AI0-001 Practice Test 36 — 15 Questions

Question 1

An organization wants to fine-tune a 7B parameter LLM for a specialized legal document summarization task. They have a small labeled dataset (500 examples) and limited GPU budget. Which THREE techniques should they consider? (Choose three.)

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

Accepted Answer

Use LoRA (Low-Rank Adaptation). PEFT methods like LoRA and QLoRA are designed for efficient fine-tuning with limited resources. Instruction tuning datasets improve task performance. Full fine-tuning is too expensive.

Answer

Train a new model from scratch on legal text

Answer

Full fine-tuning of all model parameters

Question 2

A team is deploying an AI microservice for real-time object detection in streaming video. Which TWO integration patterns are most appropriate? (Choose two.)

Accepted Answer

Streaming responses for real-time inference. AI microservices are deployed as independent services, and streaming responses are needed for real-time video processing.

Answer

Batch processing with nightly jobs

Answer

Synchronous request-response with long timeouts

Answer

Monolithic application deployment

Question 3

A team is designing an AI microservice for image classification. Which THREE practices should they implement for effective integration and testing?

Accepted Answer

Use streaming responses for real-time feedback. Unit tests for data pipelines ensure data quality, integration tests verify API communication, and streaming responses improve user experience. These cover the main integration concerns.

Answer

Train the model on the full dataset before deployment

Answer

Deploy the model as a monolithic application to simplify testing

Question 4

A data science team is training an image classification model for a medical imaging application. To prevent data leakage, they must partition the dataset correctly. Which approach ensures that no patient images appear in both training and test sets?

Accepted Answer

Split by patient ID so that all images of a patient go to one set only. Data leakage occurs when information from the test set leaks into training. Splitting by patient ID ensures that all images from the same patient are kept together in one partition.

Answer

Shuffle the dataset and take the first 80% for training and last 20% for testing

Answer

Use k-fold cross-validation without grouping

Answer

Randomly split all images into training and test sets

Question 5

A healthcare startup is developing a deep learning model to detect diabetic retinopathy from retinal images. The model is trained on a dataset of 10,000 labeled images. During initial testing, the model achieves 99% accuracy on the training set but only 85% on the test set. The startup wants to deploy the model in a clinical setting where false negatives (missing a disease) are critical. The team has access to additional unlabeled retinal images from multiple sources. Which strategy should the team use to improve the model's generalization and reduce false negatives?

Accepted Answer

Use semi-supervised learning with the unlabeled images to improve feature representations. Semi-supervised learning leverages the large pool of unlabeled retinal images to learn robust feature representations, which helps the model generalize better to unseen data. By reducing overfitting (the gap between 99% training and 85% test accuracy), this approach directly improves test-set performance. Additionally, semi-supervised methods can be tuned to emphasize recall, thereby reducing false negatives critical in clinical diabetic retinopathy screening.

Answer

Apply aggressive data augmentation to the training set

Answer

Increase the learning rate during training

Answer

Add more convolutional layers to the model

Question 6

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.

Answer

Use class weights during training

Answer

Apply SMOTE to the training data

Answer

Increase classification threshold

Question 7

A startup is training a large language model and wants to reduce its environmental impact. Which TWO practices are considered green AI?

Accepted Answer

Use energy-efficient hardware (e.g., TPUs). Using energy-efficient hardware such as Tensor Processing Units (TPUs) or specialized AI accelerators reduces the power consumption per floating-point operation, directly lowering the carbon footprint of training large language models. This aligns with green AI principles by optimizing the energy-to-performance ratio.

Answer

Train on the largest possible dataset

Answer

Use redundant backup servers

Answer

Increase batch size to maximum

Question 8

A bank wants to detect fraudulent transactions in real-time. The dataset is highly imbalanced (99.9% legitimate, 0.1% fraud). Which evaluation metric is MOST appropriate for model performance?

Accepted Answer

AUC-ROC. AUC-ROC is the most appropriate metric because it evaluates the model's ability to distinguish between the minority fraud class (0.1%) and the majority legitimate class across all classification thresholds, without being biased by the extreme class imbalance. Unlike accuracy, AUC-ROC remains robust when the dataset is 99.9% legitimate, as it measures the true positive rate against the false positive rate, providing a comprehensive view of model performance for rare event detection.

Answer

Accuracy

Answer

Recall

Answer

Precision

Question 9

A data scientist is preparing a dataset for a classification model. The dataset contains several categorical variables with high cardinality. Which TWO encoding methods are appropriate for converting these categorical variables into numerical features?

Accepted Answer

One-hot encoding. One-hot encoding is appropriate for high-cardinality categorical variables because it creates binary columns for each category, allowing the model to treat each category as an independent feature without imposing an ordinal relationship. This is crucial for classification models that assume numerical inputs, as it prevents the model from misinterpreting arbitrary integer labels as having meaningful order or magnitude.

Answer

Min-max scaling

Answer

K-means clustering

Answer

Principal component analysis (PCA)

Question 10

A startup is building a chatbot to handle customer inquiries. They want the chatbot to understand context and provide accurate responses without requiring extensive labeled data. Which AI approach is most suitable?

Accepted Answer

Transfer learning with a pre-trained transformer model. Transfer learning with a pre-trained transformer model (e.g., BERT, GPT) is the most suitable approach because it allows the chatbot to understand context and generate accurate responses using knowledge learned from vast general-domain text, requiring only minimal fine-tuning on the startup's specific customer inquiry data. This eliminates the need for extensive labeled datasets, as the model already captures nuanced language patterns and contextual relationships through its self-attention mechanism.

Answer

Reinforcement learning from human feedback

Answer

Rule-based natural language processing

Answer

Convolutional neural networks (CNNs)

Question 11

A global retailer uses an AI model to forecast demand across thousands of stores. After deployment, the model's predictions become less accurate during holiday seasons. The training data included two years of holiday periods. What is the most effective operational strategy to handle this recurring seasonal drift?

Accepted Answer

Implement a scheduled retraining cycle just before each holiday period. Scheduled retraining just before each holiday season directly addresses the recurring seasonal drift by updating the model with the most recent holiday data patterns. This is the most effective operational strategy because it proactively aligns the model with the known, periodic shift in demand behavior, rather than reacting to errors or relying on static historical data.

Answer

Deploy an anomaly detection system to flag holiday prediction outliers

Answer

Use an ensemble of models trained on different time periods

Answer

Increase the volume of training data by including five years of history

Question 12

A developer is using a pre-trained BERT model for a question-answering system. They want to ensure the model can handle out-of-vocabulary words. Which component of the BERT architecture is responsible for this?

Accepted Answer

WordPiece tokenisation. WordPiece tokenisation is the component of BERT that handles out-of-vocabulary (OOV) words by breaking them into subword units (e.g., 'playing' → 'play' + '##ing'). This allows the model to represent any word, even unseen ones, as a sequence of known subword tokens, ensuring no word is truly out of vocabulary.

Answer

Positional encoding

Answer

Feed-forward layers

Answer

Attention mechanism

Question 13

Refer to the exhibit. Which model is NOT in full compliance with the policy?

Accepted Answer

CreditScorer v2. CreditScorer v2 is not in full compliance because it uses a black-box neural network that cannot provide explainability for its credit decisions, violating the policy's requirement for model interpretability and transparency. The policy mandates that all models must support post-hoc explanation methods such as SHAP or LIME, which CreditScorer v2 lacks due to its opaque architecture.

Answer

ChurnPredict v1

Answer

FraudDetect v4

Answer

LoanApproval v3

Question 14

Which THREE are key principles of trustworthy AI according to the OECD?

Accepted Answer

Robustness. Robustness is a key principle of trustworthy AI according to the OECD, ensuring that AI systems operate reliably and securely under a wide range of conditions, including handling errors, adversarial inputs, and unexpected scenarios. This principle directly supports the goal of maintaining system integrity and preventing harm, which is fundamental to trustworthiness.

Answer

Profitability

Answer

Scalability

Question 15

Which THREE of the following are key components of an AI governance framework?

Accepted Answer

Risk assessment and mitigation plans. Risk assessment and mitigation plans are a core component of an AI governance framework, ensuring that potential harms, biases, and security vulnerabilities are identified and addressed before deployment. This aligns with frameworks like NIST AI RMF, which mandates continuous risk monitoring and mitigation strategies to maintain ethical and secure AI operations.

Answer

Cloud infrastructure configuration

Answer

Model accuracy benchmarks