AI0-001 Practice Exam 52 — 20 Questions

Question 1

A company serves a large language model (LLM) on a Kubernetes cluster. The inference latency is acceptable but the cost is high due to GPU usage. The model is 7 billion parameters and requires 16GB GPU memory. The team wants to reduce cost without increasing latency. Which strategy should they implement?

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

Accepted Answer

Use model quantization to reduce precision. Model quantization reduces the precision of the model's weights (e.g., from FP32 to INT8), which decreases the GPU memory footprint from 16GB to approximately 4GB for a 7B parameter model. This directly lowers GPU cost per inference while maintaining acceptable latency, as the model can run on fewer or cheaper GPUs without increasing inference time.

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Increase the batch size for inference

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Add more GPU nodes to distribute the load

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Switch to CPU-based inference

Question 2

A healthcare organization is deploying an AI system to analyze patient records and recommend treatment plans. To comply with data privacy regulations, what is the most important security measure to implement?

Accepted Answer

Anonymize patient data before processing. Anonymizing patient data before processing is the most important security measure because it directly addresses data privacy regulations like HIPAA and GDPR by removing personally identifiable information (PII) from the dataset. This ensures that even if a breach occurs, the data cannot be linked back to an individual, thereby minimizing compliance risk. While other measures like encryption and access control are essential, anonymization is the foundational step for lawful AI processing of sensitive health data.

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Enable detailed audit logging

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Encrypt all data at rest and in transit

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Implement role-based access control

Question 3

An AI engineer trains a deep learning model for image classification. After training, the training accuracy is 99% but validation accuracy is 85%. Which technique would best address this discrepancy?

Accepted Answer

Add dropout layers. The high training accuracy (99%) and lower validation accuracy (85%) indicate overfitting, where the model memorizes training data but fails to generalize. Dropout layers randomly deactivate neurons during training, forcing the network to learn more robust features and reducing overfitting. This technique directly addresses the discrepancy by improving validation performance without sacrificing training capacity.

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Increase data augmentation

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Decrease the learning rate

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Increase the number of layers

Question 4

An AI model for detecting fraudulent transactions has high precision but low recall. Which business impact is most likely?

Accepted Answer

Many fraudulent transactions go undetected. High precision means that when the model flags a transaction as fraudulent, it is very likely correct. However, low recall indicates that the model misses a significant proportion of actual fraudulent transactions. Therefore, the most likely business impact is that many fraudulent transactions go undetected, leading to financial losses.

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The model has no impact on fraud detection

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The model detects all fraudulent transactions

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Many legitimate transactions are flagged as fraud

Question 5

Under the EU AI Act, an AI system used for credit scoring is classified as high-risk. Which THREE obligations apply to the deployer of such a system?

Accepted Answer

Provide transparency information to affected individuals. Article 13 of the EU AI Act requires deployers of high-risk AI systems to provide clear and meaningful transparency information to affected individuals, including the system's capabilities, limitations, and the logic behind decisions. This obligation ensures that individuals subject to automated credit scoring understand how their data is being used and can exercise their rights under the regulation.

Answer

Register the system with a central EU database

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Publish the model's source code publicly

Question 6

Which TWO techniques should be considered when optimizing a deep learning model for deployment on edge devices with limited computational resources?

Accepted Answer

Model quantization. Model quantization reduces the precision of the model's weights and activations (e.g., from 32-bit floating point to 8-bit integer), which significantly decreases memory footprint and computational latency. This makes it a primary technique for deploying deep learning models on edge devices with limited resources.

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Apply adversarial training

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Use a GPU for inference

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Increase the number of layers

Question 7

A company deployed a machine learning model on a cloud inference service. Users report high latency during peak hours. The model is deployed on a single instance. Which action should the team take to reduce latency without significant architectural changes?

Accepted Answer

Enable autoscaling for the inference instances. Enabling autoscaling allows the inference service to automatically add instances during high demand, distributing the load and reducing latency. Increasing the model size would worsen latency. Switching to a batch inference pipeline would increase latency for real-time requests. Adding an API gateway does not address compute capacity.

Answer

Increase the model size to improve accuracy

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Switch to a batch inference pipeline

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Add an API gateway to route requests

Question 8

Which TWO of the following are common techniques to reduce overfitting in a neural network?

Accepted Answer

L2 regularization. L2 regularization (option C) reduces overfitting by adding a penalty term proportional to the squared magnitude of the weights to the loss function. This forces the network to keep weights small, preventing it from fitting noise in the training data and improving generalization.

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Increasing the number of hidden layers

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Using a larger learning rate

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Training for more epochs

Question 9

A self-driving car company is developing an object detection system using a convolutional neural network (CNN). The system needs to detect pedestrians and vehicles in real-time with high accuracy. Which technique can reduce inference time while maintaining accuracy?

Accepted Answer

Apply model pruning and quantization. Model pruning removes redundant or less important weights from the CNN, reducing computational load, while quantization converts floating-point weights to lower-precision integers (e.g., INT8). Together, they shrink model size and speed up inference without significantly degrading accuracy, making them ideal for real-time object detection in resource-constrained environments like autonomous vehicles.

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Use a pre-trained model and fine-tune it

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Add more convolutional layers

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Increase number of filters in each layer

Question 10

A machine learning engineer wants to evaluate a binary classifier. Which metric is MOST appropriate when the positive class is rare (e.g., 1% of total data)?

Accepted Answer

F1-score. When the positive class is rare (e.g., 1% of total data), accuracy is misleading because a classifier that always predicts the negative class would achieve 99% accuracy. The F1-score is the harmonic mean of precision and recall, making it robust to class imbalance by focusing on the positive class performance. It is the most appropriate metric for evaluating binary classifiers on imbalanced datasets.

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True negative rate

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Mean squared error

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Accuracy

Question 11

A data scientist is preparing a dataset for training a classification model. The dataset has a column with missing values in 5% of rows. Which action should the data engineer take to minimize bias?

Accepted Answer

Impute missing values with the median of the column. Imputing with the median preserves the distribution without significantly reducing sample size, minimizing bias. Removing rows reduces sample size, constant 999 introduces artificial outlier, and native handling may not be available.

Answer

Remove all rows with missing values

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Replace missing values with a constant such as 999

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Use a model that can handle missing values natively

Question 12

An AI model's performance drops significantly in production compared to testing. The data shows distribution shift. What is the best first step?

Accepted Answer

Retrain model with new data. (Retrain model with new data) is correct because retraining with more representative data adapts to distribution shift. Option A (Add more features) may not address the shift. Option C (Change algorithm) is a larger change without addressing data. Option D (Reduce model complexity) might worsen performance.

Answer

Add more features

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Use a different algorithm

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Reduce model complexity

Question 13

Which TWO are common attack vectors against AI systems? (Choose two.)

Accepted Answer

Data poisoning. Data poisoning is a common attack vector against AI systems where an attacker manipulates the training data to corrupt the model's learning process. By injecting malicious or misleading samples into the training dataset, the attacker can cause the model to learn incorrect patterns, leading to biased outputs, reduced accuracy, or targeted misclassifications. This directly targets the integrity of the AI's training pipeline.

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SQL injection

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Cross-site scripting

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Buffer overflow

Question 14

Which THREE of the following are key principles of AI ethics as defined by major frameworks?

Accepted Answer

Transparency. Transparency is a core principle of AI ethics because it requires that AI systems be open about their purpose, data sources, and decision-making processes. Major frameworks like the OECD AI Principles and the EU Ethics Guidelines for Trustworthy AI emphasize transparency to enable auditability and informed consent. Without transparency, stakeholders cannot verify that an AI system operates as intended or identify potential biases.

Answer

Scalability

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Latency

Question 15

A data engineer is designing a data pipeline for a real-time recommendation system. The pipeline must handle high velocity streams and ensure data quality. Which three components should be included in the pipeline? (Select THREE).

Accepted Answer

A stream processing engine like Apache Kafka Streams. Apache Kafka Streams is a correct choice because it is a stream processing library specifically designed for building real-time applications and microservices that process data in motion. For a high-velocity recommendation pipeline, it provides exactly-once semantics, stateful processing (e.g., windowed joins, aggregations), and seamless integration with Kafka topics, enabling low-latency transformations without requiring an external cluster.

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A data warehouse for historical analysis

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A batch processing framework like Apache Spark

Question 16

A data analyst wants to predict housing prices based on square footage, number of bedrooms, and location. Which machine learning approach is most suitable?

Accepted Answer

Linear regression. Linear regression is the most suitable approach because the problem involves predicting a continuous numeric target (housing prices) from multiple independent variables (square footage, bedrooms, location). Linear regression models the linear relationship between the features and the target, providing interpretable coefficients and efficient training for this type of regression task.

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K-means clustering

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Decision tree regression

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Association rule mining

Question 17

The exhibit shows a model configuration for a classification task with 10 classes. What is wrong with this setup?

Accepted Answer

The loss function should be categorical crossentropy, not mean squared error. In a multi-class classification task with 10 classes, the correct loss function is categorical crossentropy because it measures the dissimilarity between the true probability distribution and the predicted probability distribution. Mean squared error (MSE) is designed for regression tasks and penalizes errors in a way that is not suitable for classification probabilities, leading to poor gradient behavior and slower convergence.

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The metric should be precision, not accuracy

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The activation should be sigmoid in hidden layers

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The optimizer should be SGD, not Adam

Question 18

An AI developer observes that the training accuracy of a neural network is high, but the test accuracy is low. The model uses a ReLU activation function and Adam optimizer. Which approach is most likely to improve test accuracy?

Accepted Answer

Add L2 regularization to the loss function. L2 regularization adds a penalty on large weights, reducing overfitting and improving test accuracy.

Answer

Increase the learning rate

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Switch to a stochastic gradient descent optimizer

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Increase the number of epochs

Question 19

A healthcare startup is building a diagnostic support system using a large language model. The system must provide accurate, evidence-based answers and avoid generating harmful or fabricated information. Which THREE techniques should be implemented to achieve this? (Choose 3)

Accepted Answer

Retrieval-Augmented Generation (RAG). RAG grounds answers in retrieved evidence, fine-tuning can align with medical domain, and prompt engineering can enforce accuracy and safety.

Answer

Disabling output filtering to speed up generation

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Increasing the temperature parameter to encourage creativity

Question 20

A team is deploying an AI model that predicts patient readmission risk. The model was trained on data from three hospitals but will be used in a fourth hospital with different patient demographics. What is the most important security risk to assess?

Accepted Answer

Adversarial attacks that cause misclassification. Using a model on data from a different distribution (population shift) can degrade performance, but from a security perspective, the main risk is adversarial attacks that exploit the model's unfamiliarity with new data. Model inversion and poisoning are training-time attacks; data breach is an operational risk but not specific to this scenario.

Answer

Data poisoning during training

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Model inversion to extract patient data

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Data breach of the inference API