Free IBM A1000-125 - Assessment: AI EngineerPractice Test

An AI engineer needs to classify customer support tickets into predefined categories such as billing, technical support, and account management. Which type of machine learning approach is most appropriate for this task?

A data scientist observes that their model performs extremely well on training data (99% accuracy) but poorly on test data (65% accuracy). What problem is the model experiencing?

An organization wants to use IBM Watson Assistant to build a chatbot that can handle customer inquiries. Which Watson Assistant component is responsible for understanding what the user wants to accomplish?

A machine learning model needs to be retrained periodically as new data becomes available. Which deployment strategy best supports continuous model updates with minimal downtime?

What is the primary purpose of feature engineering in the machine learning pipeline?

A company is building a sentiment analysis solution using IBM Watson Natural Language Understanding. They need to analyze customer reviews to determine positive, negative, or neutral sentiment. Which NLU feature should they primarily use?

An AI engineer is developing a neural network for image classification and notices that training loss decreases but validation loss starts increasing after a certain number of epochs. What technique should be implemented to address this issue?

A development team needs to integrate IBM Watson Discovery into their application to enable users to search through a large collection of internal documents. What is the first step they should take?

During model development, an AI engineer needs to select an appropriate evaluation metric for a highly imbalanced dataset where only 2% of cases are positive. Why would accuracy be a poor choice as the primary metric?

An organization is deploying a machine learning model that will make predictions in real-time for a high-traffic web application. Which deployment consideration is most critical for this use case?

A data science team is implementing cross-validation for model evaluation. They have a time-series dataset with sales data from 2018-2024. Which cross-validation approach is most appropriate?

An AI engineer is using IBM Watson Studio to collaborate with team members on a machine learning project. Which feature enables version control and collaboration on notebooks and code?

A deployed machine learning model's performance has degraded significantly over the past six months. What phenomenon is most likely occurring, and what should be done?

An AI engineer needs to build a custom machine learning model using IBM Watson Studio. They want to write Python code with scikit-learn and train the model on cloud infrastructure. Which Watson Studio tool should they use?

In the context of neural networks, what is the primary purpose of using a validation dataset separate from both training and test datasets?

A company wants to use IBM Watson Assistant with Watson Discovery integration to build a chatbot that can answer questions by searching through company documentation. What is the primary benefit of this integration?

An AI engineer is implementing a recommendation system and must choose between collaborative filtering and content-based filtering. The system has limited user interaction data but rich item metadata. Which approach is most suitable?

A production machine learning model requires monitoring to detect performance degradation. Which metric should be continuously tracked to identify when the model needs retraining?

An organization is building a multi-class classification model with 10 classes. After training, they observe that the model performs well on 8 classes but poorly on 2 minority classes. What technique would most effectively improve performance on the underrepresented classes?

A data scientist is using IBM Watson Machine Learning to deploy a scikit-learn model. They need to ensure the model can handle different input data formats from various client applications. What should they implement in the deployment?