AI0-001 Practice Test 34 — 15 Questions

Question 1

A company is considering using an open-source large language model for a commercial application. Which intellectual property consideration is MOST important when deciding between open-source and proprietary models?

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

Accepted Answer

The model's license terms and any restrictions on commercial use. Understanding the model's license is critical because open-source licenses can have restrictions on commercial use, attribution requirements, or copyleft provisions that affect how the model can be used and distributed. The other options are less directly relevant to the open vs proprietary decision.

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The model's accuracy on benchmark tasks

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The size of the model's parameter count

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The model's training data provenance

Question 2

A manufacturing company is using a convolutional neural network (CNN) to detect defects on an assembly line. The model was trained on a balanced dataset of defective and non-defective parts. In production, the model shows high precision (95%) but very low recall (50%). The production line manager wants to minimize missed defects (false negatives). The data scientist has access to the original training data and can retrain the model. Which strategy is most effective for increasing recall while maintaining acceptable precision?

Accepted Answer

Lower the classification threshold for the defective class. Lowering the classification threshold for the defective class directly addresses the recall issue by allowing more samples to be classified as defective, which reduces false negatives. This is the most immediate and effective method because it does not require retraining and can be tuned to balance precision and recall based on the manager's priority of minimizing missed defects.

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Apply data augmentation to defective images

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Use a bagging ensemble of CNNs

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Oversample the defective class in training

Question 3

You are a security engineer at a large e-commerce company that uses an AI-based recommendation system. The system is deployed on a Kubernetes cluster and uses a TensorFlow model served via REST API. Recently, the security team detected unusual API calls that caused the model to return incorrect recommendations. Analysis shows that the inputs were crafted to maximize prediction error. The team suspects an adversarial attack. You need to implement a solution that detects and mitigates such attacks in real-time without requiring model retraining. Which approach should you take?

Accepted Answer

Implement an input validation filter to detect and block anomalous inputs. An input validation filter can detect and block adversarial inputs in real-time by analyzing statistical properties (e.g., outlier detection, perturbation magnitude) without modifying the model. This approach is lightweight, operates at the API gateway level, and does not require retraining, making it suitable for immediate deployment against crafted inputs that maximize prediction error.

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Increase the number of model replicas to distribute the load

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Retrain the model with adversarial examples

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Roll back the model to a previous version that was not attacked

Question 4

A self-driving car uses an AI model that learns by trial and error, receiving rewards for correct actions and penalties for mistakes. This type of learning is:

Accepted Answer

Reinforcement learning. Reinforcement learning (RL) is the correct answer because the self-driving car's AI model learns through trial and error, receiving rewards for correct actions and penalties for mistakes. This feedback-driven process, where an agent interacts with an environment to maximize cumulative reward, is the defining characteristic of reinforcement learning, not supervised or unsupervised learning.

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Supervised learning

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Unsupervised learning

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Transfer learning

Question 5

A company is training a model on proprietary data and wants to prevent data poisoning. Which TWO practices are most important? (Select TWO.)

Accepted Answer

Implementing access controls on the training dataset. Implementing access controls on the training dataset (Option A) is critical because it restricts who can read, modify, or delete the data, thereby preventing unauthorized actors from injecting malicious samples. This is a fundamental security measure to protect the integrity of the training pipeline against data poisoning attacks. Validating the integrity of training data (Option B) ensures that the data has not been tampered with, for example by using checksums or cryptographic hashes, which directly counters poisoning attempts that rely on corrupted input.

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Using a larger model

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Increasing training epochs

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Using homomorphic encryption

Question 6

Which TWO of the following are appropriate uses of unsupervised learning?

Accepted Answer

Detecting unusual patterns in network traffic that may indicate a cyberattack. Unsupervised learning discovers hidden patterns or structures in unlabeled data. Detecting unusual patterns in network traffic (option C) is a classic anomaly detection task, often performed using clustering or autoencoders, where the model learns 'normal' behavior and flags deviations without requiring labeled attack data.

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Classifying emails as spam or not spam

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Predicting the sale price of a house given its features

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Identifying a person from a photo

Question 7

A company deploys a machine learning model for fraud detection. After one month, the false positive rate has increased significantly. The model is retrained weekly on all historical data. What is the MOST effective immediate action?

Accepted Answer

Retrain the model on only the most recent 30 days of data.. The false positive rate increase suggests the model is reacting to a shift in the underlying data distribution (concept drift). Retraining on only the most recent 30 days of data (option D) is the most effective immediate action because it focuses the model on the current fraud patterns, discarding stale historical data that may no longer be representative. This approach directly addresses the drift by adapting the model to the latest behavior.

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Replace the model with a simpler logistic regression model.

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Continue retraining weekly on all historical data.

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Adjust the classification threshold to reduce false positives.

Question 8

A company deploys an AI model for loan approval. The model shows bias against a protected group. The team decides to use adversarial debiasing. What is the PRIMARY advantage of this approach?

Accepted Answer

It reduces bias while preserving predictive performance by learning representations that are invariant to sensitive attributes.. Adversarial debiasing is an in-processing technique that trains a primary model to predict the target (e.g., loan approval) while simultaneously training an adversary to predict the sensitive attribute from the model's learned representations. The primary model is penalized when the adversary succeeds, forcing it to learn representations that are invariant to the sensitive attribute. This reduces bias while preserving predictive performance because the model retains the ability to learn task-relevant patterns that are not correlated with the protected attribute.

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It guarantees the model's predictions are private.

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It is simpler to implement than pre-processing techniques.

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It ensures equal approval rates across all groups.

Question 9

A company develops an AI model that recommends job candidates. The model inadvertently discriminates against a protected group. Which approach is most effective for mitigating this bias?

Accepted Answer

Use a fairness-aware machine learning algorithm. Fairness-aware machine learning algorithms explicitly incorporate fairness constraints or objectives during model training, directly addressing and mitigating bias against protected groups. Unlike simple removal of protected attributes, these algorithms can detect and correct for proxy discrimination and disparate impact, ensuring the model's recommendations are equitable by design.

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Remove the protected attribute from the training data

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Analyze model predictions after deployment

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Collect more training data from the protected group

Question 10

Which THREE are effective methods for ensuring data privacy in AI training? (Choose three.)

Accepted Answer

Data anonymization. Data anonymization (B) is correct because it removes or obfuscates personally identifiable information (PII) from training datasets, ensuring that individuals cannot be re-identified. This is a foundational privacy technique that directly addresses regulatory requirements like GDPR and CCPA by breaking the link between data and specific individuals.

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Data encryption at rest

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Data replication

Question 11

A healthcare organization is deploying an AI model to predict patient readmission risk. They must comply with regulations that protect patient privacy. Which TWO techniques should they implement to enhance privacy preservation?

Accepted Answer

Differential privacy. Differential privacy (B) is correct because it adds calibrated noise to the training data or model outputs, ensuring that the inclusion or exclusion of any single patient's record does not significantly affect the model's predictions. This provides a formal mathematical guarantee of privacy, which is essential for complying with regulations like HIPAA that protect patient data.

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Data augmentation

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Model quantization

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Dropout regularization

Question 12

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 13

A financial services company has a real-time fraud detection system that uses Apache Kafka to stream transaction events, a TensorFlow Serving model for scoring, and a Redis cache for lookup of historical fraud patterns. The system processes 10,000 transactions per second with an SLA of 100ms latency per transaction. Recently, after a model update, the latency for some transactions spiked to over 500ms, causing timeouts. The model uses a deep neural network with 10 million parameters. The engineering team suspects the issue is due to increased model inference time. Which action should be taken to reduce latency without significant loss in accuracy?

Accepted Answer

Quantize the model weights from FP32 to FP16. The latency spike is caused by increased model inference time after a model update. Quantizing model weights from FP32 to FP16 reduces memory bandwidth and computation requirements, directly speeding up inference on compatible hardware (e.g., GPUs with Tensor Cores) with minimal accuracy loss. This addresses the root cause—model inference latency—without changing the system architecture.

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Add more Redis nodes to the cache cluster

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Increase the number of Kafka partitions and consumer threads

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Decrease the inference batch size from 32 to 1

Question 14

A data scientist is building a regression model to predict house prices. The dataset contains features such as square footage, number of bedrooms, and year built. Initial model performance is poor, and the scientist suspects that feature engineering could help. Which approach is most likely to improve model accuracy?

Accepted Answer

Create interaction terms such as bedrooms times square footage. Creating interaction terms like bedrooms × square footage captures non-linear relationships and synergies between features that a linear model alone cannot represent. In real estate, the effect of square footage on price often depends on the number of bedrooms (e.g., a large house with few bedrooms may be less valuable), so interaction terms allow the model to learn these conditional patterns, directly improving predictive accuracy.

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Use only linear features because polynomial terms overfit

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Remove all features except square footage to reduce noise

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Add random noise to the target variable to increase variance

Question 15

Which TWO strategies are effective for handling missing values in a dataset when the missingness is not random (MNAR)?

Accepted Answer

Multiple imputation using chained equations. Multiple imputation using chained equations (MICE) is effective for MNAR because it models each variable with missing values as a function of other variables, iteratively generating plausible values that preserve the relationships and uncertainty in the data. This approach can account for the systematic pattern of missingness by incorporating auxiliary variables that are correlated with both the missing values and the missingness mechanism, making it robust even when missingness depends on unobserved data.

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Listwise deletion

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KNN imputation

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Mean imputation