IBM A1000-103 Practice Exam: Test Your Knowledge 2025

What is the primary difference between supervised and unsupervised learning?

A retail company wants to analyze customer reviews to determine sentiment (positive, negative, or neutral). Which type of machine learning problem is this?

Which IBM Watson service would be most appropriate for converting spoken audio from customer service calls into text for analysis?

What is overfitting in machine learning?

In the context of neural networks, what is the purpose of an activation function?

A financial services company needs to extract entities such as account numbers, dates, and monetary amounts from unstructured documents. Which IBM Watson service is best suited for this task?

During model training, you notice that both training and validation loss are high and not decreasing significantly. What problem does this indicate, and what is the most appropriate solution?

What is the primary purpose of using a validation dataset during model development?

A healthcare organization wants to build a conversational interface that can answer patient questions about symptoms, schedule appointments, and provide medication reminders. Which IBM Watson service provides the core functionality for this use case?

What is the purpose of using cross-validation in machine learning?

When deploying a machine learning model to production, what is the primary purpose of implementing model monitoring?

In a neural network, what does the term 'epoch' refer to?

A company is experiencing latency issues when making real-time predictions with their deployed machine learning model. Which approach would most effectively reduce inference time?

What is transfer learning in the context of deep learning?

A data scientist notices that their classification model has high precision but low recall. What does this indicate about the model's performance?

An enterprise needs to analyze thousands of internal documents to extract insights, answer questions, and identify trends. The solution must handle complex queries and understand context across multiple documents. Which IBM Watson service is most appropriate?

In a production ML system, what is the primary benefit of implementing A/B testing for model deployment?

A team is building a custom entity extraction model for industry-specific terminology not covered by pre-trained models. They need to train Watson Natural Language Understanding with their own annotated data. Which IBM Watson service should they use to create and manage these custom annotations and models?

A deep learning model for image classification is experiencing vanishing gradients during training, causing very slow learning in early layers. Which combination of techniques would most effectively address this issue?

An organization has deployed multiple AI models in production and needs to ensure fairness, detect bias, and maintain explainability across all models. They require continuous monitoring of model predictions and the ability to track fairness metrics over time. Which IBM solution provides comprehensive AI governance and monitoring capabilities?