AI0-001 Practice Test 50 — 15 Questions

Question 1

Which THREE of the following are key considerations when deploying an AI model in a production environment?

Accepted Answer

Monitoring model performance for data drift. Data drift refers to the change in the statistical properties of the input data over time, which can degrade model accuracy. Continuous monitoring for data drift is essential in production to detect when the model's assumptions about the data distribution are no longer valid, triggering retraining or alerts.

Answer

Basing acceptance solely on training accuracy

Answer

Maximizing model complexity to achieve the best accuracy

Question 2

An engineer is building a regression model to predict housing prices. The dataset includes features such as square footage, number of bedrooms, and year built. The engineer notices that the square footage values range from 500 to 10,000, while the number of bedrooms ranges from 1 to 5. Which preprocessing step is most critical before training a gradient descent-based model?

Accepted Answer

Normalize or standardize the features. Gradient descent-based models are sensitive to the scale of input features because they update weights proportionally to the gradient, which is influenced by feature magnitudes. With square footage ranging 500–10,000 and bedrooms 1–5, the larger feature will dominate the gradient, causing slow or unstable convergence. Normalizing or standardizing (e.g., Z-score or min-max scaling) ensures all features contribute equally, leading to faster and more reliable training.

Answer

Use k-fold cross-validation

Answer

Apply log transformation to all features

Answer

One-hot encode the features

Question 3

During model monitoring, a loan approval model shows disparate impact against a protected group. The model's overall accuracy is high, but the false positive rate for the protected group is 0.12 compared to 0.02 for other groups. Which action should the operations team take first?

Accepted Answer

Retrain the model with reweighted training data to minimize disparity. Retraining the model with reweighted training data directly addresses the root cause of disparate impact—biased historical data—by assigning higher weights to underrepresented groups during training. This technique, often implemented via cost-sensitive learning or sample reweighting, adjusts the model's internal decision boundaries to reduce false positive rate disparities without sacrificing overall accuracy. The operations team should first attempt to mitigate bias at the data level before considering threshold adjustments or model replacement, as reweighting preserves the model's learned patterns while promoting fairness.

Answer

Document the disparity and proceed with deployment because accuracy is high

Answer

Replace the model with a simpler model that is less discriminatory

Answer

Adjust the decision threshold for the protected group to equalize false positive rates

Question 4

A financial institution is deploying a reinforcement learning agent to optimize stock trading decisions. The agent is trained in a simulated environment that mimics historical market data. After deployment, the agent performs well initially but then suffers large losses during a period of high volatility that was underrepresented in the training data. The team wants to make the agent more robust to such market conditions without retraining from scratch. They have a budget for additional simulation compute and access to a broader historical dataset including past crises. The agent uses a deep Q-network (DQN) architecture. Which strategy should they adopt?

Accepted Answer

Modify the DQN to use a recurrent neural network (e.g., DRQN) and train on the expanded dataset. A Deep Recurrent Q-Network (DRQN) can capture temporal dependencies in market data, which is crucial for handling volatile periods that were underrepresented in training. By training on the expanded dataset that includes past crises, the agent can learn from sequential patterns of volatility, making it more robust without requiring a complete retraining from scratch. This approach leverages the existing DQN architecture while adding recurrent layers to better model the dynamic market conditions.

Answer

Increase the replay buffer size and continue training on the original dataset

Answer

Keep the DQN but perform extensive hyperparameter tuning on the original data

Answer

Switch to a policy gradient method with a random exploration strategy

Question 5

A company deployed a chatbot using a pre-trained language model. Users report that the chatbot provides incorrect answers to domain-specific questions. Which approach should the AI team prioritize to improve accuracy without retraining the entire model?

Accepted Answer

Fine-tune the model on a curated dataset of domain-specific conversations.. Fine-tuning on a curated domain-specific dataset is the most efficient way to improve accuracy for specialized queries without retraining the entire model. It adjusts the model's weights using a smaller, targeted dataset, preserving general language understanding while adapting to domain terminology and context.

Answer

Increase the temperature parameter to reduce randomness.

Answer

Collect more general training data and retrain the model from scratch.

Answer

Roll back to a previous version of the model that was more accurate.

Question 6

A logistics company uses a machine learning model to predict delivery times based on historical data. The model was performing well, but recently it started making inaccurate predictions, especially for routes that have experienced new traffic patterns and road closures. The data engineering team receives an alert that the model's accuracy has dropped by 15% over the last week. They suspect data drift. The team has access to the original training data and a continuous stream of new data. What is the most appropriate first step for the team to take?

Accepted Answer

Compare the distributions of key features between the training data and the recent data to quantify data drift.. The first step in diagnosing a suspected data drift is to statistically compare the distributions of key features between the training data and the recent streaming data. This quantifies whether the input data distribution has changed, which directly explains the accuracy drop. Without this analysis, any corrective action (like retraining or rollback) would be premature and could mask the root cause.

Answer

Roll back the model to the previous stable version and schedule a full audit of the data pipeline.

Answer

Immediately retrain the model using the most recent data to adapt to the new patterns.

Answer

Add more features to the model to capture the new traffic patterns and road closures.

Question 7

A company wants to build an AI pipeline that processes streaming data from IoT sensors, performs feature engineering, trains a model incrementally, and deploys the updated model. Which data pipeline technology is BEST suited for the streaming ingestion step?

Accepted Answer

Apache Kafka. Apache Kafka is the best choice for the streaming ingestion step because it is a distributed event streaming platform designed for high-throughput, fault-tolerant ingestion of real-time data streams. It acts as a durable message broker that can ingest IoT sensor data in real time and make it available for downstream processing, which aligns perfectly with the requirement for streaming data ingestion.

Answer

Amazon S3

Answer

Apache Spark

Answer

Apache Airflow

Question 8

A security engineer is threat modeling an AI-based recommendation system using STRIDE. Which threat corresponds to an attacker extracting the model's training data by querying the system?

Accepted Answer

Information disclosure. In the STRIDE threat model, Information Disclosure occurs when an attacker gains unauthorized access to sensitive data. Extracting training data by querying the AI recommendation system (e.g., via a model inversion or membership inference attack) directly violates the confidentiality of the training dataset, which is a classic Information Disclosure threat.

Answer

Spoofing

Answer

Denial of service

Answer

Tampering

Question 9

A data engineering team is designing a pipeline to train a model on streaming data. The data arrives in a time-series format. Which approach should they use to ensure the model reflects current trends without catastrophic forgetting?

Accepted Answer

Implement incremental learning with periodic validation. Incremental learning (also called online learning) allows the model to update its parameters continuously as new streaming data arrives, without requiring access to historical data. By coupling this with periodic validation on a held-out set, the team can detect concept drift and ensure the model adapts to current trends while avoiding catastrophic forgetting, which occurs when new updates overwrite previously learned patterns.

Answer

Use a sliding window of the most recent data for training

Answer

Deploy an ensemble of models trained on different time periods

Answer

Retrain the entire model from scratch every week

Question 10

A machine learning model for credit card fraud detection is deployed. The model's precision is 0.95 and recall is 0.60. The business cost of missing a fraud is very high. Which of the following should the team prioritize to reduce the number of false negatives?

Accepted Answer

Decrease the classification threshold.. Decreasing the classification threshold makes the model more sensitive, classifying more transactions as fraudulent. This increases recall (reducing false negatives) at the cost of precision. Given the high cost of missing fraud, lowering the threshold is the direct way to capture more true positives, even if it increases false positives.

Answer

Use a different model algorithm.

Answer

Add more features.

Answer

Increase the classification threshold.

Question 11

A company has a TensorFlow model trained on-premises and wants to deploy it on AWS SageMaker for scalable inference. What is the BEST way to package the model for deployment?

Accepted Answer

Package the model in a Docker container with a TensorFlow serving script and push to Amazon ECR. SageMaker expects models in a container format; the inference container should include the model artifacts and the serving code, allowing SageMaker to host it on scalable endpoints.

Answer

Convert the model to ONNX and upload to SageMaker

Answer

Upload the .h5 file to S3 and create a SageMaker endpoint directly

Answer

Use SageMaker Studio to train the model again from scratch

Question 12

A data scientist needs to explain a single prediction from a complex ensemble model to a business stakeholder. Which technique generates local, interpretable explanations by perturbing input features and fitting a simple surrogate model?

Accepted Answer

LIME. LIME (Local Interpretable Model-agnostic Explanations) explains individual predictions by perturbing inputs and learning a linear surrogate. SHAP provides Shapley values, which are also local but game-theoretic. Attention visualisation is for transformer models. Model cards describe global model behaviour.

Answer

Model card

Answer

SHAP

Answer

Attention visualisation

Question 13

A company uses an AI model to predict equipment failures. The model outputs a probability of failure. To minimize false alarms, the operations team wants a high precision. Which deployment strategy should they implement?

Accepted Answer

Increase the decision threshold for positive classification. To minimize false alarms and achieve high precision, the operations team should increase the decision threshold for positive classification. A higher threshold means the model only predicts a failure when it is very confident, reducing the number of false positives (false alarms) at the cost of potentially missing some true failures (lower recall). This directly controls the precision-recall trade-off without changing the underlying model.

Answer

Retrain the model on more recent data

Answer

Decrease the decision threshold

Answer

Use an ensemble of models with voting

Question 14

A machine learning engineer is designing a pipeline to train a computer vision model using PyTorch on a large dataset stored in an S3 data lake. They need to preprocess images (resize, normalize) and stream them efficiently to GPUs. Which THREE components are essential in this pipeline? (Select THREE.)

Accepted Answer

GPU-accelerated training with CUDA. GPU-accelerated training with CUDA is essential for efficiently training computer vision models on large datasets. PyTorch leverages CUDA to parallelize tensor operations and model computations on NVIDIA GPUs, which is critical for reducing training time from days to hours when processing high-resolution images.

Answer

CPU-only inference pipeline

Answer

Apache Airflow to orchestrate the training job

Question 15

A data science team is building a model to predict customer churn. The dataset includes categorical variables like 'region' and 'subscription_type'. Which three preprocessing steps should be applied to these categorical features? (Select THREE).

Accepted Answer

Label encoding. Label encoding (B) is correct because it converts each unique category in a categorical variable into a unique integer, which is a simple and memory-efficient way to prepare categorical data for machine learning models. Ordinal encoding (D) is correct for categorical variables with a natural order, such as 'subscription_type' if tiers exist (e.g., basic, premium, enterprise), preserving ordinal relationships. One-hot encoding (E) is correct for nominal categorical variables like 'region' where no order exists, creating binary columns for each category to avoid implying false ordinality.

Answer

Normalization

Answer

Standard scaling