AI-900 Practice Questions 2 — 50 Questions

Question 1

A logistics company uses overhead cameras at a shipping dock to read labels on packages. The labels contain text in various fonts, sizes, and orientations, and sometimes the text is partially obscured. Which Azure Computer Vision capability should they use to extract the text from these labels?

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

Accepted Answer

Optical Character Recognition (OCR). Optical Character Recognition (OCR) is the correct choice because it is specifically designed to extract printed or handwritten text from images, handling variations in fonts, sizes, orientations, and partial occlusion. Azure Computer Vision's OCR API (Read API) uses deep-learning models to detect and digitize text from natural scenes, making it ideal for reading labels on packages in a logistics environment.

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Object detection

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Image classification

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Semantic segmentation

Question 2

What is the difference between face detection and face identification?

Accepted Answer

Face detection finds face locations; face identification determines who the person is from an enrolled database. Face detection is a computer vision task that locates human faces in an image or video, returning bounding box coordinates. Face identification (or recognition) goes a step further by matching a detected face against a database of enrolled individuals to determine a specific identity. Option B correctly distinguishes these two operations: detection finds where faces are, while identification determines who the person is.

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Face detection identifies who the person is; face identification counts how many faces are present

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Face detection works on videos; face identification works on static images only

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They are the same operation with different names

Question 3

What is the purpose of Azure AI Speech's 'batch transcription' capability?

Accepted Answer

Asynchronous processing of large volumes of audio files for cost-efficient transcription at scale. Azure AI Speech's batch transcription is designed for asynchronous processing of large volumes of pre-recorded audio files. It allows you to submit multiple audio files for transcription without requiring real-time interaction, making it cost-efficient for scenarios like call center analytics or media captioning where immediate results are not needed.

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Real-time transcription of live audio streams for immediate use

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Synchronizing speech transcription across multiple languages simultaneously

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Training a custom speech recognition model on audio samples

Question 4

What is 'label imbalance' in a classification dataset and how does it affect model training?

Accepted Answer

When one class greatly outnumbers others, causing models to be biased toward the majority class. Label imbalance refers to a situation in classification datasets where one class (the majority class) has significantly more samples than other classes (minority classes). This causes the model to become biased toward predicting the majority class, as it minimizes overall loss by ignoring minority classes, leading to poor generalization and low recall for underrepresented classes.

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When labels in the training data contain spelling errors

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When training labels are applied inconsistently by different human annotators

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When a model produces predictions that don't match any of the training labels

Question 5

What is the Azure AI Translator's 'document translation' capability?

Accepted Answer

Asynchronously translating complete documents while preserving layout and formatting. Azure AI Translator's document translation capability is designed for asynchronous batch translation of entire documents (e.g., Word, PDF, HTML) while preserving the original layout, structure, and formatting. This is achieved through the Document Translation API, which processes files in their entirety rather than translating individual text snippets in real time.

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Translating short text snippets from a chat interface

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Creating translated copies of database records

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Converting documents from one file format to another in a different language

Question 6

A data scientist is preparing a dataset to train a model that predicts customer churn. The dataset includes a column 'CustomerID' which is a unique identifier for each customer. Should the data scientist include the 'CustomerID' column as a feature in the training data?

Accepted Answer

No, because the CustomerID is a random unique identifier with no predictive power for churn.. CustomerID is a unique identifier that does not contain any meaningful pattern or relationship with the target variable (churn). Including such a column would introduce noise and risk overfitting, as the model could memorize each ID rather than learning generalizable patterns. In Azure Machine Learning, features should be predictive attributes, not arbitrary labels.

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Yes, because it uniquely identifies each customer and helps the model differentiate them.

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Yes, because the model can learn patterns from the numeric values.

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No, because the CustomerID column contains too many missing values.

Question 7

A parking lot management company uses security cameras to monitor vehicles. They need to both detect the presence of license plates in an image and read the alphanumeric characters on those plates. Which Azure Computer Vision capability should they use to achieve both requirements?

Accepted Answer

Optical Character Recognition (OCR) - Read API. (OCR - Read API) is correct because Azure's Read API is specifically designed to both detect the presence of text (including license plates) in an image and extract the alphanumeric characters from that text. This meets both requirements—detecting the plate and reading its characters—in a single call, using deep-learning-based recognition models optimized for printed and handwritten text.

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Image Analysis (describe image and detect objects)

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Face API

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Custom Vision (object detection)

Question 8

A self-driving car company tests its AI navigation system in a new city. The system fails to detect a temporary construction barrier and causes a collision. The company wants to ensure that their AI system is robust to unexpected and unusual environmental conditions. Which Microsoft responsible AI principle is most directly relevant to this requirement?

Accepted Answer

Reliability and safety. The requirement is to ensure the AI system is robust to unexpected and unusual environmental conditions, which directly falls under the responsible AI principle of Reliability and safety. This principle focuses on building systems that operate consistently and safely under a wide range of conditions, including edge cases like temporary construction barriers, and that fail gracefully when they cannot perform as expected.

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Fairness

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Privacy and security

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Transparency

Question 9

What does the Azure AI Foundry model catalog provide?

Accepted Answer

A curated collection of AI models from Microsoft and partners for evaluation and deployment. The Azure AI Foundry model catalog provides a curated collection of AI models from Microsoft and partners, including foundation models, industry-specific models, and open-source models like those from Hugging Face. This catalog enables users to evaluate, fine-tune, and deploy models directly within the Azure ecosystem, supporting generative AI workloads such as content generation and natural language processing.

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A library of pre-written Python code for common AI tasks

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A marketplace for purchasing training datasets from vendors

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A service for storing and versioning custom-trained models only

Question 10

A company wants to build a chatbot that can answer questions based on its internal policy documents. The documents are stored in Azure Blob Storage. They plan to use Azure OpenAI to generate answers. Which approach should they use to ensure the answers are grounded in the actual policy content?

Accepted Answer

Use Azure AI Search to index the documents and provide relevant passages as context to GPT-4. Azure AI Search can index the policy documents stored in Azure Blob Storage, enabling retrieval of relevant passages based on the user's query. These passages are then provided as context in the prompt to GPT-4, ensuring the generated answer is grounded in the actual policy content rather than relying on the model's pre-trained knowledge.

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Fine-tune GPT-4 on all policy documents

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Include the entire policy document text in the prompt each time

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Use DALL-E to visualize policy concepts

Question 11

A financial company develops an AI system that recommends loan amounts based on historical data. The historical data includes years of discriminatory lending practices against certain minority groups. As a result, the AI system disproportionately denies loans to members of those groups. Which Microsoft responsible AI principle is most directly violated by this scenario?

Accepted Answer

Fairness. The AI system's loan recommendations are based on historical data that contains discriminatory lending practices, leading to disproportionate denials for minority groups. This directly violates the Fairness principle, which requires AI systems to treat all people equitably and avoid reinforcing existing biases. The system's outputs are not fair because they perpetuate historical inequities, making fairness the most relevant principle.

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Reliability and Safety

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Privacy and Security

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Inclusiveness

Question 12

A city transportation department wants to use a live camera feed at a bus stop to estimate how many people are waiting for the bus. Which Azure Computer Vision capability should they use?

Accepted Answer

C. Object detection. Object detection is the correct capability because it can identify and locate multiple people in a live camera feed, providing bounding boxes around each person. This allows the system to count the number of individuals waiting at the bus stop, which is the core requirement. Optical Character Recognition (OCR) extracts text, face detection identifies faces but not necessarily counts people in a crowd, and semantic segmentation classifies each pixel but is overkill for simple counting.

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A. Optical Character Recognition (OCR)

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B. Face detection

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D. Semantic segmentation

Question 13

What is 'entity linking' in Azure AI Language and how does it differ from NER?

Accepted Answer

Linking identified entities to knowledge base entries (e.g., Wikipedia) for disambiguation. Entity linking in Azure AI Language disambiguates identified entities by associating them with a unique identifier from a knowledge base, such as Wikipedia's Q-numbers. This differs from NER, which only labels entities (e.g., 'person', 'location') without resolving ambiguity—for example, 'Paris' could refer to a city or a person, and entity linking determines the correct one via the knowledge base.

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Creating hyperlinks in a document that connect to related content online

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Connecting named entities across multiple documents to track the same person over time

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Linking entity recognition results to downstream API calls for data enrichment

Question 14

What is the primary difference between supervised and unsupervised machine learning?

Accepted Answer

Supervised uses labeled training data; unsupervised finds patterns in unlabeled data. The primary difference between supervised and unsupervised machine learning is that supervised learning uses labeled training data to learn a mapping from inputs to outputs, while unsupervised learning finds hidden patterns or structures in unlabeled data without predefined labels. This distinction is fundamental to choosing the right approach for a given problem, such as classification (supervised) versus clustering (unsupervised).

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Supervised learning requires human oversight; unsupervised runs automatically

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Supervised is for images; unsupervised is for text

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Supervised is older and less accurate than unsupervised

Question 15

What is 'AI privacy and security' in Microsoft's Responsible AI principles?

Accepted Answer

Protecting personal data from AI systems and securing AI against adversarial attacks and misuse. Microsoft's Responsible AI principle of 'privacy and security' focuses on protecting individuals' personal data from being exposed or misused by AI systems, and ensuring AI models and infrastructure are resilient against adversarial attacks, data poisoning, and other security threats. Option B correctly captures this dual focus on data protection and system security, which is distinct from general Azure encryption or cybersecurity use cases.

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Encrypting all data at rest in Azure storage used by AI workloads

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Using AI to enhance cybersecurity by detecting network intrusions

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Ensuring employees don't share AI model weights externally without authorisation

Question 16

A data scientist trains a regression model to predict house prices. The model performs poorly on both the training data and the test data, showing high error in both sets. Which concept best describes this situation?

Accepted Answer

Underfitting. Underfitting occurs when a model is too simple to capture the underlying patterns in the data, resulting in high error on both the training and test sets. In this regression scenario, the model fails to learn the relationship between features and house prices, leading to poor performance across all data splits.

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Overfitting

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Data leakage

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Feature scaling

Question 17

What is 'face attribute analysis' in Azure AI Face service?

Accepted Answer

Estimating age, emotion, head pose, and appearance attributes from detected faces. Face attribute analysis in Azure AI Face service extracts a set of facial attributes from detected faces, including estimated age, emotion (e.g., happiness, sadness, anger), head pose (pitch, yaw, roll), and appearance traits like facial hair, glasses, and makeup. This is distinct from identification or verification tasks because it does not match faces against a database or compare two images; it simply returns metadata about the face itself.

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Identifying the named person in a photograph using a face database

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Verifying whether a submitted selfie matches a government-issued ID document

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Detecting whether a face has been digitally manipulated or deepfaked

Question 18

What is 'custom named entity recognition' (custom NER) in Azure AI Language?

Accepted Answer

Training a model to recognise domain-specific entities not covered by pre-built NER. Custom named entity recognition (custom NER) in Azure AI Language allows you to train a machine learning model to identify domain-specific entities that are not covered by the pre-built NER model. This is achieved by providing labeled examples of your own entity types, enabling the model to extract specialized terms such as product codes, internal project names, or medical conditions unique to your organization.

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Renaming standard NER entity types to match your organisation's terminology

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A faster, lighter version of NER that uses simpler rules instead of machine learning

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Filtering NER outputs to return only the entity types relevant to your application

Question 19

A hotel chain wants to automatically determine whether online guest reviews express a positive, negative, or neutral opinion about their stays. Which built-in Azure AI Language feature should they use?

Accepted Answer

Sentiment analysis. Sentiment analysis is the correct Azure AI Language feature because it is specifically designed to classify text as positive, negative, or neutral, which directly matches the hotel chain's requirement to determine guest opinions from online reviews. This feature uses machine learning models to evaluate the overall sentiment expressed in a document or sentence, providing a confidence score for each sentiment category.

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Named Entity Recognition (NER)

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Key phrase extraction

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Language detection

Question 20

What is 'speech recognition' as an AI workload?

Accepted Answer

Converting spoken audio into written text. Speech recognition, also known as automatic speech recognition (ASR), is an AI workload that converts spoken language into written text. It processes audio input and maps it to words using acoustic and language models, enabling transcription, voice commands, and dictation. Option B correctly identifies this core function.

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Identifying which employee is speaking during a meeting using their voice

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Recognising specific wake words to activate voice assistant devices

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Detecting background noise in audio to improve recording quality