AI0-001 Practice Test 4 — 10 Questions

Question 1

A data scientist needs to deploy a PyTorch model to production with low-latency inference. The model must be served as a REST API and should support GPU acceleration. Which combination of tools is MOST suitable for this task?

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

Accepted Answer

Docker container with a FastAPI application and Nvidia GPU support. It combines Docker containerization with FastAPI for a lightweight REST API and NVIDIA GPU support (via nvidia-docker or NVIDIA Container Toolkit) to enable low-latency GPU-accelerated inference. This stack directly meets the requirements of low-latency inference, REST API serving, and GPU acceleration without unnecessary overhead.

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ONNX runtime with a gRPC endpoint on a CPU-only node

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Apache Spark with MLlib to serve the model in batch mode

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Kubeflow Pipelines to deploy the model as a scheduled job

Question 2

A deep learning engineer is training a convolutional neural network for image classification. The model is overfitting the training data. Which three techniques can help reduce overfitting? (Choose three.)

Accepted Answer

Add dropout layers. Dropout layers randomly deactivate a fraction of neurons during training, which prevents co-adaptation and forces the network to learn more robust features. This reduces overfitting by acting as a form of ensemble learning without increasing model complexity.

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Use a smaller learning rate

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Increase the number of convolutional layers

Question 3

A data scientist is training a binary classification model to detect fraudulent transactions. The dataset contains 99.9% legitimate transactions and 0.1% fraudulent transactions. After training a logistic regression model, the accuracy is 99.9%, but the recall for the fraud class is 0%. Which of the following is the MOST likely cause?

Accepted Answer

The dataset is highly imbalanced, and the model predicts the majority class for all instances.. The dataset has a severe class imbalance (99.9% legitimate, 0.1% fraudulent). A logistic regression model that predicts the majority class (legitimate) for every instance will achieve 99.9% accuracy but 0% recall for the fraud class, because it never identifies any positive fraud cases. This is the classic 'accuracy paradox' in imbalanced classification.

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The regularization parameter is too large, causing underfitting.

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The model is overfitting due to too many features.

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The learning rate was too high.

Question 4

A data scientist is building a binary classification model to predict customer churn. The dataset has 10,000 samples with 80% non-churn and 20% churn. The model achieves 95% accuracy but fails to identify churners correctly. Which metric should the scientist focus on to evaluate model performance properly?

Accepted Answer

Recall (TPR). Recall (True Positive Rate) measures the proportion of actual churners correctly identified by the model. With 80% non-churn and 20% churn, a model can achieve 95% accuracy by simply predicting the majority class (non-churn) for all samples, resulting in zero true positives for churn. Recall directly exposes this failure by quantifying how many churners are captured, making it the critical metric for imbalanced classification problems.

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Precision

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F1-score

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Specificity

Question 5

A company deploys a machine learning model that makes predictions on streaming data. Over time, the data distribution shifts, causing model performance to degrade. Which monitoring strategy is most appropriate to detect this drift?

Accepted Answer

Track the model's accuracy on a fixed validation set over time. Tracking the model's accuracy on a fixed validation set over time directly measures performance degradation caused by data drift. As the streaming data distribution shifts, the model's predictions on the static validation set will become less accurate, providing a clear signal that drift has occurred. This is a standard monitoring approach in production ML systems for detecting concept drift.

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Compare the distribution of predictions to the training set

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Monitor the model's training loss

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Retrain the model daily on new data

Question 6

A team trained a deep neural network on a limited dataset. The training loss decreases consistently, but the validation loss starts increasing after 20 epochs. What is the most likely issue and the best corrective action?

Accepted Answer

Overfitting; apply regularization like dropout. The training loss decreasing while validation loss increasing after 20 epochs is the classic signature of overfitting: the model has memorized the training data but fails to generalize to unseen data. Applying regularization like dropout forces the network to learn more robust features by randomly dropping neurons during training, reducing overfitting. This is the most direct and effective corrective action for this specific symptom.

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Vanishing gradient; use ReLU activation

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Underfitting; increase model complexity

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Data leakage; reshuffle split

Question 7

Which open-source framework is commonly used for building, training, and deploying machine learning models and provides high-level APIs like Keras?

Accepted Answer

TensorFlow. TensorFlow is the correct answer because it is the open-source framework that provides high-level APIs like Keras for building, training, and deploying machine learning models. Keras, now integrated as tf.keras, offers a user-friendly interface for rapid prototyping while TensorFlow handles the underlying computation graph, distributed training, and model serving via TensorFlow Serving.

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Hugging Face Transformers

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scikit-learn

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PyTorch

Question 8

A data scientist is training a binary classification model to detect fraudulent transactions. The dataset is highly imbalanced with only 1% fraud cases. Which technique is most appropriate to address the class imbalance?

Accepted Answer

Oversample the minority class. Oversampling the minority class (e.g., using SMOTE or random oversampling) is the most appropriate technique because it balances the dataset by generating synthetic or duplicate examples of the fraud cases, allowing the model to learn the decision boundary for the minority class without discarding valuable majority-class data. This directly addresses the class imbalance where only 1% of transactions are fraudulent, improving recall and precision for fraud detection.

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Use a linear regression model

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Undersample the majority class

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Increase the learning rate

Question 9

A data engineering team is building a pipeline to ingest streaming user activity data, process it in real-time, and store features in a feature store for ML models. Which streaming technology is BEST suited for this real-time data ingestion and processing?

Accepted Answer

Apache Kafka. Apache Kafka is the best choice because it is a distributed streaming platform designed for high-throughput, fault-tolerant, real-time data ingestion and processing. It provides publish-subscribe messaging, durable log storage, and stream processing capabilities, making it ideal for ingesting streaming user activity data and feeding it into a feature store for ML models.

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Apache Spark SQL

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Apache Airflow

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Apache Hadoop MapReduce

Question 10

A data science team wants to implement a feature store to serve pre-computed features for both training and inference with low latency. Which TWO tools are commonly used for building a feature store?

Accepted Answer

Feast. Feast (Feature Store) is an open-source operational data system that manages and serves machine learning features to both training and inference pipelines with low latency. It provides a consistent feature definition API, offline serving for training, and online serving via a low-latency store like Redis or DynamoDB, making it a standard choice for feature store implementations.

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Kubeflow

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Apache Hive

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MLflow