AI-900 Practice Test 27 — 15 Questions

Question 1

What is 'image classification' in Azure AI Custom Vision?

Accepted Answer

Assigning a category label to an entire image based on its dominant visual content. Image classification in Azure AI Custom Vision involves training a model to assign a single category label (e.g., 'dog', 'cat') to an entire image based on its dominant visual content. This is a supervised learning task where the model learns from labeled images to predict the most likely class for new, unseen images. Option B correctly describes this core functionality.

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Organising image files into folders on Azure Blob Storage by date

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Converting colour images to black and white for accessibility purposes

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Sorting images by their file size and resolution metadata

Question 2

What is 'model registry' in Azure Machine Learning?

Accepted Answer

A centralised versioned store for tracking and managing trained models and their lineage. The model registry in Azure Machine Learning is a centralized, versioned store that tracks trained models along with their metadata, lineage, and lifecycle. It enables data scientists to register, version, and manage models, ensuring reproducibility and governance across the ML lifecycle.

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A public marketplace where organisations can buy pre-trained models from third parties

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A database of domain-specific vocabularies used for NLP model training

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A compliance register documenting AI models used by an organisation for audit purposes

Question 3

What is 'ensemble learning' in machine learning and why does it improve performance?

Accepted Answer

Combining predictions from multiple models to improve accuracy and robustness. Ensemble learning combines predictions from multiple models (e.g., bagging, boosting, stacking) to reduce variance, bias, or improve robustness. By aggregating diverse models, it often achieves higher accuracy than any single model, as errors from individual models are averaged out or corrected. This is a core technique in Azure Machine Learning, where ensembles like Random Forest or Gradient Boosting are commonly used.

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Training a single very large model on the full dataset without any data splitting

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Selecting the best model from a group of candidates after evaluation

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Running the same model multiple times with different random seeds to test stability

Question 4

What is the purpose of Azure AI Video Indexer's transcript feature?

Accepted Answer

To automatically convert speech in videos to searchable text with timestamps. Azure AI Video Indexer's transcript feature uses automatic speech recognition (ASR) to convert spoken audio in videos into a text transcript, which is then indexed with precise timestamps for each word or phrase. This enables users to search, navigate, and analyze video content by keyword or phrase, making the video's audio content fully searchable and accessible.

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To translate video subtitles into multiple languages

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To generate written scripts for producing new videos

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To extract text visible in video frames (on-screen text)

Question 5

What is 'machine translation' in Azure AI Translator and what languages does it support?

Accepted Answer

Automatically converting text from one natural human language to another using AI. Machine translation in Azure AI Translator refers to the automated conversion of text from one natural human language to another using AI models. This is the core functionality of the Translator service, which supports over 100 languages and dialects for text translation, enabling real-time or batch translation of written content.

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Translating programming code from one language to another (e.g., Python to JavaScript)

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Translating user requirements documents into technical specifications for developers

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Converting audio speech from one language to text in another language

Question 6

What is 'object tracking' in computer vision and how does it differ from object detection?

Accepted Answer

Maintaining the identity of detected objects across consecutive video frames with persistent IDs. Object tracking maintains the identity of detected objects across consecutive video frames by assigning persistent IDs, enabling the system to follow the same object over time. This differs from object detection, which identifies and locates objects in a single frame without preserving identity across frames. In Azure Video Indexer or Custom Vision, tracking is essential for scenarios like counting unique people or vehicles in a video stream.

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Detecting the same object across multiple images in a photo album

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Monitoring GPS location of physical objects using IoT sensors

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Detecting when a tracked object leaves the camera's field of view

Question 7

What is 'AI-assisted labelling' in Azure Machine Learning data labelling?

Accepted Answer

Using a partially trained model to pre-populate labels that human annotators verify and correct. AI-assisted labelling in Azure Machine Learning uses a partially trained model to automatically suggest labels for unlabelled data. Human annotators then review and correct these suggestions, which speeds up the labelling process while maintaining quality. This is a form of active learning where the model iteratively improves as more labelled data is verified.

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Automatically generating descriptive captions for images using a pre-trained model

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Deploying a model to production without any human review of its outputs

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Using AI to detect and remove incorrectly labelled examples from a completed dataset

Question 8

A data scientist has a dataset with 100 features and 10,000 samples. They want to reduce the number of features while retaining as much variance as possible, to improve model training speed and reduce overfitting. Which technique should they use?

Accepted Answer

Principal Component Analysis (PCA). Principal Component Analysis (PCA) is an unsupervised dimensionality reduction technique that transforms the original features into a new set of orthogonal components, ordered by the amount of variance they capture. By selecting only the top principal components, the data scientist can significantly reduce the feature count (e.g., from 100 to 20) while retaining the majority of the dataset's variance, which directly improves model training speed and reduces overfitting.

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Feature scaling

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Regularization

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Cross-validation

Question 9

What is the purpose of Microsoft's 'Responsible AI Impact Assessment'?

Accepted Answer

A pre-deployment framework for identifying and mitigating potential AI harms. The Responsible AI Impact Assessment is a pre-deployment framework designed to help organizations identify, document, and mitigate potential harms associated with AI systems before they are released. It aligns with Microsoft's responsible AI principles, such as fairness, reliability, privacy, and transparency, ensuring that risks are systematically addressed rather than measured after deployment.

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A performance benchmark measuring AI response times

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A financial model for calculating AI project ROI

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A testing framework for measuring AI model accuracy

Question 10

A hospital wants to automatically extract patient symptoms and medication names from clinical notes. They have a set of pre-defined categories for symptoms and medications, and they have manually labeled a few hundred sentences to indicate which text spans belong to each category. Which Azure AI Language feature should they use to build this custom entity extraction solution?

Accepted Answer

Custom entity extraction. Custom entity extraction (D) is the correct choice because the hospital needs to identify specific text spans (symptoms and medication names) based on their own pre-defined categories, using a small set of manually labeled sentences for training. This is exactly what Azure's custom named entity recognition (NER) feature does—it allows you to train a model to extract custom entities from unstructured text, tailored to your domain-specific labels.

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Pre-built entity recognition

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Key phrase extraction

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Custom text classification

Question 11

What is 'curriculum learning' and how does it relate to training stability?

Accepted Answer

Training models on progressively harder examples to improve stability and convergence. Curriculum learning is a training strategy where a model is first exposed to simpler examples and then gradually introduced to more complex ones. This approach improves training stability by preventing the model from being overwhelmed by difficult patterns early on, which can cause large gradient updates and divergence. By structuring the learning process, the model converges more reliably and often achieves better generalization.

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Designing a course curriculum using AI to personalise learning for students

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A structured plan for the sequence of ML courses a data scientist should take

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Using a pre-defined curriculum of hyperparameter values to systematically explore the search space

Question 12

A logistics company scans thousands of packages daily. They need an automated system to read handwritten shipping labels to sort packages correctly. Which Azure Computer Vision capability should they use?

Accepted Answer

Optical Character Recognition (OCR). Optical Character Recognition (OCR), because the scenario requires extracting handwritten text from images of shipping labels to automate sorting. OCR is the specific Azure Computer Vision capability designed to detect and read printed or handwritten text from images, returning machine-readable text that can be used for downstream processing.

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Image Analysis (descriptions and tags)

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Object Detection

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Face API

Question 13

A legal firm needs to automatically extract case-specific entities such as 'docket number', 'plaintiff attorney', and 'court name' from legal documents. They have a small set of manually labeled examples for each entity. Which Azure AI Language feature should they use to build this custom entity extraction solution?

Accepted Answer

Custom named entity recognition (NER). Custom named entity recognition (NER) allows you to train a model with your own labeled examples to extract domain-specific entities like 'docket number' and 'plaintiff attorney'. Prebuilt entity extraction only recognizes common, generic entities (e.g., person, location) and cannot be customized for legal case-specific terms. This makes custom NER the correct choice for building a tailored extraction solution with a small set of manually labeled data.

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Prebuilt entity extraction

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Key phrase extraction

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Sentiment analysis

Question 14

A multinational company needs to automatically translate customer support emails from English to Spanish and French. The emails are plain text. Which Azure service should they use?

Accepted Answer

Azure AI Translator. Azure AI Translator is the correct service because it provides real-time text translation between multiple languages, including English to Spanish and French, via a REST API. It is specifically designed for plain text translation tasks, making it ideal for automatically translating customer support emails without requiring audio processing or conversational AI.

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Azure AI Language (Sentiment Analysis)

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Azure AI Speech

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Azure Bot Service

Question 15

What is 'automated machine learning' (AutoML) in Azure Machine Learning?

Accepted Answer

Automatically iterating through algorithms and hyperparameters to find the best model for a dataset. Automated machine learning (AutoML) in Azure Machine Learning automates the process of selecting the best machine learning algorithm and tuning its hyperparameters for a given dataset. It iterates through multiple combinations of algorithms and hyperparameter values, evaluating each model's performance to identify the optimal solution without manual intervention. This is why option B is correct.

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A system that automatically retrains models on a fixed daily schedule

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Automatically labelling training data using existing model predictions

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A robot that physically connects GPU hardware for distributed training