IBM A1000-103 Practice Exam 2025: Latest Questions

A team is evaluating whether a problem is best solved with supervised learning. Which scenario is the BEST fit for supervised learning?

A business user wants to quickly build a model in IBM watsonx.ai without writing code and compare algorithms. Which approach best meets this need?

A dataset contains customer age, account tenure, and monthly spend. The model underperforms because age (0–100) dominates tenure (0–10) during optimization. What is the MOST appropriate preprocessing step?

A deployed model’s predictions start degrading after a new marketing campaign changes customer behavior. Which concept BEST describes this situation?

A bank wants to automate document processing for mortgage applications. They need to extract key fields from forms and convert scanned PDFs into machine-readable text before downstream analytics. Which IBM capability is MOST appropriate?

A data scientist trains a classification model and sees high training accuracy but low validation accuracy. Which action is the BEST next step to address the issue?

A team wants to ensure consistent, repeatable model training across environments and enable collaboration. Which practice BEST supports this goal?

A company needs a conversational solution that can answer FAQs and also call backend systems (e.g., check order status) when the user provides an order number. What is the BEST high-level design?

A healthcare organization is deploying an ML model and must meet governance requirements: explainability for decisions, traceability of training data, and the ability to detect and mitigate bias. Which approach BEST aligns with these requirements in an IBM AI lifecycle?

A model is trained on balanced data, but in production only 1% of events are truly positive (rare fraud). The deployed model generates too many false positives, overwhelming investigators. What is the MOST effective next step?

A project team is evaluating whether a classification model is biased against a protected group. They need metrics and visualizations to assess fairness and identify potential bias after training. Which IBM capability best fits this need?

A data scientist is creating a supervised learning model and realizes that some features were inadvertently derived using information collected after the prediction time (e.g., a 'resolved_date' field used to predict 'will_resolve'). What is the primary risk of this issue?

A team has a trained model running in production and wants to detect when the input data distribution changes significantly over time, potentially degrading performance. What is this problem commonly called?

A customer-support organization wants to build a conversational interface that can answer FAQs and also collect structured information (like account number and issue type) before handing off to a human agent. Which IBM service is the best foundation for this solution?

A model performs well on the training set but significantly worse on the validation set. The team suspects the model is too complex for the available data. Which action is a common best practice to address this?

A team is building a document search solution. Users ask natural-language questions and expect answers grounded in an internal corpus of PDFs and web pages. They want to ingest documents, index them, and tune relevance based on user feedback. Which IBM service is most appropriate?

A deployed model is being monitored and shows high accuracy overall, but monitoring reveals that a specific customer segment has significantly worse outcomes. The business wants to understand which features most influenced individual predictions for that segment. Which capability best supports this requirement?

A regulated enterprise needs to promote a model from development to production with consistent governance. They want to ensure the production model can be traced back to its training data, code, and evaluation results, and that approvals are documented. Which approach best supports this goal?

A team trains a binary classifier on a dataset where only 2% of examples are positive (rare event detection). Accuracy is very high, but the model rarely predicts the positive class. Which evaluation metric is generally more informative in this scenario?

An organization uses a pre-trained foundation model for text summarization and fine-tunes it using internal documents. After deployment, they must ensure that sensitive information from prompts or outputs is not logged or exposed to unauthorized teams. What is the most appropriate operational control?

A team is building a classifier where 95% of records are non-fraud and 5% are fraud. They initially report 96% accuracy, but fraud cases are still frequently missed. Which evaluation approach is MOST appropriate to validate model performance on the minority class?

A business wants to extract key fields (e.g., invoice number, total amount, vendor) from varied invoice formats (scanned PDFs and images) without building a custom ML pipeline from scratch. Which IBM Watson service is the BEST fit?

During model development, a data scientist notices the model performs very well on the training set but significantly worse on a held-out validation set. Which action is the BEST first step to reduce this issue?

An organization must deploy an AI model where all customer data and inference requests must remain inside its private network (no public internet exposure). They also need scalable, containerized deployment with monitoring. Which deployment approach best meets these requirements?

A team deploys a model and later learns that the input data distribution has changed (new product categories and customer behavior). Prediction quality degrades over time. Which operational capability is MOST important to detect and address this issue early?