IBM A1000-125 - Assessment: AI Engineer Practice Exam 2025: Latest Questions

What is the primary difference between supervised and unsupervised learning in machine learning?

An AI engineer needs to extract entities like person names, locations, and organizations from unstructured text documents. Which IBM Watson service is most appropriate for this task?

During model training, a data scientist notices that the training accuracy is 98% but the validation accuracy is only 65%. What problem is the model experiencing?

A company wants to deploy a machine learning model that needs to handle variable traffic loads and scale automatically based on demand. Which deployment strategy is most appropriate?

An AI engineer is building a sentiment analysis model and needs to convert text into numerical representations that capture semantic meaning. Which technique would best preserve the contextual relationships between words?

A customer service application using Watson Assistant is experiencing inconsistent responses for similar user queries. What is the most effective way to improve response consistency?

An organization needs to monitor a deployed machine learning model for prediction drift and data quality issues in production. Which practice should be implemented?

What is the role of activation functions in neural networks?

A financial services company is developing a credit risk prediction model that must be explainable to regulatory authorities. The model needs to provide clear reasoning for each prediction. Which approach best balances accuracy and explainability?

An AI engineer is integrating multiple Watson services (Natural Language Understanding, Discovery, and Assistant) into a complex enterprise application. The application experiences intermittent failures when calling Watson APIs. What architectural pattern would best improve reliability and handle transient failures?

An AI engineer needs to extract structured information from unstructured customer feedback emails, including sentiment, keywords, and entity recognition. Which IBM Watson service combination would be most appropriate for this task?

During model training, an AI engineer observes that the training accuracy is 98% but the validation accuracy is only 72%. What is the most likely issue and recommended solution?

A financial services company is deploying a credit scoring model and needs to ensure transparency and explainability of model decisions for regulatory compliance. Which IBM tool should they implement?

An AI engineer is building a neural network for image classification and needs to choose an appropriate activation function for the hidden layers. The model is experiencing vanishing gradient problems. Which activation function would best address this issue?

A retail company wants to build a recommendation system using Watson Machine Learning. They have deployed their model but now need to implement A/B testing to compare the new model against the current production model. What is the best approach?

An AI engineer is preprocessing text data for a natural language processing task. The dataset contains words like 'running', 'runs', and 'ran' that should be treated as the same concept. Which text preprocessing technique should be applied?

A healthcare organization is building a chatbot using Watson Assistant to help patients schedule appointments. They need the assistant to extract specific information like date, time, and doctor specialty from user input. What Watson Assistant feature should they configure?

An AI engineer is training a deep learning model on Watson Studio and encounters an out-of-memory error. The model has 50 million parameters and uses batch size of 128. What would be the most effective immediate solution?

A company is deploying multiple machine learning models in production and needs to detect when model performance degrades due to changes in incoming data distribution over time. They want to automatically receive alerts when this occurs. Which Watson OpenScale feature should they configure?

An AI engineer is designing a solution for real-time fraud detection in credit card transactions. The system must process transactions within 100 milliseconds and make predictions on individual transactions as they occur. Which deployment architecture pattern is most appropriate?

An AI engineer is deploying a machine learning model to production and needs to ensure that the model can handle varying traffic loads while minimizing infrastructure costs. The model receives predictable traffic during business hours but minimal traffic at night. Which deployment strategy best addresses these requirements?

During model training, an AI engineer notices that the training loss continues to decrease while the validation loss starts to increase after epoch 15. What problem is occurring and what is the most appropriate solution?

A financial services company is building a credit risk assessment model using IBM Watson Studio. They need to ensure the model's decisions are explainable to regulators and can identify which features most influence predictions. Which IBM Watson capability should they prioritize implementing?

An AI engineer needs to preprocess a dataset containing customer reviews with inconsistent text formatting, missing values, and both numerical and categorical features before training a sentiment analysis model. What is the correct order of preprocessing steps?

What is the primary difference between supervised learning and unsupervised learning in machine learning?