AI-900 Practice Test 45 — 15 Questions

Question 1

What is 'confusion matrix' and what does it tell you about a classification model?

Accepted Answer

A table showing counts of correct and incorrect predictions broken down by predicted vs. actual class. A confusion matrix is a specific table layout that allows visualization of the performance of a classification model. It shows the counts of true positive, true negative, false positive, and false negative predictions, broken down by each actual class versus each predicted class. This directly tells you not just overall accuracy, but also the types of errors the model is making, which is critical for evaluating classifiers in Azure Machine Learning.

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A measure of how confused users are when interacting with an AI system's predictions

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A graphical display of how confident the model is across its entire test dataset

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A diagram comparing the accuracy of multiple models on the same test set

Question 2

What does 'responsible AI' mean in the context of Microsoft's AI principles?

Accepted Answer

Following principles of fairness, reliability, privacy, inclusiveness, transparency, and accountability in AI systems. Microsoft's responsible AI framework is built on six core principles: fairness, reliability and safety, privacy and security, inclusiveness, transparency, and accountability. These principles guide the development and deployment of AI systems to ensure they are ethical, trustworthy, and beneficial to society. The other options either misrepresent the scope of responsible AI or focus on narrow compliance or access restrictions.

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Using AI only for tasks that generate a financial return on investment

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Ensuring AI models comply with GDPR data residency requirements

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Limiting AI access to only trained professionals to prevent misuse

Question 3

A company deploys an AI system to screen job applications and recommend candidates for interviews. The system consistently rates male candidates higher than equally qualified female candidates. Which Microsoft responsible AI principle is most directly violated?

Accepted Answer

Fairness. The AI system's consistent rating of male candidates higher than equally qualified female candidates demonstrates a clear bias in outcomes based on gender, which directly violates the Fairness principle. Fairness in responsible AI requires that AI systems treat all people equitably, avoiding discrimination based on sensitive attributes such as gender, race, or age. This bias likely stems from biased training data or flawed feature engineering that encodes historical hiring disparities.

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Reliability and safety

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Privacy and security

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Inclusiveness

Question 4

What is 'document intelligence' (Azure AI Document Intelligence) and what types of documents can it process?

Accepted Answer

A service that extracts structured data (fields, tables, key-value pairs) from forms and documents. Azure AI Document Intelligence (formerly Form Recognizer) is a service that uses optical character recognition (OCR) and machine learning to extract structured data—such as fields, tables, and key-value pairs—from scanned forms and documents. This enables automated processing of invoices, receipts, business cards, and other structured documents without manual data entry.

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A service that creates documents from structured data in a database

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A document management system for storing and organising files in Azure

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A grammar checking tool that reviews documents for writing quality

Question 5

What is 'Azure AI Speech's keyword recognition' and what are its use cases?

Accepted Answer

Continuously listening for a specific wake word to activate full speech processing without cloud round-trips. Azure AI Speech's keyword recognition is designed to continuously listen for a specific wake word (e.g., 'Hey Cortana') and activate full speech processing only when that keyword is detected. This allows the system to remain idle until triggered, reducing unnecessary cloud round-trips and conserving bandwidth and processing resources.

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Extracting the most frequently used words from a speech transcript

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Highlighting important keywords in a speech transcript for meeting notes

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Detecting when a customer uses specific product keywords during a support call

Question 6

What is 'Azure Machine Learning Responsible AI dashboard's error analysis'?

Accepted Answer

Identifying data subgroups where the model makes disproportionately more errors than average. Azure Machine Learning Responsible AI dashboard's error analysis is specifically designed to identify data subgroups where the model performs poorly, often revealing bias or systematic failures. It uses a decision tree-based approach to partition the dataset and highlight cohorts with disproportionately high error rates, enabling targeted mitigation. This goes beyond simple aggregate metrics to uncover hidden disparities in model performance.

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A log of all Python exceptions and errors that occurred during model training

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Counting the total number of incorrect predictions across the full test set

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Reviewing error messages from failed Azure ML pipeline runs to diagnose infrastructure issues

Question 7

What is 'computer vision' as a category of AI workload?

Accepted Answer

AI capabilities that interpret and understand images, video, and visual information. Computer vision is an AI workload category that enables systems to extract meaningful information from digital images, videos, and other visual inputs. It involves techniques like object detection, image classification, facial recognition, and optical character recognition (OCR), allowing machines to interpret and act on visual data. This is distinct from display hardware or UI design, as it focuses on understanding content rather than rendering or creating it.

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The display technology used in computer monitors and screens

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Software for designing user interfaces and graphical layouts

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A programming paradigm for writing code that processes visual data efficiently

Question 8

What is 'AI transparency' and why is it challenging for deep learning models?

Accepted Answer

Deep learning models are 'black boxes' — high performance but difficult to explain because of millions of interacting parameters. Deep learning models, particularly those with many layers and millions of parameters, operate as 'black boxes.' Their internal decision-making processes are highly complex and non-linear, making it extremely difficult to trace how specific inputs lead to particular outputs. This lack of interpretability is the core challenge of AI transparency in deep learning.

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Transparency is easy for all AI models because they use simple mathematical formulas

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Transparency only matters for AI systems used in consumer products

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Transparency is fully solved by showing the training data to stakeholders

Question 9

What is the purpose of a confusion matrix in evaluating a classification model?

Accepted Answer

To show the breakdown of correct and incorrect predictions by class. A confusion matrix is a table that compares the actual class labels against the model's predicted class labels, showing the counts of true positives, true negatives, false positives, and false negatives for each class. This breakdown allows you to compute key performance metrics such as accuracy, precision, recall, and F1-score, which are essential for evaluating a classification model's performance. Option B correctly identifies this purpose.

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To measure how long the model takes to make predictions

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To visualize the distribution of training data

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To show how confused users are when interacting with AI systems

Question 10

A hospital deploys an AI diagnostic system that achieves 95% accuracy overall. However, for patients from a specific minority ethnic group, the accuracy drops to 60%. The hospital decides to continue using the system because the overall accuracy is acceptable. Which Microsoft responsible AI principle is most directly violated by this decision?

Accepted Answer

Fairness. The decision to continue using the system despite a 60% accuracy for a minority ethnic group directly violates the Fairness principle. Fairness requires that AI systems treat all groups equitably and avoid discrimination, even if overall metrics are high. A 35% accuracy gap between groups indicates systemic bias, which the hospital is ignoring by prioritizing aggregate performance over equitable outcomes.

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Inclusiveness

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Transparency

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Accountability

Question 11

What is 'data augmentation' and how does it help with limited training data?

Accepted Answer

Creating synthetic training variants (flips, rotations, synonyms) to expand small datasets. Data augmentation is a technique that artificially expands a training dataset by applying transformations (e.g., image flips, rotations, cropping, or text synonym replacement) to existing samples. This helps models generalize better when real-world data is scarce, reducing overfitting without requiring new labeled data collection.

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Collecting more labelled data from external sources to supplement training

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Increasing the number of compute nodes to process large training datasets faster

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Adding more evaluation metrics to get a richer view of model performance

Question 12

A hospital uses an AI system to analyze patient records for research. To protect patient identities, the system should not store or transmit any personally identifiable information (PII) outside the secure network. Which responsible AI principle is most directly addressed by this requirement?

Accepted Answer

Privacy and Security. The requirement to not store or transmit PII outside the secure network directly addresses the Privacy and Security principle. This principle ensures that individuals' data is protected from unauthorized access or exposure, which is critical in healthcare AI systems handling sensitive patient records. By restricting PII to the secure network, the system upholds data confidentiality and compliance with regulations like HIPAA.

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Fairness

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Inclusiveness

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Reliability and Safety

Question 13

A legal firm needs to process thousands of contracts to automatically identify important terms such as dates, monetary amounts, names of parties, and legal citations. Which built-in feature of the Azure AI Language service is best suited for this task?

Accepted Answer

C) Entity Recognition. Entity Recognition (also called Named Entity Recognition, NER) is the correct choice because it is specifically designed to identify and categorize predefined entities such as dates, monetary amounts, person names, organizations, and legal citations from unstructured text. The Azure AI Language service's NER capability can automatically extract these important terms from thousands of contracts, making it the ideal built-in feature for this task.

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A) Sentiment Analysis

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B) Key Phrase Extraction

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D) Language Detection

Question 14

What information does Azure AI Face service provide about detected faces beyond just their location?

Accepted Answer

Age estimate, emotion, head pose, and other facial attributes. Azure AI Face service can extract a wide range of facial attributes beyond just the bounding box coordinates. These include age estimate, emotion (e.g., happiness, sadness, surprise), head pose (pitch, yaw, roll), facial hair, glasses, and more, making option B correct.

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Only the coordinates of the face bounding box

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The person's name and identity from a public database

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Only whether the face belongs to a human or not

Question 15

A company deploys an AI chatbot on its website to answer customer questions. The company wants to be transparent about the nature of the interaction. Which Microsoft responsible AI principle is most directly relevant to ensuring users know they are communicating with an AI and not a human?

Accepted Answer

Transparency. Transparency is the Microsoft responsible AI principle that requires AI systems to be designed so that users are aware they are interacting with an AI, not a human. In the context of a chatbot, this means clearly disclosing the AI nature of the system, such as through a label or introductory message, to avoid deception and build trust.

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Inclusiveness

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Privacy and security

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Accountability