AI-900 Practice Test 13 — 15 Questions

Question 1

A global e-commerce company receives product reviews in multiple languages. They want to automatically identify the language of each review to route it to the appropriate translation queue. Which Azure AI Language feature should they use?

Accepted Answer

Language Detection. Language Detection is the correct Azure AI Language feature because it is specifically designed to identify the language of a given text input, returning a language name and a confidence score. This directly meets the requirement to automatically detect the language of product reviews so they can be routed to the appropriate translation queue.

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Sentiment Analysis

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Key Phrase Extraction

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Entity Recognition

Question 2

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 3

A manufacturing company uses overhead cameras on an assembly line to check that each part is present in the correct location on a circuit board. The system must not only confirm the part is there but also draw a box around each part to show its exact position. Which Azure Computer Vision capability should they use?

Accepted Answer

Object Detection. Object Detection is the correct capability because it not only identifies whether a specific object (like a circuit board part) is present in an image but also returns bounding box coordinates that indicate the exact location of each detected object. This meets the requirement to both confirm the part's presence and draw a box around it.

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Optical Character Recognition (OCR)

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Image Classification

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Face Detection

Question 4

A hospital uses an AI system to recommend treatment plans for patients. The system's decision process is complex and not easily understood by doctors. The hospital wants to ensure that doctors can trust and verify the system's recommendations. Which Microsoft responsible AI principle is most directly relevant?

Accepted Answer

Transparency. The scenario describes a complex AI decision process that doctors cannot easily understand, which directly relates to the need for interpretability and openness about how the system works. Transparency is the Microsoft responsible AI principle that focuses on making AI systems understandable and providing clear documentation, so users can verify and trust the outputs. By ensuring transparency, the hospital can enable doctors to audit the reasoning behind treatment recommendations, fostering trust and accountability.

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Reliability and safety

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Fairness

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Privacy and security

Question 5

A hospital receives patient notes in free text. They need to automatically identify entities like disease names, medications, and dosages from these notes without requiring any custom training. Which Azure AI Language feature is specifically designed for this medical entity extraction task?

Accepted Answer

Text Analytics for Health. Text Analytics for Health is a pre-built Azure AI Language feature specifically designed to extract medical entities such as diseases, medications, dosages, symptoms, and procedures from unstructured clinical text without requiring any custom training. It uses specialized medical ontologies (e.g., UMLS, SNOMED CT) and natural language processing models trained on healthcare data, making it the correct choice for this task.

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Custom Named Entity Recognition

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Key Phrase Extraction

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Sentiment Analysis

Question 6

A hospital deploys an AI system to predict patient readmission risk using historical health records. To protect patient privacy, the hospital wants to ensure that individual patients cannot be identified from the data used for training. Which responsible AI principle is most directly relevant to this requirement?

Accepted Answer

Privacy and security. The requirement to prevent individual patient identification from training data directly aligns with the privacy and security principle, which mandates data anonymization, de-identification, and access controls. In AI systems, this is implemented through techniques like differential privacy (adding noise to data) or k-anonymity to ensure that outputs cannot be re-identified. The hospital's goal is to protect patient confidentiality, which is the core focus of this principle.

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Fairness

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Reliability and safety

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Inclusiveness

Question 7

A legal research firm uses Azure OpenAI Service to answer questions about specific case law documents. They want the model to base its answers exclusively on the content of the provided documents, without using any external knowledge from its training. Which approach should they use?

Accepted Answer

Use the 'Azure OpenAI on your data' feature with a 'Search' data source containing the documents. Option C is correct because the 'Azure OpenAI on your data' feature with a 'Search' data source allows the model to retrieve and ground its answers exclusively on the content of the provided documents. This approach uses a search index (e.g., Azure Cognitive Search) to fetch relevant document chunks and inject them into the prompt, ensuring the model does not rely on its pre-trained knowledge. It is the only method that enforces strict document-based grounding without external knowledge leakage.

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Increase the 'temperature' parameter to 0.0

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Use the system message to instruct the model to only use provided documents

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Set the 'max_tokens' parameter to a low value

Question 8

A law firm needs to automatically categorize documents (e.g., 'contract', 'pleading', 'memo') and extract specific clauses such as 'indemnity' and 'confidentiality'. They have a large set of labeled examples for both tasks. Which combination of Azure AI Language features should they use?

Accepted Answer

Custom text classification and custom named entity recognition. Option B is correct because the law firm needs to categorize documents (a text classification task) and extract specific clauses (a named entity recognition task). Custom text classification allows training a model on labeled examples to classify documents into categories like 'contract' or 'pleading', while custom named entity recognition (NER) can be trained to extract domain-specific entities such as 'indemnity' and 'confidentiality' clauses from the text. Azure AI Language supports both custom features, enabling the firm to build tailored models using their labeled dataset.

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Prebuilt sentiment analysis and key phrase extraction

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Question answering and conversation summarization

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Language detection and translation

Question 9

A developer uses Azure OpenAI Service to generate product name suggestions. They want to ensure the model never outputs a specific word, such as 'Corporation', because it is too formal for their brand. Which parameter should the developer configure to reduce the probability of that token being generated?

Accepted Answer

Logit Bias. Logit Bias is the correct parameter because it directly modifies the logits (raw prediction scores) for specific tokens before the softmax function, allowing the developer to reduce the probability of generating a particular token like 'Corporation'. By setting a negative bias value for that token's ID, the model is less likely to output it, even if it would otherwise be a high-probability choice. This is the only parameter that provides token-level control over output content.

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Temperature

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Top P (Nucleus Sampling)

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Frequency Penalty

Question 10

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?

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 11

A law firm wants to automatically categorize incoming legal documents into custom categories such as 'Motion', 'Contract', 'Discovery', and 'Memorandum'. The firm has a set of manually labeled documents that can be used to train the system. Which Azure AI Language feature should they use?

Accepted Answer

Custom text classification. The law firm needs to categorize documents into custom categories using their own labeled data. Custom text classification in Azure AI Language is specifically designed for this purpose, allowing you to train a model on your own labeled documents to classify text into user-defined categories. Prebuilt Text Analytics for sentiment only detects sentiment (positive/negative/neutral), not custom categories.

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Prebuilt Text Analytics for sentiment

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Conversational Language Understanding

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Key phrase extraction

Question 12

A marketing team uses Azure OpenAI to generate social media posts. They want to ensure the generated text maintains a consistent, predictable brand voice without being overly creative or random. Which parameter should they primarily adjust to control the randomness of the output?

Accepted Answer

Temperature. Temperature controls the randomness of token selection by scaling the logits before applying the softmax function. A lower temperature (e.g., 0.2) makes the model more deterministic and conservative, producing outputs that stick closely to the most likely tokens—ideal for maintaining a consistent, predictable brand voice. Higher temperatures increase randomness, which the team wants to avoid.

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Max tokens

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Frequency penalty

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Top P

Question 13

A manufacturing company uses Azure Computer Vision to analyze assembly line images. They need to identify specific product defects (e.g., scratches, dents) and also read serial numbers printed on the products in various fonts. Which combination of Azure Computer Vision features should they use?

Accepted Answer

Custom Vision (object detection) and OCR. Option B is correct because the scenario requires two distinct capabilities: identifying specific defect types (scratches, dents) and reading variable-font serial numbers. Custom Vision's object detection model can be trained on labeled defect images to recognize those specific patterns, while Azure's OCR (part of Computer Vision's Read API) extracts printed text regardless of font. Combining these two features directly addresses both requirements.

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Image Analysis (object detection) and OCR

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Face API and OCR

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Image Analysis (tags) and OCR

Question 14

A marketing team uses Azure OpenAI Service to generate taglines for a new advertising campaign. They want the output to be more predictable and less surprising, sticking to the most common phrases and avoiding unusual combinations. Which parameter should they decrease?

Accepted Answer

Temperature. Temperature controls the randomness of the model's output. Lowering the temperature (e.g., from 1.0 to 0.2) makes the model more deterministic, favoring high-probability tokens and common phrases, which reduces surprise and unusual combinations. This directly aligns with the team's goal of predictable, conservative taglines.

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Top P

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Frequency penalty

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Presence penalty

Question 15

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). The correct answer is B, 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