AI-900 Practice Test 50 — 15 Questions

Question 1

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 2

A global e-commerce company receives customer support emails in over 30 languages. They want to automatically route each email to the correct department (Billing, Technical Support, or Returns) and also extract the order number and the product name mentioned in the email. The solution must be able to identify the language of each email before further processing. Which combination of Azure AI Language features should they use?

Accepted Answer

Language Detection, Custom Text Classification, and Entity Recognition. Option A is correct because the scenario requires three distinct capabilities: Language Detection to identify the email's language (a prerequisite for further processing), Custom Text Classification to route emails to the correct department (Billing, Technical Support, or Returns), and Entity Recognition to extract the order number and product name. Azure AI Language provides these as separate, combinable features that directly map to the stated requirements.

Answer

Language Detection, Key Phrase Extraction, and Sentiment Analysis

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Entity Recognition, Sentiment Analysis, and Key Phrase Extraction

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Custom Text Classification, Key Phrase Extraction, and Sentiment Analysis

Question 3

A legal firm needs to automatically extract key information from contracts, including the names of parties involved, important dates, and monetary amounts. Which Azure AI Language feature should they use to identify and extract these specific pieces of information from the text?

Accepted Answer

Named Entity Recognition (NER). Named Entity Recognition (NER) is the correct Azure AI Language feature because it is specifically designed to identify and categorize entities such as people (parties involved), dates, and monetary amounts from unstructured text. This directly matches the legal firm's requirement to extract key information from contracts.

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Sentiment analysis

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Key phrase extraction

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Language detection

Question 4

A data scientist trains a regression model on a dataset with 100 features and 10,000 samples. The model achieves a low training error but a much higher error on a held-out test set. Which approach is most likely to improve the model's generalization performance?

Accepted Answer

Reduce the number of features or apply regularization. The model exhibits high variance (overfitting), as indicated by low training error but high test error. Reducing the number of features or applying regularization (e.g., L1/L2) directly constrains model complexity, forcing it to learn more general patterns rather than memorizing noise. This is the standard approach to improve generalization in regression models.

Answer

Increase the complexity of the model by adding more layers or parameters

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Add more training data

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Use a different train-test split ratio like 80-20 instead of 70-30

Question 5

A law firm receives hundreds of legal documents daily. They need to automatically extract key entities like names of parties, dates, jurisdictions, and also classify each document as 'contract', 'pleading', or 'memo'. Which combination of Azure AI Language features should they use?

Accepted Answer

Entity recognition and custom text classification. Option B is correct because the law firm needs both entity extraction (to identify parties, dates, jurisdictions) and document classification (contract, pleading, memo). Azure AI Language's prebuilt entity recognition handles the entity extraction, while custom text classification allows the firm to train a model to classify documents into their specific categories. This combination directly addresses both requirements without unnecessary features.

Answer

Entity recognition and key phrase extraction

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Sentiment analysis and language detection

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Summarization and conversation analysis

Question 6

A healthcare research organization publishes an AI system that diagnoses skin conditions from images. In a study, they discover that the model's accuracy is significantly lower for people with darker skin tones compared to those with lighter skin tones. According to Microsoft's Responsible AI principles, which principle most directly requires the organization to disclose this limitation in their documentation?

Accepted Answer

Transparency. The Transparency principle requires AI systems to be understandable and for their limitations to be clearly communicated. In this scenario, the organization must disclose the model's lower accuracy for darker skin tones because users and clinicians need to know when the system is less reliable to make informed decisions. Without this disclosure, the system could be misused or trusted inappropriately, violating the core tenet of transparency.

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Fairness

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Accountability

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Privacy and Security

Question 7

A legal firm wants to use Azure OpenAI to generate summaries of lengthy contracts. The firm requires that the generated summaries are strictly based on the provided contract text and do not include any external knowledge or hallucinated facts. Which Azure OpenAI feature should the firm configure to meet this requirement?

Accepted Answer

Azure OpenAI on Your Data (data grounding). Option A is correct because Azure OpenAI on Your Data (data grounding) restricts the model's responses to the content of the provided contract documents, preventing the generation of information not present in the source text. This feature uses a retrieval-augmented generation (RAG) approach, where the model only references the indexed contract data, effectively eliminating external knowledge or hallucinated facts.

Answer

Content filtering

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Prompt engineering with system messages

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Fine-tuning the model on legal texts

Question 8

What is the Azure Machine Learning workspace?

Accepted Answer

The top-level Azure ML resource that organizes experiments, models, compute, and deployments. The Azure Machine Learning workspace is the top-level resource in Azure that serves as a centralized hub for managing all machine learning activities. It organizes experiments, models, compute targets, and deployments, providing a unified environment for the entire ML lifecycle. This is the correct answer because the workspace is the foundational resource that ties together all other Azure ML components.

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A web-based IDE for writing machine learning code in Python

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A virtual machine pre-configured with ML tools and libraries

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A dedicated GPU cluster for distributed deep learning training

Question 9

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.

Answer

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 10

What is 'grounding with Bing search' in Microsoft Copilot?

Accepted Answer

Retrieving current web information from Bing to augment LLM responses beyond its training cutoff. Grounding with Bing search in Microsoft Copilot refers to the technique of retrieving real-time, current web information from Bing to augment the responses of a large language model (LLM) beyond its static training cutoff date. This allows Copilot to provide up-to-date answers on recent events, data, or topics not present in the model's original training corpus, effectively grounding the AI's output in verifiable, live web content.

Answer

Using Bing Maps to provide location-based responses

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Translating Copilot responses using Microsoft's Bing Translator

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Using Bing advertising data to personalize AI responses

Question 11

What does it mean to 'export' a model from Azure AI Custom Vision?

Accepted Answer

Downloading the trained model as a file for offline inference on edge devices. Exporting a model from Azure AI Custom Vision means downloading the trained model as a file (e.g., TensorFlow, ONNX, CoreML, or Docker container) so it can be run locally on edge devices without requiring an internet connection to the cloud API. This enables offline inference, reduced latency, and data privacy for scenarios like manufacturing or retail.

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Sharing the model configuration with other Azure subscriptions

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Moving the model from Custom Vision to Azure Machine Learning

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Submitting the model for Microsoft certification review

Question 12

A financial institution uses an AI system to recommend credit limits for new customers. When a customer is declined for a credit limit increase, the customer asks why, but the institution cannot provide any explanation because the model is a complex deep neural network and the decision-making process is opaque. Which Microsoft responsible AI principle is most directly violated?

Accepted Answer

Transparency. The correct answer is A, Transparency. The scenario describes a deep neural network that cannot explain its decision to deny a credit limit increase, which directly violates the transparency principle. Microsoft's responsible AI principle of transparency requires that AI systems be understandable and that their decisions can be explained to users, especially when those decisions have significant impact on individuals.

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Reliability and safety

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Fairness

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Privacy and security

Question 13

What does Azure Machine Learning's 'compute cluster' provide?

Accepted Answer

Scalable, auto-scaling cloud compute for running ML training jobs that scales to zero when idle. Azure Machine Learning's compute cluster provides a scalable, auto-scaling cloud compute environment specifically designed for running ML training jobs. It automatically scales up to handle large workloads and scales down to zero nodes when idle, optimizing cost and resource utilization.

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A Kubernetes cluster for deploying trained models as REST APIs

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A data storage cluster for distributing training datasets across nodes

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A network of IoT sensors for collecting training data

Question 14

What is 'abstractive summarization' vs. 'extractive summarization' in Azure AI Language, and which produces summaries in new words?

Accepted Answer

Abstractive generates new sentences; extractive selects existing sentences from the source. Abstractive summarization generates new sentences that rephrase the core meaning of the source text, similar to how a human would summarize. Extractive summarization, in contrast, selects and copies key sentences directly from the original document without rewording them. Option B correctly identifies that abstractive produces new sentences while extractive selects existing ones.

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Extractive produces new words; abstractive copies sentences

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They produce identical output through different computational paths

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Abstractive works only for legal documents; extractive for general text

Question 15

A hotel chain wants to automatically analyze guest reviews to identify the most frequently mentioned aspects of their stay, such as 'cleanliness', 'staff friendliness', or 'location'. They want to use a prebuilt Azure AI Language feature without custom training. Which feature should they use?

Accepted Answer

Key Phrase Extraction. Key Phrase Extraction is the correct choice because it is a prebuilt Azure AI Language feature designed to automatically identify and return the most salient words or phrases from unstructured text, such as 'cleanliness', 'staff friendliness', or 'location'. This feature requires no custom training and directly addresses the hotel chain's need to surface frequently mentioned aspects of guest reviews.

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Sentiment Analysis

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Entity Recognition

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Language Detection