AI0-001 Practice Test 31 — 15 Questions

Question 1

An organisation needs to deploy PyTorch models on mobile devices with minimal latency. Which framework or tool should they use to convert and optimise the model for on-device inference?

Accepted Answer

TorchScript. TorchScript is the correct choice because it is PyTorch's native model serialization and optimization format, designed specifically for deploying PyTorch models on mobile devices with minimal latency. It allows you to trace or script a PyTorch model into a static graph that can be run efficiently on iOS and Android via the PyTorch Mobile runtime, without the overhead of Python interpreter.

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TensorFlow Lite

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Keras for mobile

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ONNX Runtime with Core ML conversion

Question 2

A company uses a large language model (LLM) to generate customer support responses. They notice the model sometimes produces harmful outputs. Which implementation strategy best reduces this risk while maintaining performance?

Accepted Answer

Fine-tune the model using reinforcement learning from human feedback. Reinforcement learning from human feedback (RLHF) directly trains the model to align its outputs with human preferences for safety and helpfulness, reducing harmful outputs while preserving performance. Unlike superficial filters or prompts, RLHF adjusts the model's internal behavior through reward modeling and policy optimization, making it the most effective strategy for sustained safety improvements.

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Implement a keyword-based output filter

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Use a smaller, less capable model

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Add system prompts instructing the model to be safe

Question 3

Which TWO are key requirements for AI governance under the EU AI Act for high-risk AI systems? (Choose two.)

Accepted Answer

Human oversight. The EU AI Act mandates that high-risk AI systems must incorporate human oversight mechanisms to ensure that humans can intervene or override the system's decisions when necessary. This requirement is designed to prevent or minimize risks to health, safety, and fundamental rights, and it is a core governance obligation under Article 14 of the Act.

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Regular performance benchmarks

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Open-source licensing

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Mandatory use of cloud

Question 4

A startup wants to identify unusual patterns in network traffic to detect potential security breaches. They have a large dataset of normal traffic but very few labeled attacks. Which machine learning approach is MOST suitable?

Accepted Answer

Unsupervised anomaly detection. Unsupervised anomaly detection is the most suitable approach because the startup has a large dataset of normal traffic but very few labeled attacks. This technique learns the baseline of normal behavior from unlabeled data and flags deviations as potential anomalies, which is ideal for detecting unknown or rare attack patterns without requiring labeled attack samples.

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Supervised classification with logistic regression

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Reinforcement learning

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Semi-supervised learning

Question 5

Which TWO are valid techniques to reduce overfitting in a deep neural network? (Choose TWO.)

Accepted Answer

L2 regularization. L2 regularization (option C) is a valid technique to reduce overfitting by adding a penalty term proportional to the square of the weight magnitudes to the loss function. This discourages the network from learning overly complex patterns, effectively shrinking weights and improving generalization. Dropout (option E) randomly drops a fraction of neurons during training, which prevents co-adaptation of features and forces the network to learn more robust representations, also reducing overfitting.

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Increase batch size

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Increase learning rate

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Gradient clipping

Question 6

An MLOps team observes that their production inference API experiences increasing latency as more concurrent requests arrive. They need to scale horizontally while maintaining session state of preprocessing steps. Which deployment strategy should they implement?

Accepted Answer

Deploy multiple instances behind a round-robin load balancer with sticky sessions. Sticky sessions (session affinity) ensure that all requests from a given client are routed to the same backend instance, preserving the in-memory session state of preprocessing steps. Combined with a round-robin load balancer, this allows horizontal scaling while maintaining stateful behavior, which is essential for the described latency issue under concurrent load.

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Deploy stateless containers without session persistence

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Use a single larger GPU instance to handle all requests

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Implement a message queue (e.g., Kafka) to buffer requests

Question 7

Refer to the exhibit. A batch inference job fails with the given logs. What is the most likely root cause of the failure?

Accepted Answer

The input data contains missing values that are not handled in preprocessing. The logs indicate a 'ValueError' or similar exception when the batch inference job attempts to process the input data. This error typically arises when the preprocessing pipeline encounters missing values (e.g., NaN or None) that it cannot handle, causing the job to fail. Option B is correct because missing values not handled in preprocessing are a common root cause for such failures, especially when the training data had no missing values but the inference data does.

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The input data has values that exceed the model's expected range

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The model was not trained to handle categorical features

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The model version is outdated and incompatible with the current preprocessing pipeline

Question 8

A team is building a recommendation system using collaborative filtering. They have a sparse user-item matrix. Which technique should they use to handle the sparsity and improve recommendations?

Accepted Answer

Matrix factorization. Matrix factorization (B) is the correct technique because it decomposes the sparse user-item matrix into lower-dimensional latent factor matrices, effectively capturing underlying patterns and filling in missing entries. This directly addresses sparsity by learning dense representations that generalize beyond observed interactions, which is a core strength in collaborative filtering for recommendation systems.

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Association rule mining

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k-nearest neighbors

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Content-based filtering

Question 9

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 10

Which TWO are best practices for deploying AI models in a containerized production environment? (Select TWO.)

Accepted Answer

Use an orchestration platform like Kubernetes for scaling and health management. Orchestration platforms like Kubernetes provide automated scaling, self-healing, and rolling updates for containerized AI models. Kubernetes uses liveness and readiness probes to monitor model health and restart failed containers, ensuring high availability in production.

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Always pull the latest image tag for automatic updates

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Store model artifacts inside the container image for portability

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Configure JVM heap arguments inside the container if using Java

Question 11

Which TWO of the following are common activation functions used in deep neural networks?

Accepted Answer

ReLU. ReLU (Rectified Linear Unit) is a common activation function in deep neural networks because it introduces non-linearity while being computationally efficient, outputting the input directly if positive and zero otherwise. It helps mitigate the vanishing gradient problem, making it a default choice for hidden layers in many architectures.

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Linear Regression

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Support Vector Machine

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K-means

Question 12

A team is training a language model using a large text corpus. They want to ensure the model does not learn biased associations between gender and professions. Which data engineering technique should they apply?

Accepted Answer

Balance the representation of professions across genders. Balancing the representation of professions across genders in the training data directly addresses the root cause of biased associations. By ensuring that each profession appears with roughly equal frequency for all gender references, the model learns statistical correlations that are fair rather than skewed by imbalanced data. This is a fundamental data engineering technique for bias mitigation, as it prevents the model from encoding spurious correlations between gender and occupation.

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Remove all gender-related words from the text

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Use a pre-trained model that is already debiased

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Apply adversarial debiasing during training

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 machine learning team is developing a model to predict server failure from telemetry data. They use a deep neural network with 3 hidden layers. After training, the model achieves 99% accuracy on training data but only 85% on validation data. Which technique should the team apply to reduce the generalization error?

Accepted Answer

Apply L2 regularization. The model exhibits high variance (overfitting) because it achieves 99% accuracy on training data but only 85% on validation data. L2 regularization (also known as weight decay) adds a penalty proportional to the squared magnitude of the weights to the loss function, which discourages the network from fitting noise in the training data and improves generalization. This directly reduces the gap between training and validation performance.

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Increase the number of hidden layers

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Increase the learning rate

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Add more training data

Question 15

A data engineer is splitting a dataset into training, validation, and test sets for a machine learning project. The dataset is large and representative of the population. Which split ratio is commonly recommended?

Accepted Answer

80% training, 10% validation, 10% test. (80% training, 10% validation, 10% test) is commonly recommended for large, representative datasets because it provides sufficient data for model training while retaining enough samples in the validation and test sets to reliably evaluate model performance and detect overfitting. This split balances the need for a robust training set with the requirement for statistically meaningful holdout sets, as recommended in standard machine learning practices for AI model development.

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90% training, 5% validation, 5% test

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70% training, 20% validation, 10% test

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50% training, 25% validation, 25% test