AI-900 Practice Test 14 — 15 Questions

Question 1

Drag and drop the steps to analyze an image with Azure Computer Vision into the correct order.

Accepted Answer

Create an Azure Computer Vision resource, then obtain the endpoint and key, then call the API with the image and desired features, then parse the JSON response.. Image analysis requires resource setup, API call with features, and understanding the response.

Answer

Obtain the endpoint and key, then call the API, then create the resource, then parse the response.

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Call the API with the image, then parse the response, then create the resource, then obtain endpoint and key.

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Create the resource, then call the API, then obtain endpoint and key, then parse the response.

Question 2

A retail store uses ceiling-mounted cameras to analyze customer traffic flow. They need to detect when a person enters a specific aisle and determine the direction they are walking. Which Azure Computer Vision capability should they use?

Accepted Answer

People counting (Spatial Analysis). Spatial Analysis, part of Azure Computer Vision, uses ceiling-mounted cameras to track people's movement and direction in a physical space. It specifically provides people counting and trajectory analysis, making it ideal for detecting when a person enters an aisle and determining their walking direction.

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Image Analysis dense captioning

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Facial recognition

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Optical Character Recognition (OCR)

Question 3

Drag and drop the steps to create a knowledge base in QnA Maker (now Language service) into the correct order.

Accepted Answer

Create a QnA Maker resource, then create a knowledge base and add QnA pairs, then test the knowledge base, then publish the knowledge base.. Creating a QnA knowledge base involves setting up the resource, adding QnAs, testing, and publishing.

Answer

Create a QnA Maker resource, then test the knowledge base, then create a knowledge base and add QnA pairs, then publish the knowledge base.

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Create a knowledge base and add QnA pairs, then create a QnA Maker resource, then test the knowledge base, then publish the knowledge base.

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Create a QnA Maker resource, then create a knowledge base and add QnA pairs, then publish the knowledge base, then test the knowledge base.

Question 4

Match each Azure AI service to its associated API or SDK.

Accepted Answer

Azure Computer Vision: Computer Vision API. The correct matches are: Azure Computer Vision with Computer Vision API, Azure Face Service with Face API, Azure Speech Service with Speech-to-Text API, and Azure Translator Text with Translator Text API. Common confusions include mixing up services with similar capabilities, such as attributing the Face API to Computer Vision or the Speech-to-Text API to Translator Text.

Answer

Azure Computer Vision: Face API

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Azure Translator Text: Speech-to-Text API

Question 5

Match each Azure AI concept to its definition.

Accepted Answer

Anomaly Detector: Identifies unusual patterns or outliers in data.. The correct matches are: Anomaly Detector detects unusual patterns, Computer Vision analyzes visual data, NLP processes human language, and Speech Services handles audio conversion. The distractors swap definitions between Anomaly Detector and Computer Vision.

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Anomaly Detector: Extracts information from images, videos, and other visual inputs.

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Computer Vision: Identifies unusual patterns or outliers in data.

Question 6

A data scientist is training a regression model to predict house prices. The data scientist wants to evaluate the model using a metric that penalizes large prediction errors significantly more than small errors. Which evaluation metric should the data scientist choose?

Accepted Answer

Root Mean Squared Error (RMSE). Root Mean Squared Error (RMSE) is the correct choice because it squares the residuals before averaging, which heavily penalizes large prediction errors (outliers) more than small errors. This aligns with the requirement to penalize large errors significantly more than small ones, as the squaring operation amplifies the impact of larger deviations.

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Mean Absolute Error (MAE)

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R-squared (R²)

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Mean Absolute Percentage Error (MAPE)

Question 7

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 8

A customer support team wants to analyze chat logs to automatically identify the most common reasons for customer complaints and track how customer sentiment changes throughout a conversation. They plan to use prebuilt Azure AI Language features without any custom training. Which combination of features should they use?

Accepted Answer

Key phrase extraction and sentiment analysis. Key phrase extraction identifies the most common reasons for complaints by pulling out important terms from the chat logs, while sentiment analysis tracks how customer sentiment changes throughout a conversation by assigning positive, negative, or neutral scores per utterance. Both are prebuilt Azure AI Language features that require no custom training, making them the correct combination for this scenario.

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Entity recognition and language detection

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Text summarization and question answering

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Conversational language understanding and personal identification

Question 9

A legal firm needs to automatically process thousands of court documents. The system must identify and redact sensitive personal information such as names, addresses, and social security numbers. Additionally, it must extract legal-specific entities like case numbers, judge names, and statute references. The firm has a small set of manually annotated documents with these legal entities. Which combination of Azure AI Language features should they use?

Accepted Answer

PII detection and custom named entity recognition (NER). The firm needs both PII detection to redact sensitive personal information and custom NER to extract legal-specific entities like case numbers and judge names from a small set of annotated documents. Azure AI Language provides a prebuilt PII detection feature for common sensitive data and a custom NER capability that can be trained on the firm's annotated documents to recognize domain-specific entities.

Answer

Prebuilt entity recognition and key phrase extraction

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Sentiment analysis and language detection

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PII detection only

Question 10

A wildlife research team uses drone imagery to monitor penguin populations in a remote area. The penguins are small, blend into the rocky background, and are often only partially visible. The team has a limited set of 500 labeled drone images showing penguins. They want to build a system that accurately detects and counts penguins. Which approach should they take using Azure AI services?

Accepted Answer

Train a Custom Vision object detection model using the labeled images.. The pre-built Computer Vision object detection API is optimized for common objects and may not perform well on small, camouflaged penguins in rocky terrain. Custom Vision allows the team to train a dedicated object detection model using their 500 labeled images, enabling the model to learn the specific visual features of penguins in this challenging environment. This approach is ideal for domain-specific detection tasks where off-the-shelf models lack accuracy.

Answer

Use the pre-built Computer Vision object detection API directly.

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Use the Computer Vision Image Analysis API with the 'dense captioning' feature.

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Train a Custom Vision image classification model with the labeled images.

Question 11

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. 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 12

A data scientist is using Azure Automated Machine Learning to build a binary classification model for a highly imbalanced dataset (95% negative, 5% positive). The data scientist wants AutoML to select the best model based on a metric that is robust to class imbalance. Which primary metric should the data scientist configure in the AutoML settings?

Accepted Answer

AUC_weighted. AUC_weighted is the correct primary metric for imbalanced binary classification because it computes the area under the ROC curve for each class and averages them with weight proportional to the class support. This weighting ensures that the metric reflects performance on both the majority (95% negative) and minority (5% positive) classes, making it robust to severe imbalance. Azure Automated Machine Learning uses AUC_weighted as a recommended metric when the dataset is skewed, as it penalizes models that ignore the minority class.

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Accuracy

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F1_score

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Log_loss

Question 13

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.

Answer

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 14

A construction safety team wants to automatically detect whether workers on a job site are wearing hard hats by analyzing images from surveillance cameras. They have a large set of labeled images containing workers wearing hard hats and workers without hard hats. The team needs to train a model that can identify the location of each hard hat in an image. Which Azure Computer Vision service should they use?

Accepted Answer

Custom Vision – Object Detection. Custom Vision – Object Detection is specifically designed to identify and locate multiple objects within an image by drawing bounding boxes around them. The construction safety team needs to detect the location of each hard hat, which requires object detection, not just classification. Custom Vision allows training a model with labeled images that include bounding box annotations for objects like hard hats.

Answer

Computer Vision – Optical Character Recognition (OCR)

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Face API

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Custom Vision – Image Classification

Question 15

A data scientist has trained a binary classification model to detect fraudulent credit card transactions. The dataset contains 99.9% legitimate transactions and only 0.1% fraudulent ones. The model predicts all transactions as legitimate, achieving 99.9% accuracy on the test set. However, the business requires the model to actually catch as many fraudulent transactions as possible. Which metric would best reveal the model's failure to identify fraud?

Accepted Answer

Recall. Recall (also known as sensitivity) measures the proportion of actual positive cases (fraudulent transactions) that were correctly identified by the model. In this scenario, the model predicts all transactions as legitimate, so it correctly identifies 0 out of the 0.1% fraudulent transactions, yielding a recall of 0%. This directly reveals the model's complete failure to catch fraud, despite the high accuracy.

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Accuracy

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Precision

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F1 score