AI-900 Practice Exam 61 — 20 Questions

Question 1

Which Azure AI service can identify and extract named entities (people, organizations, locations, dates) from text?

Accepted Answer

Azure AI Language (Named Entity Recognition). Azure AI Language's Named Entity Recognition (NER) capability is specifically designed to identify and categorize named entities such as people, organizations, locations, and dates from unstructured text. This is a core feature of the Natural Language Processing (NLP) workload within Azure AI Language, making option B the correct choice.

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Azure AI Vision

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Azure AI Translator

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Azure AI Speech

Question 2

What is the Azure AI Language feature 'custom named entity recognition' used for?

Accepted Answer

Training models to recognize domain-specific entity types unique to your business. Custom named entity recognition (NER) in Azure AI Language allows you to train a model to identify and extract domain-specific entities that are not covered by the pre-built entity catalog. This is achieved by providing labeled example data, which the service uses to learn the unique entity types relevant to your business, such as product codes, internal document IDs, or specialized medical terms.

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Automatically renaming Azure resources with appropriate names

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Replacing personally identifiable information with pseudonyms

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Detecting when text contains company-specific named brands

Question 3

A healthcare organization needs to automatically redact personally identifiable information (PII) such as patient names and addresses from unstructured medical notes, while keeping clinical terms like disease names and medications. They want to use prebuilt Azure AI Language features without any custom training. Which combination of features should they use?

Accepted Answer

PII detection and Text Analytics for Health. PII detection identifies and redacts personally identifiable information like patient names and addresses, while Text Analytics for Health extracts clinical entities such as diseases and medications from unstructured medical notes. Both are prebuilt Azure AI Language features that require no custom training, making them ideal for this healthcare redaction scenario.

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Key phrase extraction and sentiment analysis

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Named Entity Recognition (NER) and language detection

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Entity linking and conversational language understanding

Question 4

What is the Azure AI Evaluation SDK used for in generative AI development?

Accepted Answer

Systematically measuring quality (groundedness, relevance, coherence) and safety of generative AI responses. The Azure AI Evaluation SDK is specifically designed to systematically measure the quality and safety of generative AI responses. It evaluates key metrics such as groundedness (how well the response aligns with source data), relevance, and coherence, as well as safety aspects like content filtering and harm detection. This makes it essential for validating and improving generative AI applications before deployment.

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Evaluating the environmental impact of AI model training

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Evaluating Azure subscription costs for AI workloads

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A peer review system for human evaluation of AI responses

Question 5

A hotel chain receives thousands of online guest reviews each month. The management wants to automatically determine whether the overall feedback for each review is positive, negative, or neutral to identify areas for improvement. Which Azure AI Language feature should they use?

Accepted Answer

Sentiment analysis. Sentiment analysis is the correct Azure AI Language feature because it evaluates text to determine the overall sentiment—positive, negative, or neutral—at the document or sentence level. This directly matches the hotel chain's requirement to classify each review's feedback automatically, enabling them to identify areas for improvement based on sentiment trends.

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Key phrase extraction

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Named entity recognition

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Language detection

Question 6

A data scientist is training a logistic regression model to predict customer churn using a small dataset with 500 records and 200 features. The model achieves 97% accuracy on the training set but only 65% on a held-out test set, indicating severe overfitting. The data scientist wants to reduce overfitting by automatically eliminating irrelevant features. Which technique should the data scientist apply?

Accepted Answer

Apply L1 regularization (Lasso) to the model. L1 regularization (Lasso) adds a penalty equal to the absolute value of the magnitude of coefficients, which can shrink some coefficients exactly to zero. This performs automatic feature selection by eliminating irrelevant features, directly addressing the overfitting caused by having 200 features on only 500 records. The high training accuracy (97%) versus low test accuracy (65%) is a classic sign of overfitting that L1 regularization mitigates by reducing model complexity.

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Apply L2 regularization (Ridge) to the model

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Use k-fold cross-validation to select the best model

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Increase the number of training samples by data augmentation

Question 7

What is the Azure Machine Learning workspace?

Accepted Answer

The top-level Azure ML resource that organizes experiments, models, compute, and deployments. The Azure Machine Learning workspace is the top-level resource in Azure that serves as a centralized hub for managing all machine learning activities. It organizes experiments, models, compute targets, and deployments, providing a unified environment for the entire ML lifecycle. This is the correct answer because the workspace is the foundational resource that ties together all other Azure ML components.

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A web-based IDE for writing machine learning code in Python

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A virtual machine pre-configured with ML tools and libraries

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A dedicated GPU cluster for distributed deep learning training

Question 8

A bank deploys an AI system to approve loan applications. The system was trained on historical data that contains systematic biases against certain ethnic groups. Despite awareness of this bias, the bank proceeds with deployment, expecting the system to correct itself over time. Which Microsoft responsible AI principle is most directly violated?

Accepted Answer

Fairness. The bank knowingly deployed an AI system trained on biased historical data, expecting it to self-correct. This directly violates the Fairness principle, which requires AI systems to treat all groups equitably and avoid discrimination. Microsoft's responsible AI framework mandates that biases be actively identified and mitigated before deployment, not left to chance.

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Reliability and safety

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Transparency

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Privacy and security

Question 9

A manufacturing company uses cameras on an assembly line to inspect products for cosmetic defects such as scratches, dents, or color inconsistencies. They need to classify each product as 'defective' or 'non-defective' and also identify the precise region (e.g., a specific area of the product surface) that contains the defect. Which Azure Computer Vision capability should they use?

Accepted Answer

Semantic segmentation. Semantic segmentation is the correct choice because it assigns a class label (e.g., 'defective' or 'non-defective') to every pixel in the image, enabling the model to not only classify the product but also delineate the exact boundary of the defect region. This pixel-level precision is required to identify the precise area of the product surface containing the scratch, dent, or color inconsistency.

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Image classification

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Object detection

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Optical Character Recognition (OCR)

Question 10

A company wants to build a chatbot that can engage in free-form conversations with customers, answering questions and providing information without being limited to a fixed set of responses. Which type of AI model is most suitable?

Accepted Answer

Generative language model. A generative language model is the most suitable for building a chatbot that engages in free-form conversations because it can generate novel, contextually relevant responses based on the input it receives, rather than selecting from a fixed set of predefined answers. This capability is essential for handling the open-ended nature of customer queries, where the chatbot must produce coherent and varied responses dynamically.

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Classification model

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Regression model

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Object detection model

Question 11

A company uses Azure OpenAI Service to generate executive summaries of lengthy reports. The generated summaries sometimes include information that was not present in the original report, making them unreliable. Which Azure OpenAI Service feature should the company use to anchor the model to the provided report content?

Accepted Answer

Use the 'Add your data' feature (also known as 'Azure OpenAI on your data'). The 'Add your data' feature (Azure OpenAI on your data) allows the model to ground its responses in the specific content you provide, such as the original report. This prevents the model from generating information not present in the source, addressing the hallucination issue directly by restricting the model's knowledge base to the uploaded documents.

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Increase the temperature parameter

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Increase the frequency_penalty parameter

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Use the system message to instruct the model to only use provided content

Question 12

A creative agency wants to use Azure OpenAI to generate unique images for social media campaigns based on text descriptions. Which Azure OpenAI model should they use for this purpose?

Accepted Answer

DALL-E 3. DALL-E 3 is the correct choice because it is the Azure OpenAI model specifically designed for generating images from natural language text descriptions. It uses a diffusion-based architecture to create high-quality, unique visuals that align with the provided prompts, making it ideal for creative social media campaigns.

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GPT-4

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Codex

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Whisper

Question 13

What is 'speech synthesis markup language' (SSML) in Azure AI Speech?

Accepted Answer

An XML language for controlling TTS speech rate, pitch, pauses, and emphasis. SSML is an XML-based markup language that allows you to fine-tune text-to-speech (TTS) output by controlling prosodic elements such as speech rate, pitch, volume, pauses, and emphasis. In Azure AI Speech, SSML tags like , , and are embedded in the input text to produce more natural and expressive synthesized speech.

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A markup language for styling how text is displayed on screen in Azure apps

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A security standard for marking sensitive spoken content for redaction

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A standard for describing the structure of multi-turn dialogue in speech interfaces

Question 14

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.

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Fairness

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Reliability and safety

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Transparency

Question 15

What is Azure Machine Learning's 'responsible AI dashboard'?

Accepted Answer

A multi-dimensional model analysis tool covering error analysis, interpretability, and fairness. The responsible AI dashboard in Azure Machine Learning is a comprehensive, multi-dimensional tool that integrates several open-source components (such as Error Analysis, InterpretML, and Fairlearn) to help data scientists and developers evaluate and improve their models across error analysis, interpretability, and fairness dimensions. It is designed to operationalize responsible AI practices by providing a single pane of glass for debugging model behavior, understanding feature importance, and detecting potential fairness issues.

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A legal compliance checklist for AI regulations in different countries

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A monitoring dashboard for tracking API usage and costs

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A tool for documenting model cards for AI transparency

Question 16

What does 'human-in-the-loop' data labeling mean in Azure Machine Learning?

Accepted Answer

Using ML to pre-label data while routing uncertain cases to human reviewers for quality assurance. In Azure Machine Learning, 'human-in-the-loop' data labeling combines ML model pre-labeling with human review for uncertain cases. This approach improves efficiency by automating easy labels while ensuring quality and accuracy through human oversight on ambiguous or low-confidence predictions, directly supporting active learning workflows.

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Replacing all human data labelers with ML models

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Requiring all data to be labeled by humans without any ML assistance

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Using a loop in Python code to automate the labeling process

Question 17

What is 'AI bias' and how can it harm individuals in high-stakes decisions?

Accepted Answer

Systematic unfair outcomes for demographic groups caused by biased training data or design choices. AI bias refers to systematic and unfair outcomes that disproportionately affect certain demographic groups, often resulting from biased training data, flawed design choices, or improper feature selection. In high-stakes decisions such as loan approvals, hiring, or criminal sentencing, such bias can lead to discrimination, reinforce societal inequalities, and cause real harm to individuals by denying them opportunities or subjecting them to unjust treatment.

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When a model's predictions consistently favour one output class due to class imbalance

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When an AI model performs worse on unseen test data than on the training data

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The tendency of users to trust AI recommendations over their own judgment

Question 18

A developer uses Azure OpenAI to generate customer support responses. The developer wants to ensure that the model does not produce responses that contain offensive, hateful, or harmful language, even when users input problematic prompts. Which Azure OpenAI feature should the developer configure to achieve this?

Accepted Answer

Enabling the content filter. The content filter in Azure OpenAI is specifically designed to detect and block offensive, hateful, or harmful language in both user prompts and model responses. By enabling this feature, the developer ensures that even if a user submits a problematic input, the model's output will be filtered to prevent generating inappropriate content. This directly addresses the requirement to avoid harmful language.

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Setting a low temperature value

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Limiting the max_tokens parameter

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Setting a high frequency penalty

Question 19

What is 'Azure AI Vision's image moderation' and what content categories does it detect?

Accepted Answer

Detecting sexually explicit (adult) and suggestive (racy) content in images with confidence scores. Azure AI Vision's image moderation is specifically designed to detect sexually explicit (adult) and suggestive (racy) content in images, returning confidence scores for each category. This is a core feature of the computer vision service that helps platforms comply with content policies by classifying inappropriate visual content rather than modifying images or checking for copyright violations.

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Moderating the resolution and quality of user-uploaded images for platform standards

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Modifying images to blur or remove inappropriate elements automatically

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Detecting copyright violations in user-uploaded images by comparing to known copyrighted works

Question 20

A news agency publishes hundreds of articles daily. They want to automatically extract the main topics discussed in each article, such as 'politics', 'economy', or 'sports', to categorize content without manual tagging. Which built-in Azure AI Language feature should they use?

Accepted Answer

Key phrase extraction. Key phrase extraction is the correct choice because it identifies the main topics or subjects discussed in a document, such as 'politics', 'economy', or 'sports', without requiring manual tagging. This feature returns a list of key phrases that represent the core content of each article, directly addressing the need to automatically categorize content by topic.

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Named entity recognition

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Sentiment analysis

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Language detection