AI-900 Practice Test 7 — 10 Questions

Question 1

A company wants to use Azure OpenAI to generate product descriptions. They have a few example descriptions that perfectly match their desired style and structure. They want the model to produce new descriptions in the same style without retraining the underlying model. Which approach should they use?

Accepted Answer

Few-shot prompting with the examples in the prompt. Few-shot prompting provides the model with a small number of example inputs and outputs directly in the prompt, allowing it to infer the desired style and structure without any training. This approach is ideal when you have a few high-quality examples and want to generate new content that matches them, without the cost and complexity of fine-tuning.

Answer

Fine-tune the model on the example descriptions

Answer

Embeddings and similarity search

Answer

Content filtering configurations

Question 2

A customer support team receives thousands of emails daily. They want to automatically route each email to the appropriate department (Billing, Technical Support, or General Inquiry). They also want to extract the customer's account number and order ID from each email. Which combination of Azure AI Language features should they use?

Accepted Answer

Text classification and custom entity recognition. Option C is correct because the scenario requires two distinct NLP tasks: categorizing emails into predefined departments (Billing, Technical Support, General Inquiry) which is a text classification task, and extracting specific structured data (account number and order ID) which requires custom entity recognition to identify domain-specific entities not covered by prebuilt NER. Azure AI Language provides both custom text classification and custom entity recognition features to handle these requirements.

Answer

Sentiment analysis and key phrase extraction

Answer

Language detection and translation

Answer

Named entity recognition (NER) and summarization

Question 3

A data science team trains a regression model to predict house prices. They evaluate the model using Mean Absolute Error (MAE). After deployment, they notice that the model occasionally produces large errors (e.g., underpredicting a luxury home by $500,000) while most predictions are within $20,000. The business is more concerned about the impact of these large errors than the average small error. Which additional metric should the team use to better capture the penalty for large errors?

Accepted Answer

Root Mean Squared Error (RMSE). Root Mean Squared Error (RMSE) is the correct additional metric because it squares the residuals before averaging, which heavily penalizes large errors like the $500,000 underprediction. Unlike MAE, which treats all errors equally, RMSE amplifies the impact of outliers, making it a better fit for a business that cares more about catastrophic failures than typical small errors. This aligns with the need to capture the penalty for large deviations in regression model evaluation.

Answer

R-squared

Answer

Mean Absolute Percentage Error (MAPE)

Answer

F1 score

Question 4

A bike-sharing company wants to predict the number of rentals per hour. Their model's predictions are usually close but occasionally have large errors due to unexpected events like sudden rain. They want a metric that heavily penalizes these large errors to ensure the model is not overly confident. Which evaluation metric should they primarily use?

Accepted Answer

Mean Squared Error (MSE). Mean Squared Error (MSE) is the correct choice because it squares the residuals, which heavily penalizes large errors. Since the bike-sharing company wants to discourage occasional large prediction errors (e.g., due to sudden rain), MSE’s quadratic penalty ensures that models with even a few large outliers receive a much worse score, forcing the model to avoid overconfidence.

Answer

Mean Absolute Error (MAE)

Answer

Classification Accuracy

Answer

R-squared

Question 5

A company wants to build a chatbot that can answer customer questions about their product return policy, shipping times, and warranty information. They have a structured document with these questions and answers. Which Azure AI Language feature should they use to create this chatbot without writing custom code?

Accepted Answer

Custom Question Answering. Custom Question Answering (C) is the correct choice because it is specifically designed to create a chatbot that answers questions based on a structured document (e.g., FAQ, product manual) without writing custom code. It uses a predefined knowledge base of question-answer pairs and provides a built-in orchestration for bot integration, making it ideal for this scenario.

Answer

Conversational Language Understanding (CLU)

Answer

Custom Text Classification

Answer

Language Detection

Question 6

A city deploys an AI-powered kiosk to help residents access government services. The kiosk uses a voice interface only, without any text or screen reader support. Which Microsoft responsible AI principle is most directly being ignored?

Accepted Answer

C. The kiosk uses only a voice interface without text or screen reader support, which directly violates the Microsoft responsible AI principle of Inclusiveness. Inclusiveness requires that AI systems are designed to empower everyone, including people with disabilities such as hearing impairments or those who rely on visual or text-based interfaces. By excluding non-verbal interaction methods, the system fails to accommodate diverse user needs, making it inaccessible.

Answer

A

Answer

B

Answer

D

Question 7

A company deploys an AI-powered voice assistant that only supports English. The assistant is used in a country where the official languages are English, French, and Dutch. Many users who speak French or Dutch cannot use the assistant effectively. Which Microsoft responsible AI principle is most directly relevant to this situation?

Accepted Answer

Inclusiveness. The assistant's inability to support French and Dutch users directly violates the inclusiveness principle, which requires AI systems to be designed for all users regardless of language, ability, or background. By supporting only English in a multilingual country, the system excludes a significant portion of the target audience, failing to provide equitable access.

Answer

Fairness

Answer

Reliability and safety

Answer

Transparency

Question 8

A company develops an AI system to screen job candidates based on their resumes. The system is trained on historical data. Analysis reveals that the model has an adverse impact against female candidates due to a proxy feature (e.g., 'years of continuous employment') that correlates with gender. The team removes the protected attribute 'gender' from the training data but the biased outcome persists. According to Microsoft's responsible AI principles, which additional step should the team take to address this unfairness?

Accepted Answer

Use a tool like Fairlearn to detect and mitigate the bias while maintaining model performance.. Option B is correct because Microsoft's responsible AI principle of fairness requires not just removing protected attributes but also detecting and mitigating proxy features that cause bias. Fairlearn is a Microsoft open-source toolkit specifically designed to assess and mitigate unfairness in AI systems, offering algorithms like 'Exponentiated Gradient Reduction' or 'Grid Search' to reduce disparity while preserving model performance. Simply removing the proxy feature (A) may not always be feasible if it carries predictive value, and Fairlearn provides a systematic way to balance fairness and accuracy.

Answer

Remove the offending proxy feature 'years of continuous employment' from the training data.

Answer

Train a separate model for each gender group to ensure equal outcomes.

Answer

Collect more training data from underrepresented groups.

Question 9

A business analyst wants to quickly summarize the main topics discussed in a large collection of customer feedback emails. The analyst needs to identify recurring concepts such as 'product quality', 'shipping delay', and 'customer service'. They want to use a prebuilt Azure AI Language feature without any custom training. Which feature should they use?

Accepted Answer

Key Phrase Extraction. Key Phrase Extraction is the correct choice because it is a prebuilt Azure AI Language feature designed to automatically identify and return the main topics, concepts, and recurring themes from unstructured text, such as 'product quality' or 'shipping delay'. Unlike custom-trained models, this feature requires no training data and works out-of-the-box, making it ideal for quickly summarizing large collections of customer feedback emails.

Answer

Named Entity Recognition (NER)

Answer

Language Detection

Answer

Sentiment Analysis

Question 10

A company needs to automatically extract text from scanned invoices that contain both printed text and handwritten notes. Which Azure AI service is specifically designed to handle this type of document?

Accepted Answer

Azure AI Document Intelligence (formerly Form Recognizer). Azure AI Document Intelligence (formerly Form Recognizer) is specifically designed to extract text, key-value pairs, and tables from scanned documents, including invoices with both printed text and handwritten notes. It uses optical character recognition (OCR) combined with deep learning models to handle mixed content, making it the correct choice for this scenario.

Answer

Azure Face API

Answer

Azure Custom Vision

Answer

Azure Video Indexer