AI-900 Practice Test 46 — 15 Questions

Question 1

A global news organization receives articles in multiple languages. They need to first identify the language of each article, then translate it into English. Which combination of prebuilt Azure AI services should they use?

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

Accepted Answer

Language Detection (Azure AI Language) and Translator (Azure AI Translator). The scenario requires two distinct steps: first, identifying the language of each article, which is performed by the Language Detection API in Azure AI Language; second, translating the article into English, which is handled by the Azure AI Translator service. These two prebuilt services are designed to work together seamlessly for multilingual content processing.

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Key Phrase Extraction and Sentiment Analysis

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Entity Recognition and Translator

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Language Detection and Text Analytics for Health

Question 2

What is 'Azure OpenAI's Assistants API' and what capabilities does it add?

Accepted Answer

A stateful API enabling AI assistants with persistent threads, tool use, and file handling. The Assistants API is a stateful API that manages persistent threads, supports tool use (e.g., code interpreter, file search), and handles file attachments, enabling multi-turn, context-aware AI assistants. This goes beyond simple completions by maintaining conversation state and integrating external tools, which is a core generative AI workload capability on Azure.

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An API for hiring human assistants to review and approve AI model outputs

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An API for building traditional rule-based chatbots without language model capabilities

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A simplified interface for generating single-turn completions without conversation history

Question 3

What is 'model compression' and what techniques does it include?

Accepted Answer

Reducing model size through pruning, quantisation, distillation, and factorisation for efficient deployment. Model compression is a set of techniques used to reduce the size of a trained machine learning model while preserving its accuracy as much as possible. This is critical for deploying models on resource-constrained devices like edge devices or mobile phones. The key techniques include pruning (removing unnecessary weights), quantization (reducing the precision of weights, e.g., from 32-bit floats to 8-bit integers), distillation (training a smaller 'student' model to mimic a larger 'teacher' model), and factorization (decomposing large weight matrices into smaller ones). Option B correctly lists these four core techniques.

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Compressing training data files to reduce storage costs

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Summarising model documentation into a shorter model card format

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Packaging model code and dependencies into a container image for deployment

Question 4

What is the Azure AI Face service's 'face verification' capability?

Accepted Answer

Comparing two facial images to determine if they belong to the same person. Azure AI Face service's 'face verification' capability is designed to compare two facial images and determine if they belong to the same person. It returns a confidence score and a boolean result indicating whether the faces match, based on a user-defined threshold. This is distinct from identification, which matches against a larger database.

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Confirming that detected faces belong to humans and not artificial representations

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Verifying that facial recognition results meet accuracy requirements

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Confirming the identity of a known person against a database of millions

Question 5

What is 'guardrails' in generative AI applications and how are they implemented?

Accepted Answer

Safety and quality constraints (content filters, system prompts, output validation) preventing harmful AI outputs. Guardrails in generative AI applications are safety and quality constraints implemented to prevent harmful or inappropriate AI outputs. They include content filters that block offensive language, system prompts that steer model behavior, and output validation that checks responses against predefined policies. This is correct because guardrails are a core feature of responsible AI deployment, ensuring that generative models like GPT-4 in Azure OpenAI Service produce safe, compliant, and contextually appropriate content.

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Physical barriers around AI data centres to prevent unauthorised access

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Legal terms of service that constrain how developers can use Azure OpenAI

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Rate limits that prevent individual users from generating too many responses

Question 6

What is 'conversation history' and why is it important in multi-turn chatbot interactions?

Accepted Answer

The sequence of prior messages included in the prompt so the model can maintain context across turns. Conversation history is the sequence of prior messages included in the prompt to a language model, allowing it to maintain context across multiple turns in a dialogue. This is critical because the model itself has no inherent memory; without the history, each turn would be treated as an isolated query, breaking the flow of a multi-turn interaction. By appending previous user inputs and assistant responses to the prompt, the model can reference earlier statements and provide coherent, context-aware replies.

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A database log of all chatbot conversations for compliance auditing

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A summary of frequently asked questions generated from past user interactions

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The total number of messages a user has sent to a chatbot over their lifetime

Question 7

A data scientist has a dataset containing customer transaction records with features such as age, income, and purchase history, but no labels. The goal is to identify natural groupings of customers for a targeted marketing campaign. Which type of machine learning should be used?

Accepted Answer

Clustering. Clustering is the correct choice because the dataset has no labels, and the goal is to discover natural groupings of customers based on feature similarity. Unsupervised learning algorithms like K-Means or DBSCAN partition data into clusters where intra-cluster similarity is high and inter-cluster similarity is low, enabling targeted marketing without pre-existing categories.

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Classification

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Regression

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Reinforcement learning

Question 8

A company wants to build a chatbot that answers customer questions using only their internal knowledge base, which consists of several PDFs and Word documents. They do not want the chatbot to use any information from the model's pre-trained knowledge. Which Azure OpenAI feature should they use to achieve this?

Accepted Answer

Azure OpenAI on your data. Azure OpenAI on your data allows you to connect Azure OpenAI models to your own data sources (such as PDFs and Word documents) and restrict the model to generate responses solely from that data, without using the model's pre-trained knowledge. This is achieved by indexing the documents into an Azure Cognitive Search index and using retrieval-augmented generation (RAG) to ground the model's responses in your specific content.

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Content filtering

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Prompt flow

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Temperature parameter

Question 9

A company deploys an AI system to screen job resumes and rank candidates. The company wants to ensure that candidates can understand how the system arrived at its decisions. Which Microsoft responsible AI principle is most directly addressed by this requirement?

Accepted Answer

Transparency. The requirement that candidates can understand how the AI system arrived at its decisions directly aligns with the Transparency principle, which mandates that AI systems be interpretable and that their decision-making processes be explainable to users. In the context of resume screening, this means providing clear reasoning for why a candidate was ranked a certain way, such as highlighting which features (e.g., skills, experience) most influenced the score.

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Fairness

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Reliability and Safety

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Privacy and Security

Question 10

A data scientist trains a classification model to distinguish between images of cats and dogs. The model achieves 99% accuracy on the training set but only 75% accuracy on a validation set. Which concept best describes this situation?

Accepted Answer

Overfitting. The model performs exceptionally well on the training data (99% accuracy) but significantly worse on unseen validation data (75% accuracy). This gap indicates the model has memorized noise and specific patterns in the training set rather than learning generalizable features, which is the classic definition of overfitting.

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Underfitting

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Model bias

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Data leakage

Question 11

A beverage company uses a camera system to inspect bottles on a conveyor belt. The system must automatically identify which bottles are defective (e.g., cracked or chipped) and which are acceptable, based on the overall appearance of each bottle. The company has thousands of labeled images of bottles (defective and non-defective). Which Azure Computer Vision service should they use to train a custom model?

Accepted Answer

Custom Vision – Image classification. The scenario requires classifying each bottle image into one of two categories (defective or acceptable) based on overall appearance. Custom Vision – Image classification is designed exactly for this: it trains a model on labeled images to predict a single label per image, making it ideal for binary or multi-class classification tasks like defect detection.

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Custom Vision – Object detection

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Optical Character Recognition (OCR)

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Face API

Question 12

What is 'Azure Cognitive Services' and how does it relate to Azure AI Services?

Accepted Answer

Microsoft's family of pre-built AI APIs (now rebranded as Azure AI Services) for vision, speech, and language. Azure Cognitive Services is the original name for Microsoft's family of pre-built AI APIs that provide capabilities in vision, speech, language, and decision-making. These APIs have been rebranded as Azure AI Services, making option B correct because it accurately describes the service as pre-built AI APIs for vision, speech, and language, and correctly notes the rebranding.

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A set of tools for cognitive psychology research at Microsoft Research

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Services that simulate human cognitive functions like memory and problem-solving in robots

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A premium Azure support tier that provides AI specialists to help with complex deployments

Question 13

A retail company wants to automatically group customers into segments based on their purchasing history, age, and location without using any predefined labels. The goal is to identify distinct customer profiles for targeted marketing campaigns. Which type of machine learning approach should they use?

Accepted Answer

Unsupervised learning. Unsupervised learning is the correct approach because the company wants to group customers into segments without predefined labels. The algorithm will discover natural patterns and clusters in the data (purchasing history, age, location) on its own, which is the core characteristic of unsupervised learning.

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Supervised learning

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Reinforcement learning

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Regression

Question 14

What is the purpose of 'entities' in conversational language understanding (CLU) models?

Accepted Answer

To extract specific parameter values from user utterances needed to fulfill an intent. Entities in CLU models are designed to extract specific pieces of information (parameter values) from user utterances, such as dates, locations, or product names, which are necessary to fulfill the user's intent. For example, in the utterance 'Book a flight to Seattle on June 5th,' the intent is 'BookFlight,' and entities extract 'Seattle' (destination) and 'June 5th' (date). This directly supports the intent by providing the required parameters for downstream actions.

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To determine the overall topic or domain of the conversation

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To classify how confident the model is in its intent prediction

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To define the fallback response when no intent is recognized

Question 15

A developer wants to use Azure OpenAI to generate text that follows a specific style, such as formal business letters. They provide three examples of the desired output format in the prompt and then ask the model to generate a new letter. Which technique is the developer using?

Accepted Answer

Few-shot learning. The developer is using few-shot learning, a technique where a prompt includes several examples (in this case, three formal business letters) to guide the model's output style and format without updating the model's weights. This approach leverages the model's in-context learning ability to generalize from the provided examples, making it ideal for tasks requiring specific stylistic adherence.

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Zero-shot learning

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Fine-tuning

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Temperature scaling