Individual fairness violation

Question 8

During the data preparation phase of an AI project, a data scientist discovers that the target variable in a binary classification dataset is heavily imbalanced: 95% negative class and 5% positive class. Which technique should be applied to improve model performance on the minority class?

Accepted Answer

Apply oversampling of the minority class using techniques like SMOTE. Oversampling the minority class (e.g., SMOTE) or undersampling the majority class are standard techniques to handle imbalanced datasets and improve recall on the minority class.

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Remove all samples from the majority class to balance the dataset

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Normalize all numerical features to have zero mean and unit variance

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Use a train-test split of 80-20 without any modification

Question 9

Which TWO of the following are effective defenses against adversarial examples in AI systems?

Accepted Answer

Train the model with adversarial examples (adversarial training). Adversarial training (A) is effective because it exposes the model to perturbed inputs during training, forcing it to learn robust decision boundaries that are less sensitive to small, malicious perturbations. Input sanitization and feature squeezing (D) reduce the attack surface by compressing input features (e.g., reducing bit depth or spatial smoothing) to eliminate adversarial noise before inference, making it harder for an attacker to craft a successful perturbation.

Answer

Use an ensemble of models and majority voting

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Increase the model's sensitivity to input changes

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Reduce model complexity through pruning

Question 10

Which TWO of the following are key characteristics of unsupervised learning?

Accepted Answer

It uses data without labeled responses. Unsupervised learning algorithms, such as k-means clustering or hierarchical clustering, operate exclusively on input data that has no labeled responses. The model must infer the underlying structure directly from the features without any ground-truth outputs to guide it, which is the defining characteristic of unsupervised learning.

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It predicts a target variable based on input features

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It requires a reward signal to learn optimal actions

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It typically requires a separate validation set for tuning

Question 11

An ML operations team needs to monitor a deployed model's performance. Which TWO metrics are most useful for detecting concept drift in a regression model? (Choose two.)

Accepted Answer

Distribution of residuals between predictions and actuals. Monitoring the distribution of residuals (predicted vs. actual values) directly reveals when the relationship between inputs and outputs has shifted, which is the essence of concept drift. In a regression model, if the residuals become systematically biased or their variance changes over time, it indicates that the underlying data-generating process has changed, even if input feature distributions remain stable.

Answer

Distribution of input features

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Classification accuracy

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Model inference latency

Question 12

Which TWO of the following are common activation functions used in neural networks? (Choose two.)

Accepted Answer

ReLU. ReLU (Rectified Linear Unit) is a widely used activation function that outputs the input directly if it is positive, and zero otherwise, introducing non-linearity while mitigating the vanishing gradient problem. Sigmoid is another common activation function that maps any real-valued input to a value between 0 and 1, making it useful for binary classification output layers. Both are fundamental building blocks in neural network architectures.

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Gradient descent

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LSTM

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Dropout

Question 13

A team is building a natural language processing (NLP) model to analyze customer feedback. They have a large corpus of unlabeled text data and want to generate word embeddings that capture semantic meaning. Which approach should they use?

Accepted Answer

Word2Vec. Word2Vec is the correct approach because it learns dense, distributed word embeddings from large unlabeled corpora by training a shallow neural network to predict words in context (CBOW) or context from words (Skip-gram). This captures semantic relationships such as analogy and similarity, which is essential for analyzing customer feedback without labeled data.

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One-hot encoding

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TF-IDF vectorization

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Bag-of-words model

Question 14

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 15

A hospital's radiology department uses an AI model to detect lung nodules in CT scans. The model was trained on data from a specific brand of scanners and patient demographics common in Europe. Recently, the hospital acquired new scanners from a different manufacturer and started serving a more diverse patient population. Over the past month, the model's false-positive rate has increased by 15% and false-negative rate by 8%. The radiologists are losing confidence and are considering abandoning the AI tool altogether. The IT team has verified that the model inference is running correctly and the hardware is performing as expected. The data science team suspects the problem is related to the change in input data distribution. The hospital's AI operations policy requires that any model update must be validated on at least 500 recent cases before deployment. What is the BEST course of action for the AI operations team?

Accepted Answer

Collect 500 recent CT scans from the new scanners, retrain the model on a combined old and new dataset, and validate before deployment.. Collecting 500 recent CT scans from the new scanners and retraining on a combined dataset addresses the data drift (change in scanner brand and patient demographics). This approach updates the model to the new distribution while retaining knowledge from the original data, and it complies with the validation requirement of 500 recent cases. Option A is wrong because rolling back and restricting use does not solve the underlying issue and limits utility. Option C is wrong because retraining on the original data with increased regularization does not incorporate the new data distribution, so it would not fix the performance degradation. Option D is wrong because adjusting the decision threshold only trades off false positives and false negatives without addressing the root cause of data drift, and it does not involve retraining or validation as per policy.

Answer

Roll back to the previous model version and restrict use of the AI tool to only European patients.

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Retrain the model using the original training data but with increased regularization to avoid overfitting.

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Adjust the model's decision threshold to reduce false positives and then monitor for two weeks.