AI-900 Practice Test 47 — 15 Questions

Question 1

What is 'guardrails' in generative AI applications and how are they implemented?

Accepted Answer

Safety and quality constraints (content filters, system prompts, output validation) preventing harmful AI outputs. Guardrails in generative AI applications are safety and quality constraints implemented to prevent harmful or inappropriate AI outputs. They include content filters that block offensive language, system prompts that steer model behavior, and output validation that checks responses against predefined policies. This is correct because guardrails are a core feature of responsible AI deployment, ensuring that generative models like GPT-4 in Azure OpenAI Service produce safe, compliant, and contextually appropriate content.

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Physical barriers around AI data centres to prevent unauthorised access

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Legal terms of service that constrain how developers can use Azure OpenAI

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Rate limits that prevent individual users from generating too many responses

Question 2

What is 'conversation history' and why is it important in multi-turn chatbot interactions?

Accepted Answer

The sequence of prior messages included in the prompt so the model can maintain context across turns. Conversation history is the sequence of prior messages included in the prompt to a language model, allowing it to maintain context across multiple turns in a dialogue. This is critical because the model itself has no inherent memory; without the history, each turn would be treated as an isolated query, breaking the flow of a multi-turn interaction. By appending previous user inputs and assistant responses to the prompt, the model can reference earlier statements and provide coherent, context-aware replies.

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A database log of all chatbot conversations for compliance auditing

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A summary of frequently asked questions generated from past user interactions

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The total number of messages a user has sent to a chatbot over their lifetime

Question 3

A data scientist has a dataset containing customer transaction records with features such as age, income, and purchase history, but no labels. The goal is to identify natural groupings of customers for a targeted marketing campaign. Which type of machine learning should be used?

Accepted Answer

Clustering. Clustering is the correct choice because the dataset has no labels, and the goal is to discover natural groupings of customers based on feature similarity. Unsupervised learning algorithms like K-Means or DBSCAN partition data into clusters where intra-cluster similarity is high and inter-cluster similarity is low, enabling targeted marketing without pre-existing categories.

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Classification

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Regression

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Reinforcement learning

Question 4

A company wants to build a chatbot that answers customer questions using only their internal knowledge base, which consists of several PDFs and Word documents. They do not want the chatbot to use any information from the model's pre-trained knowledge. Which Azure OpenAI feature should they use to achieve this?

Accepted Answer

Azure OpenAI on your data. Azure OpenAI on your data allows you to connect Azure OpenAI models to your own data sources (such as PDFs and Word documents) and restrict the model to generate responses solely from that data, without using the model's pre-trained knowledge. This is achieved by indexing the documents into an Azure Cognitive Search index and using retrieval-augmented generation (RAG) to ground the model's responses in your specific content.

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Content filtering

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Prompt flow

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Temperature parameter

Question 5

A company deploys an AI system to screen job resumes and rank candidates. The company wants to ensure that candidates can understand how the system arrived at its decisions. Which Microsoft responsible AI principle is most directly addressed by this requirement?

Accepted Answer

Transparency. The requirement that candidates can understand how the AI system arrived at its decisions directly aligns with the Transparency principle, which mandates that AI systems be interpretable and that their decision-making processes be explainable to users. In the context of resume screening, this means providing clear reasoning for why a candidate was ranked a certain way, such as highlighting which features (e.g., skills, experience) most influenced the score.

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Fairness

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Reliability and Safety

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Privacy and Security

Question 6

A data scientist trains a classification model to distinguish between images of cats and dogs. The model achieves 99% accuracy on the training set but only 75% accuracy on a validation set. Which concept best describes this situation?

Accepted Answer

Overfitting. The model performs exceptionally well on the training data (99% accuracy) but significantly worse on unseen validation data (75% accuracy). This gap indicates the model has memorized noise and specific patterns in the training set rather than learning generalizable features, which is the classic definition of overfitting.

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Underfitting

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Model bias

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Data leakage

Question 7

A beverage company uses a camera system to inspect bottles on a conveyor belt. The system must automatically identify which bottles are defective (e.g., cracked or chipped) and which are acceptable, based on the overall appearance of each bottle. The company has thousands of labeled images of bottles (defective and non-defective). Which Azure Computer Vision service should they use to train a custom model?

Accepted Answer

Custom Vision – Image classification. The scenario requires classifying each bottle image into one of two categories (defective or acceptable) based on overall appearance. Custom Vision – Image classification is designed exactly for this: it trains a model on labeled images to predict a single label per image, making it ideal for binary or multi-class classification tasks like defect detection.

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Custom Vision – Object detection

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Optical Character Recognition (OCR)

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Face API

Question 8

What is 'Azure Cognitive Services' and how does it relate to Azure AI Services?

Accepted Answer

Microsoft's family of pre-built AI APIs (now rebranded as Azure AI Services) for vision, speech, and language. Azure Cognitive Services is the original name for Microsoft's family of pre-built AI APIs that provide capabilities in vision, speech, language, and decision-making. These APIs have been rebranded as Azure AI Services, making option B correct because it accurately describes the service as pre-built AI APIs for vision, speech, and language, and correctly notes the rebranding.

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A set of tools for cognitive psychology research at Microsoft Research

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Services that simulate human cognitive functions like memory and problem-solving in robots

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A premium Azure support tier that provides AI specialists to help with complex deployments

Question 9

A retail company wants to automatically group customers into segments based on their purchasing history, age, and location without using any predefined labels. The goal is to identify distinct customer profiles for targeted marketing campaigns. Which type of machine learning approach should they use?

Accepted Answer

Unsupervised learning. Unsupervised learning is the correct approach because the company wants to group customers into segments without predefined labels. The algorithm will discover natural patterns and clusters in the data (purchasing history, age, location) on its own, which is the core characteristic of unsupervised learning.

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Supervised learning

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Reinforcement learning

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Regression

Question 10

What is the purpose of 'entities' in conversational language understanding (CLU) models?

Accepted Answer

To extract specific parameter values from user utterances needed to fulfill an intent. Entities in CLU models are designed to extract specific pieces of information (parameter values) from user utterances, such as dates, locations, or product names, which are necessary to fulfill the user's intent. For example, in the utterance 'Book a flight to Seattle on June 5th,' the intent is 'BookFlight,' and entities extract 'Seattle' (destination) and 'June 5th' (date). This directly supports the intent by providing the required parameters for downstream actions.

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To determine the overall topic or domain of the conversation

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To classify how confident the model is in its intent prediction

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To define the fallback response when no intent is recognized

Question 11

A developer wants to use Azure OpenAI to generate text that follows a specific style, such as formal business letters. They provide three examples of the desired output format in the prompt and then ask the model to generate a new letter. Which technique is the developer using?

Accepted Answer

Few-shot learning. The developer is using few-shot learning, a technique where a prompt includes several examples (in this case, three formal business letters) to guide the model's output style and format without updating the model's weights. This approach leverages the model's in-context learning ability to generalize from the provided examples, making it ideal for tasks requiring specific stylistic adherence.

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Zero-shot learning

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Fine-tuning

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Temperature scaling

Question 12

What is 'AI inclusiveness' in Microsoft's Responsible AI principles?

Accepted Answer

Ensuring AI systems empower and benefit all people including those with disabilities and diverse demographics. Microsoft's Responsible AI principle of inclusiveness requires that AI systems are designed to empower and benefit all people, including those with disabilities and diverse demographics. This ensures that AI technologies do not discriminate or exclude groups based on ability, culture, or socioeconomic status, aligning with Microsoft's commitment to fairness and accessibility in AI.

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Including all team members in the AI development process regardless of technical skill

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Making AI models available to all organisations regardless of their budget

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Including diverse training data sources to improve model accuracy

Question 13

What is 'AI in HR' (Human Resources) and what specific applications does it enable?

Accepted Answer

CV screening, employee sentiment, attrition prediction, skills analysis, and learning recommendations. AI in HR leverages machine learning and natural language processing to automate and enhance tasks like CV screening (e.g., parsing resumes for relevant skills), employee sentiment analysis (e.g., using NLP on survey responses), attrition prediction (e.g., classification models on historical data), skills gap analysis, and personalized learning recommendations. These applications augment human decision-making rather than replacing it, aligning with common AI workloads in the HR domain.

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Using AI to fully automate all HR decisions without human involvement

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Managing employee payroll and benefits calculations using traditional database systems

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Ensuring HR documents comply with employment law using rule-based systems

Question 14

A retail company uses an AI system to predict customer churn based on demographic and behavioral data. The team discovers that the model gives disproportionately higher churn predictions for customers from a particular zip code, even when their behavior is similar to others. Which Microsoft responsible AI principle is most directly relevant to addressing this issue?

Accepted Answer

Fairness. The model's disproportionate churn predictions for a specific zip code, despite similar behavior, indicates a bias that unfairly impacts that group. Microsoft's Fairness principle directly addresses this by requiring AI systems to treat all groups equitably and avoid discrimination based on sensitive attributes like location. Ensuring fairness involves auditing training data and model outputs for such disparities and applying mitigation techniques.

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Transparency

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Reliability and Safety

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Privacy and Security

Question 15

What is 'orchestration workflow' in Azure AI Language for multi-skill scenarios?

Accepted Answer

A single entry point that routes queries to the appropriate language skill based on intent. In Azure AI Language, the orchestration workflow is a feature that allows you to connect multiple language skills (such as custom question answering, conversational language understanding, and LUIS) into a single endpoint. It acts as a router that analyzes the user's query intent and then directs the request to the most appropriate skill for processing, enabling multi-skill scenarios without requiring separate endpoints or manual routing logic.

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Scheduling language model inference jobs to run at off-peak hours

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Automating the data labelling workflow for custom NLP model training

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Managing the sequence of preprocessing steps applied to text before model inference