AI-900 Practice Exam 52 — 20 Questions

Question 1

A data scientist wants to train a model that predicts whether a customer will respond to a marketing offer (yes or no). The dataset includes features such as age, income, past purchase history, and the labeled outcome (responded or not responded) for previous customers. Which type of machine learning is this?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Supervised learning. This is supervised learning because the dataset includes labeled outcomes (responded or not responded) for previous customers, which the model uses to learn a mapping from input features (age, income, past purchase history) to the correct output. The goal is to predict a categorical label (yes/no), making it a classification task within supervised learning.

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Unsupervised learning

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Reinforcement learning

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Semi-supervised learning

Question 2

A logistics company receives thousands of handwritten shipping labels daily. They need an automated solution to extract the destination address, sender name, and package weight from these labels. Which prebuilt Azure Computer Vision capability should they use?

Accepted Answer

Optical Character Recognition (OCR). Azure Computer Vision's Optical Character Recognition (OCR) API is specifically designed to extract printed or handwritten text from images. In this scenario, the handwritten shipping labels contain textual data (destination address, sender name, package weight), and OCR can read and digitize that text for automated processing. The other options address different visual tasks—object detection, classification, or facial recognition—none of which extract text content.

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Object detection

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Image classification

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Facial recognition

Question 3

A logistics warehouse uses a conveyor belt system to move packages. They need to automatically read the alphanumeric serial numbers printed on labels attached to each box. The labels may have different fonts and be somewhat dusty. Which Azure Computer Vision feature should they use?

Accepted Answer

Optical Character Recognition (OCR) using the Read API. The Read API, part of Azure Computer Vision's OCR capabilities, is specifically designed to extract printed and handwritten text from images, including alphanumeric serial numbers. It can handle varying fonts and degraded image quality (e.g., dusty labels) by using deep-learning models optimized for text recognition. This makes it the correct choice for reading serial numbers from conveyor belt packages.

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Image Classification

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Object Detection

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Image Analysis (captioning and tagging)

Question 4

An autonomous delivery robot uses AI to navigate sidewalks. The robot occasionally fails to detect pedestrians in low-light conditions, leading to near-collisions. The company wants to ensure the system is robust and safe before wider deployment. Which Microsoft responsible AI principle is most directly relevant?

Accepted Answer

Reliability and safety. The robot's failure to detect pedestrians in low-light conditions directly impacts the system's ability to operate reliably and safely. The Reliability and safety principle in Microsoft's responsible AI framework requires that AI systems perform consistently under expected conditions and fail gracefully when they cannot. Ensuring the robot can handle edge cases like low light is a core safety requirement before deployment.

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Fairness

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Privacy and security

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Transparency

Question 5

What is 'Azure AI Content Safety' and what types of harmful content does it detect?

Accepted Answer

A service that detects hate, violence, sexual, and self-harm content in text and images at configurable severity levels. Azure AI Content Safety is a cloud service that detects harmful user-generated and AI-generated content in text and images. It identifies categories such as hate, violence, sexual, and self-harm content, and allows you to configure severity levels (safe, low, medium, high) to filter content appropriately. This makes option B correct because it accurately describes the service's purpose and the specific types of harmful content it detects.

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A firewall that blocks malicious network traffic from reaching Azure AI services

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A GDPR compliance tool that detects and redacts personal data from AI training datasets

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Copyright detection software that identifies AI-generated content derived from copyrighted material

Question 6

A retail company wants to use Azure Computer Vision to automatically monitor shelf inventory. They need to detect whether items are present on a shelf and count the number of items, without needing to identify the specific product type. Which prebuilt Computer Vision capability should they use?

Accepted Answer

Object detection. Object detection (Option C) is the correct prebuilt Computer Vision capability because it can both locate items within an image using bounding boxes and count them, without requiring identification of the specific product type. This aligns directly with the requirement to detect presence and count items on a shelf, as object detection outputs the coordinates and count of detected objects, not their classification into fine-grained categories.

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Optical Character Recognition (OCR)

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Image classification

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Semantic segmentation

Question 7

A retail store wants to use an AI solution to automatically monitor security camera feeds and detect when a shelf is empty or if a person is in a restricted area. Which type of AI workload is best suited for this task?

Accepted Answer

Computer Vision. Computer Vision is the correct AI workload because it enables the system to analyze video frames from security cameras to detect visual patterns such as empty shelves (object absence) or unauthorized persons in restricted areas (object presence and location). This workload uses image classification, object detection, and semantic segmentation to interpret visual data in real time.

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Natural Language Processing

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Speech Recognition

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Anomaly Detection

Question 8

A data scientist trains a machine learning model to predict housing prices. On the training data, the model achieves an R-squared value of 0.99, but on a separate validation dataset it achieves an R-squared of only 0.65. What is the most likely issue with this model?

Accepted Answer

Overfitting. The model performs exceptionally well on the training data (R² = 0.99) but poorly on the validation data (R² = 0.65), which is a classic symptom of overfitting. Overfitting occurs when the model learns noise and specific patterns in the training set that do not generalize to unseen data, often due to excessive complexity (e.g., too many features or deep decision trees). In Azure Machine Learning, this can be detected by comparing training and validation metrics in automated ML runs or by using regularization techniques like L1/L2 penalties.

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Underfitting

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High bias

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Insufficient training data

Question 9

Which Azure AI service can read text from a photo of a street sign taken by a mobile device?

Accepted Answer

Azure AI Vision (Read API / OCR). Azure AI Vision's Read API (part of the Computer Vision service) is designed to extract printed and handwritten text from images, including photos of street signs. It uses optical character recognition (OCR) to detect and digitize text, making it the correct choice for reading text from a mobile device photo.

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Azure AI Speech

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Azure AI Language

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Azure AI Translator

Question 10

What is 'model lineage' in Azure Machine Learning?

Accepted Answer

A tracked history of the dataset, code, hyperparameters, and compute used to produce a model. Model lineage in Azure Machine Learning is a tracked history that captures the complete lifecycle of a model, including the dataset, code, hyperparameters, and compute environment used to produce it. This is essential for reproducibility, auditability, and governance, as it allows data scientists to trace exactly how a model was trained and which artifacts were involved. Azure ML automatically logs this lineage through its run history and model registry, ensuring every model version is linked to its training run.

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The family tree of model architectures showing which models inspired the design

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The geographic lineage of training data showing which regions it was collected from

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The sequence of model versions deployed to production over time

Question 11

What is 'imbalanced classification' handling using 'SMOTE'?

Accepted Answer

Generating synthetic minority class examples by interpolating between existing examples. SMOTE (Synthetic Minority Over-sampling Technique) is a data augmentation method that creates synthetic examples for the minority class by interpolating between existing minority class instances. It selects a minority example, finds its k-nearest neighbors from the same class, and generates new samples along the line segments connecting the example to those neighbors. This balances the class distribution without duplicating existing data or discarding majority class examples.

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A technique for collecting more real minority class examples from external data sources

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Removing majority class examples until all classes have equal representation

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Setting model confidence thresholds to classify more examples as the minority class

Question 12

A company uses Azure OpenAI to build a customer service chatbot. They want to prevent malicious users from injecting prompts that cause the chatbot to behave unexpectedly, such as revealing its system instructions. Which responsible AI consideration is most directly relevant?

Accepted Answer

Reliability and Safety. Prompt injection attacks target the system by embedding malicious instructions in user input, causing the model to override its original directives or reveal sensitive information. This directly undermines the reliability and safety of the AI system, as the chatbot's behavior becomes unpredictable and potentially harmful. Azure OpenAI's safety systems (e.g., content filtering, abuse detection) are designed to mitigate such risks, making Reliability and Safety the most relevant responsible AI consideration.

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Fairness

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Privacy and Security

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Inclusiveness

Question 13

What is 'image recognition for accessibility' and how does Microsoft's Seeing AI app use it?

Accepted Answer

An app using Azure AI Vision to describe scenes, read text, and identify objects aloud for blind users. Microsoft's Seeing AI app leverages Azure AI Vision (specifically the Computer Vision API) to perform real-time image recognition for accessibility. It describes scenes, reads text via optical character recognition (OCR), and identifies objects aloud, enabling blind or low-vision users to understand their surroundings through audio feedback.

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A feature that makes AI models accessible to users without programming expertise

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Accessibility compliance checking software that validates AI applications meet WCAG standards

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Screen reader software that makes Azure portal accessible to keyboard-only users

Question 14

A bank deploys an AI system to approve personal loans. The system uses a complex deep learning model that produces a decision (approve or reject) without any explanation of why. Loan applicants who are rejected are not given any reason. According to Microsoft's responsible AI principles, which principle is most directly violated by this system?

Accepted Answer

Transparency. The system's inability to provide any explanation for its loan approval or rejection decisions directly violates the transparency principle. Microsoft's responsible AI principle of transparency requires that AI systems be understandable and that users be informed about how decisions are made, including the factors that influenced the outcome. A black-box deep learning model that gives no reasoning or feedback to rejected applicants fails this requirement.

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Fairness

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Reliability and safety

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Privacy and security

Question 15

Drag and drop the steps to process text with Azure Text Analytics (Language service) into the correct order.

Accepted Answer

Step 1: Provision an Azure Text Analytics resource, then Step 2: Retrieve the endpoint and key, then Step 3: Instantiate the Text Analytics client, then Step 4: Submit a document for analysis and process the response.. Using Text Analytics involves setting up a resource, making an API call, and interpreting results.

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Step 2: Retrieve the endpoint and key, then Step 1: Provision the resource, then Step 3: Instantiate the client, then Step 4: Submit a document.

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Step 1: Provision the resource, then Step 3: Instantiate the client, then Step 2: Retrieve the endpoint and key, then Step 4: Submit a document.

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Step 1: Provision the resource, then Step 2: Retrieve the endpoint and key, then Step 4: Submit a document, then Step 3: Instantiate the client.

Question 16

A legal firm needs to automatically extract key information from contracts, including the names of parties involved, important dates, and monetary amounts. Which Azure AI Language feature should they use to identify and extract these specific pieces of information from the text?

Accepted Answer

Named Entity Recognition (NER). Named Entity Recognition (NER) is the correct Azure AI Language feature because it is specifically designed to identify and categorize entities such as people (parties involved), dates, and monetary amounts from unstructured text. This directly matches the legal firm's requirement to extract key information from contracts.

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Sentiment analysis

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Key phrase extraction

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Language detection

Question 17

A data scientist is training a regression model to predict house prices using features like square footage, number of bedrooms, and location. After evaluating the model on a test set, the data scientist wants to select a metric that measures the average magnitude of prediction errors in the same units as the target variable (price). Which evaluation metric should the data scientist use?

Accepted Answer

Root Mean Squared Error (RMSE). Root Mean Squared Error (RMSE) is the correct metric because it measures the average magnitude of prediction errors in the same units as the target variable (price). RMSE is computed as the square root of the average squared differences between predicted and actual values, which brings the error metric back to the original unit (e.g., dollars), making it directly interpretable for regression tasks like house price prediction.

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Accuracy

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F1 Score

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Precision

Question 18

A company builds an AI system to filter job applications and rank candidates. The system is trained on historical hiring data. To reduce potential bias, the company removes protected attributes such as gender and ethnicity from the training data. However, after deployment, the system still shows a statistically significant bias against female candidates. Which Microsoft responsible AI principle most directly requires the company to investigate and address this remaining bias, even when protected attributes are removed?

Accepted Answer

Fairness. The Fairness principle requires AI systems to treat all people fairly and avoid creating or reinforcing discriminatory outcomes. Even when protected attributes like gender are removed from training data, bias can persist through proxy variables (e.g., zip code, education history) that correlate with protected attributes. The company must investigate and mitigate this remaining bias because Fairness mandates proactive assessment and correction of disparate impact, not just removal of obvious features.

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Inclusiveness

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Reliability and safety

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Transparency

Question 19

A social media platform uses Azure OpenAI Service to generate summaries of user comments. The development team discovers that sometimes the generated summaries include offensive or harmful language that was present in the original comments. The team wants to ensure that the generated output is always free of hate speech, profanity, and self-harm references. What should the team configure in the Azure OpenAI Service?

Accepted Answer

Configure a content filter. Azure OpenAI Service provides built-in content filtering that can be configured to block hate speech, profanity, and self-harm references in both input prompts and generated completions. This ensures that even if offensive language appears in the original user comments, the generated summaries will be free of such harmful content. The content filter operates at the service level, applying predefined severity thresholds to filter out undesirable language.

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Set the temperature parameter to 0

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Increase the max_tokens parameter

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Use a grounding source

Question 20

A customer service team wants to analyze thousands of support tickets to automatically categorize them into predefined topics like 'billing', 'technical issue', and 'account management'. They have a small set of labeled tickets for each category. Which Azure AI Language feature should they use?

Accepted Answer

Custom Text Classification. The Custom Text Classification feature of Azure AI Language allows you to train a model using a small set of labeled tickets to automatically categorize text into predefined topics like 'billing', 'technical issue', and 'account management'. This feature is specifically designed for scenarios where you have labeled data and need to classify documents into custom categories, making it ideal for analyzing support tickets.

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Pre-built Text Classification

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Sentiment Analysis

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Key Phrase Extraction