AI-900 Practice Test 45 — 15 Questions

Question 1

A data scientist is training a classification model on a dataset with 100 features and only 500 labeled samples. The model achieves 99% accuracy on the training data but only 68% accuracy on a held-out test set, indicating overfitting. Which technique is most appropriate to directly address this problem?

Accepted Answer

Reduce the number of features used for training. Option B is correct because reducing the number of features directly combats overfitting by decreasing model complexity and the risk of learning noise from irrelevant or redundant features. With only 500 samples and 100 features, the model has a high variance problem; feature selection or dimensionality reduction (e.g., using Azure Machine Learning's Filter-Based Feature Selection or PCA) simplifies the hypothesis space, improving generalization to the test set.

Answer

Increase the amount of training data by collecting more samples

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Increase the complexity of the model by adding more layers

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Train for more epochs

Question 2

What is the purpose of Azure AI Speech's 'batch transcription' capability?

Accepted Answer

Asynchronous processing of large volumes of audio files for cost-efficient transcription at scale. Azure AI Speech's batch transcription is designed for asynchronous processing of large volumes of pre-recorded audio files. It allows you to submit multiple audio files for transcription without requiring real-time interaction, making it cost-efficient for scenarios like call center analytics or media captioning where immediate results are not needed.

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Real-time transcription of live audio streams for immediate use

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Synchronizing speech transcription across multiple languages simultaneously

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Training a custom speech recognition model on audio samples

Question 3

What is 'responsible AI by design' in the context of building Azure AI applications?

Accepted Answer

Integrating ethical AI principles and safety tools throughout the entire development lifecycle. Option B is correct because 'responsible AI by design' means proactively embedding ethical principles—such as fairness, reliability, transparency, privacy, and accountability—into every phase of building an Azure AI application, from problem definition and data collection to deployment and monitoring. This approach aligns with Microsoft's Responsible AI Standard and is operationalized through tools like Fairlearn, Error Analysis, and the Responsible AI dashboard in Azure Machine Learning, ensuring that safety and ethical considerations are not afterthoughts but integral to the development lifecycle.

Answer

Using only Azure-approved AI models to avoid legal liability

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Designing AI systems that only respond to pre-approved questions

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Requiring legal review before every AI model deployment

Question 4

A data scientist trains a multiclass classification model to identify different species of flowers (Iris setosa, Iris virginica, Iris versicolor). The overall accuracy is 94%, but the accuracy for the Iris virginica class is only 60%. Which additional metric should the data scientist examine to better understand the model's performance on the minority class?

Accepted Answer

F1-score. The F1-score is the harmonic mean of precision and recall, providing a single metric that balances both false positives and false negatives. Since the model has high overall accuracy but poor performance on the minority class (Iris virginica), the F1-score is ideal for evaluating the model's effectiveness on that class, as it accounts for class imbalance better than accuracy alone.

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Precision

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Recall

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Mean Absolute Error (MAE)

Question 5

What does Azure AI Speech's 'custom neural voice' capability allow organizations to do?

Accepted Answer

Create a unique branded synthetic voice trained on recordings of a specific speaker. Azure AI Speech's 'custom neural voice' capability allows organizations to create a unique, branded synthetic voice by training a neural text-to-speech model on recordings of a specific speaker. This enables high-quality, natural-sounding voice personalization for applications like virtual assistants, audiobooks, and customer service bots, while requiring explicit speaker consent and adherence to responsible AI guidelines.

Answer

Train speech recognition to understand unique industry vocabulary

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Translate speech from one language to another in real time

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Automatically identify accents and dialects in speech

Question 6

What is the purpose of the Azure AI Document Intelligence's prebuilt models?

Accepted Answer

Extracting structured data from common document types (invoices, receipts, IDs) without custom training. Azure AI Document Intelligence's prebuilt models are designed to extract structured data from common document types such as invoices, receipts, and IDs without requiring any custom training. They leverage pre-trained neural networks that recognize fields like invoice totals, receipt line items, and ID numbers, enabling rapid data extraction for standard forms. This aligns with the purpose of reducing manual data entry and accelerating document processing workflows.

Answer

Training custom document extraction models for unique business forms

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Translating documents from one language to another

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Converting documents to PDF format for archiving

Question 7

A data scientist trains a binary classification model to detect spam emails. The dataset contains 95% legitimate emails (negative class) and 5% spam (positive class). The model predicts all emails as legitimate. The accuracy is 95%, but the model is useless. Which metric would best indicate the model's failure?

Accepted Answer

Recall. Recall (sensitivity) measures the proportion of actual positive cases correctly identified. With 5% spam and the model predicting all as legitimate, recall is 0% because no spam emails are detected. This directly exposes the model's failure to identify the positive class despite high accuracy.

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Precision

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F1 score

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Specificity

Question 8

What does Azure AI Language's 'text analytics for health' feature provide?

Accepted Answer

Extracting structured clinical information from unstructured medical text. Azure AI Language's 'text analytics for health' feature is designed to extract structured clinical information—such as diagnoses, medications, symptoms, and procedures—from unstructured medical text like clinical notes, discharge summaries, and radiology reports. It uses specialized NLP models trained on medical ontologies (e.g., UMLS, ICD-10-CM) to identify and normalize healthcare entities, enabling downstream analytics and decision support.

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Real-time patient health monitoring from IoT medical devices

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Providing medical advice and treatment recommendations

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Monitoring the health status of Azure AI services

Question 9

A data scientist is training a model to predict whether a customer will purchase a product (Yes/No). The dataset contains 90% 'No' and 10% 'Yes'. After training, the model achieves 90% accuracy. Which evaluation metric would be more informative to assess the model's performance on the minority class?

Accepted Answer

F1 score. In this imbalanced dataset (90% 'No', 10% 'Yes'), a model that always predicts 'No' would achieve 90% accuracy, making accuracy a misleading metric. The F1 score is the harmonic mean of precision and recall, specifically designed to evaluate a model's performance on the minority class by balancing false positives and false negatives. It is the most informative metric here because it directly measures how well the model identifies the rare 'Yes' purchases without being inflated by the majority class.

Answer

Mean Absolute Error (MAE)

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Area Under the Curve (AUC)

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R-squared

Question 10

A data scientist is preparing a dataset to train a model that predicts customer churn. The dataset includes a column 'CustomerID' which is a unique identifier for each customer. Should the data scientist include the 'CustomerID' column as a feature in the training data?

Accepted Answer

No, because the CustomerID is a random unique identifier with no predictive power for churn.. Option B is correct because CustomerID is a unique identifier that does not contain any meaningful pattern or relationship with the target variable (churn). Including such a column would introduce noise and risk overfitting, as the model could memorize each ID rather than learning generalizable patterns. In Azure Machine Learning, features should be predictive attributes, not arbitrary labels.

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Yes, because it uniquely identifies each customer and helps the model differentiate them.

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Yes, because the model can learn patterns from the numeric values.

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No, because the CustomerID column contains too many missing values.

Question 11

A hospital has a dataset with historical patient records, each labeled as either 'readmitted within 30 days' or 'not readmitted'. The hospital wants to train a model to predict which current patients are likely to be readmitted. Which type of machine learning task is this?

Accepted Answer

Supervised classification. This is a supervised classification task because the dataset contains labeled historical patient records (readmitted or not readmitted), and the goal is to predict a discrete category (binary outcome) for new patients. In Azure Machine Learning, this would use a classification algorithm like logistic regression or decision tree to assign each patient to one of the two classes.

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Supervised regression

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Unsupervised clustering

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Reinforcement learning

Question 12

What is 'sentiment analysis' at the opinion mining level vs. document level in Azure AI Language?

Accepted Answer

Document-level assigns one overall sentiment; opinion mining extracts per-aspect sentiments. Option B is correct because in Azure AI Language, document-level sentiment analysis assigns a single overall sentiment (positive, negative, neutral, or mixed) to the entire document, while opinion mining (a feature of aspect-based sentiment analysis) extracts sentiments for specific aspects or targets within the text, such as 'service' or 'food' in a restaurant review. This allows for granular, per-aspect sentiment detection rather than a single aggregate score.

Answer

Document-level is more accurate; opinion mining is a faster but less precise approximation

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Opinion mining works only on social media posts; document-level works on all text types

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Document-level sentiment requires training data; opinion mining is pre-built

Question 13

A data scientist is developing a classification model to detect fraudulent transactions. The dataset is split into training and test sets. The data scientist repeatedly tunes the model's hyperparameters and evaluates performance on the test set until the test accuracy reaches 95%. However, when the model is deployed on new, unseen data, its accuracy drops to 70%. Which concept best explains this performance degradation?

Accepted Answer

Overfitting to the test set. Option C is correct because the data scientist repeatedly tuned hyperparameters based on test set performance, effectively using the test set as part of the training process. This causes the model to become specialized to the test set's specific patterns and noise, so it fails to generalize to new, unseen data. This phenomenon is known as overfitting to the test set, where the test set no longer provides an unbiased estimate of real-world performance.

Answer

Overfitting to the training data

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Data leakage from the training set to the test set

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Underfitting the training data

Question 14

A digital art library wants to automatically generate a list of relevant keywords (e.g., 'landscape', 'portrait', 'abstract', 'nature') for each image in their collection. Which Azure Computer Vision capability should they use?

Accepted Answer

Image Tagging. Image Tagging (B) is the correct capability because it analyzes the content of an image and returns a set of relevant keywords (tags) based on the detected objects, scenes, and concepts. This directly matches the requirement to generate a list of keywords like 'landscape', 'portrait', 'abstract', and 'nature' for each image.

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Optical Character Recognition (OCR)

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Image Captioning

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Object Detection

Question 15

A government agency needs to digitize thousands of handwritten application forms so that the text can be searched and processed. Which Azure Computer Vision capability should they use?

Accepted Answer

Optical Character Recognition (Read API). The correct answer is B, Optical Character Recognition (Read API), because the agency needs to extract printed or handwritten text from images of application forms and make it searchable and processable. The Read API is specifically designed for this purpose, handling both printed and handwritten text, and is part of Azure Computer Vision's OCR capabilities.

Answer

Object detection

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Image classification

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Face detection