AI0-001 Practice Exam 3 — 20 Questions

Question 1

A model trained on a dataset has high bias and low variance. What does this indicate?

Accepted Answer

Underfitting. High bias and low variance indicate that the model is too simple to capture the underlying patterns in the data, leading to systematic errors on both training and test sets. This is the classic signature of underfitting, where the model fails to learn the training data adequately.

Answer

Good fit

Answer

Data leakage

Answer

Overfitting

Question 2

A machine learning team is splitting a dataset for a binary classification problem. They want to ensure robust evaluation and avoid data leakage. Which TWO practices should they follow? (Choose 2)

Accepted Answer

Split into training, validation, and test sets. Option B is correct because splitting the dataset into separate training, validation, and test sets allows the model to be tuned on the validation set while the test set remains untouched for an unbiased final evaluation, which is essential for robust assessment of a binary classifier. Option D is correct because shuffling the data before splitting ensures that the training, validation, and test sets are representative of the overall distribution and prevents ordering bias or temporal artifacts from skewing the split, which helps avoid leakage when the data is not inherently ordered. Option A is not correct because normalising the entire dataset before splitting leaks information from the validation and test sets into the training process, since the scaling parameters would be computed using all data. Option C is not correct because including validation data in the training set removes the independent validation signal needed for hyperparameter tuning and model selection, undermining robust evaluation. Option E is not correct because reusing the same split for all experiments can lead to overfitting to that particular split and does not provide the variability needed to assess generalization reliably.

Answer

Normalise the entire dataset before splitting

Answer

Include validation data in the training set for more data

Answer

Use the same split for all experiments

Question 3

A model trained on customer reviews achieves 98% accuracy on the test set. However, when deployed, it performs poorly on real-world data. The data scientist suspects distribution shift. Which action is MOST important to address this?

Accepted Answer

Implement a monitoring system to detect data drift and retrain with fresh data. Distribution shift (data drift) causes the model's training distribution to differ from the real-world distribution, degrading performance despite high test accuracy. Implementing a monitoring system to detect drift and retraining with fresh data directly addresses this by ensuring the model adapts to the current data distribution, which is the most critical action for maintaining performance in production.

Answer

Reduce the learning rate during training

Answer

Add more features to the model

Answer

Increase the number of cross-validation folds

Question 4

A data scientist needs to store large volumes of unstructured log data for future AI model training. They also need to run SQL-based analytics on the data. Which THREE services are appropriate for this requirement? (Choose 3)

Accepted Answer

Snowflake. Snowflake is correct because it is a cloud-native data warehouse that supports both structured and semi-structured data (like JSON, Avro, Parquet) via its VARIANT data type, enabling SQL-based analytics on unstructured log data. It also integrates with cloud storage (e.g., Amazon S3) for storing large volumes of raw logs, making it suitable for AI model training pipelines.

Answer

Pinecone

Answer

pgvector

Question 5

An ML engineering team has a retraining pipeline that triggers automatically when model accuracy drops below a threshold. Recently, the model's accuracy has been fluctuating, causing frequent retraining and high compute costs. The team suspects the data distribution is changing slowly. Which approach should the team implement to reduce unnecessary retraining while maintaining model performance?

Accepted Answer

Implement a statistical drift detection method on input features. Implementing a statistical drift detection method (e.g., using KL divergence, PSI, or ADWIN) on input features allows the team to identify when the data distribution has genuinely changed, rather than reacting to random accuracy fluctuations. This reduces unnecessary retraining by triggering the pipeline only when statistically significant drift is detected, maintaining model performance without the high compute costs of frequent retraining.

Answer

Use a simpler model to reduce variability

Answer

Increase the frequency of model retraining

Answer

Reduce the batch size for inference

Question 6

Which THREE techniques can help reduce overfitting in neural networks?

Accepted Answer

Increasing training data size. Increasing the training data size helps reduce overfitting by providing the model with more examples to learn from, which reduces the variance and improves generalization. With more data, the model is less likely to memorize noise and instead learns the underlying patterns, making it more robust on unseen data.

Answer

Using a larger learning rate

Answer

Increasing number of layers

Question 7

A team is deploying a generative AI model for a real-time customer-facing application. They need to balance cost and latency. Which deployment strategy is MOST suitable?

Accepted Answer

AI microservices with streaming responses and async processing queues. AI microservices with streaming responses and async processing queues decouple inference from the request lifecycle, allowing the system to handle variable loads efficiently while maintaining low latency for real-time interactions. This architecture balances cost by scaling only the necessary components (e.g., GPU-backed inference services) and uses streaming (e.g., Server-Sent Events or WebSockets) to deliver partial results, reducing perceived latency for the customer.

Answer

Monolithic API with serverless functions

Answer

Edge deployment on user devices

Answer

Batch processing with synchronous requests

Question 8

An organization has a dataset with categorical features having high cardinality (e.g., ZIP codes). They plan to use a tree-based model. Which encoding method is most appropriate?

Accepted Answer

Target encoding (mean encoding). Target encoding (mean encoding) replaces each category with the mean of the target variable for that category, which works well with tree-based models on high-cardinality features because it captures the predictive signal without exploding the feature space. This method avoids the dimensionality explosion of one-hot encoding and the arbitrary ordering of label encoding, making it the most appropriate choice for high-cardinality categorical features in tree-based models.

Answer

Label encoding

Answer

One-hot encoding

Answer

Frequency encoding

Question 9

A company is deploying a generative AI system that produces text content. To comply with emerging transparency obligations, which THREE measures should they implement?

Accepted Answer

Watermark AI-generated content. Watermarking AI-generated content (Option A) is correct because it embeds an imperceptible, machine-detectable signal into the output, enabling provenance verification. This directly addresses transparency obligations by allowing downstream systems to identify synthetic text, which is a key requirement in emerging AI regulations such as the EU AI Act.

Answer

Encrypt all training data

Answer

Limit model access to internal employees only

Question 10

An MLOps team uses a CI/CD pipeline to automate model retraining. The pipeline triggers on new labeled data, runs feature engineering, retrains the model, evaluates against a holdout set, and deploys if metrics exceed thresholds. Recently, a retrained model passed validation but caused a 5% accuracy drop in production. Which improvement best prevents this?

Accepted Answer

Implement canary deployment with shadow scoring to compare with current model. Canary deployment with shadow scoring allows the new model to serve predictions to a small subset of traffic while comparing its outputs against the current production model in real time, without affecting all users. This catches subtle data drift or concept drift that a static holdout set may miss, preventing the 5% accuracy drop from reaching full production.

Answer

Require manual approval before deployment

Answer

Use the entire production dataset for validation instead of a holdout set

Answer

Increase the amount of training data used in each retraining cycle

Question 11

A healthcare organization uses an AI model to predict patient readmission risk. To comply with patient privacy regulations, they apply differential privacy during training. What is the primary trade-off of using differential privacy?

Accepted Answer

Reduced model accuracy for increased privacy. Differential privacy works by adding calibrated noise to the training process or model outputs, which directly reduces the model's accuracy in exchange for a quantifiable privacy guarantee (e.g., ε-differential privacy). This trade-off is fundamental: stronger privacy (lower ε) requires more noise, which degrades predictive performance. The healthcare organization must balance the need to protect patient data against the clinical utility of accurate readmission predictions.

Answer

Increased training time for reduced bias

Answer

Lower interpretability for higher fairness

Answer

Faster inference for lower memory usage

Question 12

An e-commerce company deploys a recommendation system using collaborative filtering. After launch, the system shows high accuracy for popular items but fails to recommend niche products to users who would likely buy them. Which technique should the team implement to improve recommendations for long-tail items?

Accepted Answer

Switch to a hybrid filtering approach that incorporates item metadata. Collaborative filtering relies on user-item interactions, which are sparse for niche products (the long tail). A hybrid filtering approach that incorporates item metadata (e.g., category, description, attributes) can bridge the gap by using content-based signals to recommend niche items even when interaction data is limited. This directly addresses the cold-start and sparsity problems for long-tail items.

Answer

Apply matrix factorization with higher latent factors

Answer

Increase the weight of popular items in the recommendation score

Answer

Collect more user interaction data over time

Question 13

A hospital uses an AI system to prioritize patient triage based on vital signs and medical history. During a trial, the system consistently assigns lower urgency to elderly patients with chronic conditions, even when their symptoms suggest high risk. Which approach best addresses this bias?

Accepted Answer

Audit the training data for representation of elderly patients and retrain with balanced data. The bias originates from the training data underrepresenting elderly patients with chronic conditions, causing the model to learn skewed urgency patterns. Auditing the data for representation and retraining with balanced data directly addresses the root cause by ensuring the model learns from a fair distribution of cases, which is a standard bias mitigation technique in AI systems.

Answer

Use a different dataset from a similar hospital without checking demographics

Answer

Manually increase the weight of age-related features in the model

Answer

Replace the neural network with a decision tree to simplify decision logic

Question 14

A company implements an AI-based chatbot for customer service. After deployment, customers report that the chatbot sometimes uses offensive language. The development team reviews the training data and finds no explicit offensive content. What is the most likely explanation?

Accepted Answer

The model learned biased language patterns from the training corpus. The chatbot's offensive language likely stems from biased or toxic patterns present in the training corpus, even if no explicit offensive content was flagged. Large language models learn statistical associations from their training data, and if the corpus contains subtle biases, stereotypes, or indirect toxic language, the model can reproduce these patterns in its responses. This is a well-known issue in AI ethics and governance, where models inadvertently amplify societal biases embedded in the data.

Answer

There is a bug in the deployment pipeline

Answer

The model is overfitting to rare examples

Answer

The training data was poisoned by an attacker

Question 15

Which THREE factors are common causes of bias in AI systems?

Accepted Answer

Lack of diversity in the development team. A lack of diversity in the development team leads to homogeneity of thought, which can cause blind spots in identifying potential biases in data, features, or model behavior. When the team does not represent the full spectrum of end users, the AI system may inadvertently encode assumptions that disadvantage underrepresented groups, resulting in biased outcomes.

Answer

Cross-validation

Answer

High regularization

Question 16

Which technique adds controlled noise to query results or training data to prevent an attacker from inferring whether a specific individual's data was included in the dataset?

Accepted Answer

Differential privacy. Differential privacy is the technique that adds controlled statistical noise (e.g., Laplace or Gaussian noise) to query results or training data so that the presence or absence of any single individual's data cannot be inferred. It provides a mathematical guarantee (epsilon-differential privacy) that the output distribution is nearly identical whether or not a specific record is included. This directly matches the question's requirement.

Answer

Anonymisation

Answer

Federated learning

Answer

Pseudonymisation

Question 17

A team is training a deep learning model for natural language processing using a large corpus. They notice the model has a very high number of parameters and training is slow. Which technique can reduce the number of parameters without significant performance loss?

Accepted Answer

Apply embedding compression. Embedding compression reduces the dimensionality of the embedding layer, which often contains the majority of the model's parameters in NLP tasks. By using techniques like low-rank factorization or pruning, the model retains most of its representational power while significantly decreasing the parameter count and training time.

Answer

Add more dropout layers

Answer

Use a larger batch size

Answer

Increase learning rate

Question 18

A research team is training a deep neural network for image classification. The training loss decreases rapidly for the first few epochs but then plateaus, while validation loss starts to increase after epoch 10. Which action would best address this issue?

Accepted Answer

Implement early stopping based on validation loss to prevent further overfitting.. The training loss decreasing rapidly then plateauing while validation loss increases after epoch 10 is a classic sign of overfitting. Early stopping monitors validation loss and halts training when it begins to rise, preventing the model from memorizing noise in the training data. This directly addresses the overfitting issue without requiring architectural or hyperparameter changes that could destabilize training.

Answer

Reduce the batch size to introduce more noise during training.

Answer

Increase the learning rate to help the model escape the plateau.

Answer

Add more convolutional layers to increase model capacity.

Question 19

A data scientist is training a model to detect fraudulent transactions. To protect customer privacy, the team wants to ensure that the model does not inadvertently memorize and reveal sensitive information about individuals in the training set. Which technique should be applied during training?

Accepted Answer

Differential privacy. Differential privacy is the correct technique because it adds calibrated noise to the training process or output, ensuring that the model cannot infer whether any specific individual's data was included in the training set. This directly addresses the goal of preventing memorization and leakage of sensitive information while still allowing the model to learn useful patterns for fraud detection.

Answer

Federated learning

Answer

Homomorphic encryption

Answer

Model quantization

Question 20

A data scientist needs to predict whether a customer will churn (yes/no) based on historical data. Which type of machine learning problem is this?

Accepted Answer

Binary classification. This is a binary classification problem because the target variable has exactly two discrete outcomes: 'yes' (churn) or 'no' (no churn). Classification algorithms such as logistic regression, decision trees, or support vector machines are used to assign input features to one of these two predefined classes. The output is a categorical label, not a continuous value or a reward signal.

Answer

Reinforcement learning

Answer

Regression

Answer

Clustering