AI-900 Practice Exam 55 — 20 Questions

Question 1

A logistics company processes packages on an automated conveyor belt. They need to read shipping labels that are often rotated or skewed, and also detect whether a 'FRAGILE' sticker is present on the package. Which combination of Azure Computer Vision capabilities should they use?

Accepted Answer

OCR (Read API) and Object Detection. The scenario requires reading rotated or skewed text from shipping labels (handled by the OCR Read API, which extracts printed and handwritten text from images, even when rotated or skewed) and detecting whether a 'FRAGILE' sticker is present (handled by Object Detection, which identifies and locates specific objects—like stickers—within an image). Option A correctly pairs these two capabilities to meet both requirements.

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Image Classification and OCR (Read API)

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Object Detection and Face Detection

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Image Classification and Face Detection

Question 2

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 3

A marketing team uses Azure OpenAI Service to generate product descriptions. They want the descriptions to follow a specific brand voice (formal, concise) and avoid generating any harmful or offensive language. Which combination of features should the team use?

Accepted Answer

A: Fine-tune the model with brand-specific data and enable content filtering.. Fine-tuning the model with brand-specific data allows the model to learn the desired brand voice (formal, concise) by adjusting its weights based on a curated dataset. Enabling content filtering ensures that any harmful or offensive language is blocked, either by Azure's built-in content moderation or by custom filters, meeting the safety requirement. This combination directly addresses both the style and safety needs.

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B: Use few-shot learning with examples and disable content filtering for creativity.

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C: Increase the temperature parameter and use the logprobs parameter.

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D: Use the top_p parameter and set max_tokens to a low value.

Question 4

A manufacturing company wants to use Azure Computer Vision to automatically inspect products on an assembly line for defects. They need to identify and locate specific types of defects (e.g., scratch, dent, crack) in product images. Which Azure Computer Vision capabilities could be used together to achieve this? (Select two options.)

Accepted Answer

Object Detection. Option A is correct because Object Detection in Azure Computer Vision can identify and locate multiple specific defect types (e.g., scratch, dent, crack) within product images by drawing bounding boxes around each defect. This capability directly meets the requirement to both identify and locate defects on the assembly line.

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Optical Character Recognition (OCR)

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Image Classification

Question 5

What is 'Azure AI Foundry' and what is its primary purpose?

Accepted Answer

A unified enterprise platform for building, evaluating, and deploying AI applications with the full development lifecycle. Azure AI Foundry is a unified enterprise platform that provides an integrated environment for building, evaluating, and deploying AI applications across the full development lifecycle. It combines tools for data preparation, model training, evaluation, and deployment, enabling teams to manage AI projects from ideation to production within a single interface.

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A physical Microsoft facility for AI hardware manufacturing

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A subscription tier that includes all Azure AI services at a fixed monthly price

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A training platform specifically for AI engineers at Microsoft

Question 6

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 7

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

Accepted Answer

An XML markup language for controlling TTS voice characteristics like pitch, rate, pauses, and pronunciation. SSML is an XML-based markup language that allows you to fine-tune text-to-speech (TTS) output by controlling prosodic elements such as pitch, speaking rate, volume, and pronunciation. It also supports inserting pauses, specifying phonetic pronunciations, and adjusting emphasis, making it essential for generating natural-sounding speech in Azure AI Speech.

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A programming language for writing speech recognition algorithms

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A system for transcribing speech in real time to a database

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A security protocol for encrypting speech API calls

Question 8

What is 'chain-of-thought prompting' and when is it most effective?

Accepted Answer

Prompting the model to show explicit reasoning steps before giving a final answer. Chain-of-thought prompting instructs the model to break down a complex problem into intermediate reasoning steps before producing the final answer. This technique improves accuracy on tasks requiring multi-step logic, such as arithmetic, commonsense reasoning, or symbolic manipulation, by making the model's internal reasoning explicit and reducing errors from shortcut answers.

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Linking multiple AI models in a pipeline where each model's output feeds the next

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Training a model on a sequence of related documents to build contextual knowledge

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A method for connecting chatbot conversation turns to maintain long-term memory

Question 9

A transportation company wants to automatically identify whether an image contains a car, a truck, or a motorcycle. The system should output a single label for the entire image. Which computer vision capability in Azure should they use?

Accepted Answer

Image classification. Image classification assigns a single label to an entire image based on its dominant content. Since the requirement is to output one label (car, truck, or motorcycle) per image, this maps directly to Azure's Custom Vision image classification capability, which trains a model to categorize whole images into predefined classes.

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Object detection

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Optical Character Recognition (OCR)

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Semantic segmentation

Question 10

A multinational company receives customer feedback in multiple languages. They need to automatically determine the language of each piece of feedback before routing it to the appropriate support team. Which prebuilt Azure AI Language feature should they use?

Accepted Answer

Language Detection. The Language Detection feature in Azure AI Language is specifically designed to identify the language of a given text input, returning the language name and a confidence score. This directly matches the requirement to automatically determine the language of customer feedback before routing it to the appropriate support team.

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Sentiment Analysis

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Key Phrase Extraction

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Named Entity Recognition (NER)

Question 11

A manufacturing team wants to predict product defects based on sensor readings from the production line. They have 10,000 historical samples, each labeled as 'defective' or 'non-defective'. Which type of machine learning should they use in Azure Machine Learning?

Accepted Answer

Supervised learning. This is a supervised learning problem because the dataset contains labeled historical samples (defective or non-defective), and the goal is to predict a categorical outcome based on sensor readings. In Azure Machine Learning, supervised learning algorithms such as two-class logistic regression or boosted decision trees are used to train a model that maps input features to known labels.

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Unsupervised learning

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Reinforcement learning

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Semi-supervised learning

Question 12

A news agency wants to automatically extract the names of people and organizations mentioned in thousands of articles. They plan to use a prebuilt Azure AI Language feature that requires no custom training. Which feature should they use?

Accepted Answer

Named entity recognition. Named entity recognition (NER) is the correct feature because it is specifically designed to identify and categorize entities such as people, organizations, locations, and dates from unstructured text. The news agency's requirement to extract names of people and organizations from articles matches NER's prebuilt capability, which requires no custom training and is available in Azure AI Language.

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Sentiment analysis

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Key phrase extraction

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Language detection

Question 13

A logistics company uses drone imagery to monitor a busy container yard. They need to count the exact number of individual shipping containers, even when containers are partially stacked on top of each other or overlapping in the image. Which Azure Computer Vision capability should they choose to achieve the most accurate individual object separation?

Accepted Answer

Instance segmentation. Instance segmentation is the correct choice because it not only detects each individual object in an image but also generates a pixel-level mask for each instance, allowing the model to distinguish between overlapping or stacked objects like shipping containers. This capability provides the most accurate separation of individual containers, even when they partially occlude each other, by assigning unique masks to each instance rather than grouping all containers into a single class.

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Image classification

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Object detection

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Semantic segmentation

Question 14

What is the purpose of Azure AI Vision's 'thumbnail generation' feature?

Accepted Answer

Generating crop-focused preview images that highlight the most important content area. Azure AI Vision's thumbnail generation feature analyzes the image content to identify the most important region (e.g., a person's face or a prominent object) and then crops the image around that region to produce a focused preview. This is distinct from simple resizing or compression, as it uses AI-based spatial analysis to preserve the key subject while discarding irrelevant background areas.

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Reducing file sizes of images for faster web page loading

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Creating thumbnail-sized AI model icons for the Azure portal

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Generating multiple image variations in different artistic styles

Question 15

A marketing team wants to automatically analyze thousands of customer reviews to identify the most commonly discussed aspects, such as 'price', 'durability', or 'customer service'. They do not have any labeled data for custom training. Which prebuilt Azure AI Language feature should they use?

Accepted Answer

Key phrase extraction. Key phrase extraction is the correct choice because it automatically identifies the most important points or topics (like 'price', 'durability', 'customer service') from unstructured text without requiring any labeled training data. This prebuilt Azure AI Language feature is designed specifically to surface commonly discussed aspects from large volumes of text, making it ideal for analyzing thousands of customer reviews.

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Sentiment analysis

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Entity recognition

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Language detection

Question 16

What is 'model deployment' in Azure OpenAI, and why are named deployments used?

Accepted Answer

A named instance of an AI model with allocated quota, enabling version control and quota management. Model deployment in Azure OpenAI creates a named instance of a specific model (e.g., GPT-4) with dedicated quota (tokens per minute, rate limits). Named deployments enable version control by pinning to a specific model version (e.g., 0613 vs. 1106) and allow separate quota management per deployment, which is critical for production workloads. This is distinct from simply calling an API endpoint; it provisions a dedicated inference endpoint with guaranteed capacity.

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The process of physically shipping AI hardware to Azure data centers

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Automatically scaling the number of model instances based on traffic

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The initial training step that produces an Azure OpenAI model

Question 17

What is the ethical concern with using AI for 'predictive policing'?

Accepted Answer

Potential to perpetuate racial bias, undermine due process, and create discriminatory self-fulfilling prophecies. Option B is correct because predictive policing AI systems often rely on historical crime data, which can contain inherent biases from over-policing in minority communities. This can lead to a feedback loop where the AI predicts more crime in those areas, prompting more police presence, which in turn generates more arrests and reinforces the original bias. Such systems also risk undermining due process by making decisions based on statistical correlations rather than individual evidence, and can create self-fulfilling prophecies where predicted crime hotspots become actual crime hotspots due to increased enforcement.

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Predictive policing AI is too expensive to implement at city scale

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Predictive policing models are too slow to be useful for real-time decisions

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Predictive policing AI might predict crimes in the wrong ZIP code

Question 18

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 19

A marketing team wants to use Azure AI to automatically generate unique product descriptions for thousands of items in an e-commerce catalog based on a few keywords provided by the inventory team. Which Azure service should they use?

Accepted Answer

A. Azure OpenAI Service. Azure OpenAI Service provides access to large language models (LLMs) like GPT-4, which are specifically designed for generative tasks such as creating unique, human-like text from a few input keywords. This makes it the ideal choice for automatically generating product descriptions at scale, as it can produce varied and contextually relevant content without requiring pre-labeled training data.

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B. Azure Computer Vision

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C. Language Understanding (LUIS)

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D. Azure Machine Learning

Question 20

A manufacturing company wants to use computer vision to inspect products on an assembly line. They need to identify and locate specific types of defects (e.g., scratch, dent, crack) in product images. Which Azure Computer Vision capability should they use?

Accepted Answer

Object Detection. Object Detection is the correct choice because it not only classifies defects (e.g., scratch, dent, crack) but also provides bounding box coordinates to locate each defect within the product image. This meets the requirement to both identify and locate specific defect types on the assembly line.

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Image Classification

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Optical Character Recognition (OCR)

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Face Detection