AI-900 Timed Practice Test 2 — 10 Questions

Question 1

A data scientist trains a binary classification model to detect fraudulent credit card transactions. The dataset contains 99.5% legitimate transactions and 0.5% fraudulent transactions. The model predicts every transaction as legitimate and achieves 99.5% accuracy on the test set. Which metric would best reveal that the model is failing to identify any fraudulent transactions?

Accepted Answer

Recall. Recall (also known as sensitivity) measures the proportion of actual positive cases correctly identified by the model. In this scenario, the model predicts all transactions as legitimate, so it correctly identifies zero fraudulent transactions, giving a recall of 0%. Accuracy alone is misleading because the dataset is highly imbalanced (99.5% legitimate, 0.5% fraudulent), and a 99.5% accuracy can be achieved by simply predicting the majority class. Recall directly reveals the model's failure to detect any fraud.

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Precision

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F1 score

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Mean Absolute Error (MAE)

Question 2

A developer uses Azure OpenAI Service to generate product reviews for an e-commerce site. The developer notices that the model often repeats the same phrases within the same review, making the output sound unnatural. Which parameter should the developer adjust to reduce this repetition?

Accepted Answer

Frequency_penalty. The frequency_penalty parameter reduces the likelihood of the model repeating the same phrases by penalizing tokens that have already appeared in the generated text. A higher frequency_penalty value (e.g., 0.5 to 1.0) discourages the model from reusing the same words or phrases, making the output more diverse and natural. This directly addresses the issue of repetitive phrasing in product reviews.

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Temperature

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Top_p

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Max_tokens

Question 3

A data scientist trains a regression model to predict house prices. The model has a mean absolute error (MAE) of $5,000 on the test set. Which statement best interprets this metric?

Accepted Answer

On average, the model's predictions are $5,000 away from the actual prices.. Option A is correct because Mean Absolute Error (MAE) measures the average absolute difference between predicted and actual values. An MAE of $5,000 means that, on average, each prediction deviates from the true house price by $5,000. This is a standard interpretation of MAE in regression metrics.

Answer

The model is accurate 95% of the time.

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The model's predictions are within $5,000 of the actual prices for 50% of the houses.

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The square root of the average squared error is $5,000.

Question 4

A data scientist trains a regression model to predict house prices using features like bedrooms, square footage, and location. The model achieves an R-squared of 0.95 on the test set. However, when deployed to predict prices in a new city with different property characteristics, the predictions are very inaccurate. Which concept best explains this poor performance?

Accepted Answer

Overfitting. The model achieved an R-squared of 0.95 on the test set, indicating excellent performance on data from the same distribution. However, when deployed to a new city with different property characteristics, the predictions were very inaccurate. This is a classic symptom of overfitting, where the model has learned noise and patterns specific to the training data (e.g., city-specific price trends) that do not generalize to unseen data from a different distribution.

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Underfitting

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High bias

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Data drift

Question 5

A logistics company receives thousands of handwritten shipping labels daily. They need an automated solution to extract the destination address, sender name, and package weight from these labels. Which prebuilt Azure Computer Vision capability should they use?

Accepted Answer

Optical Character Recognition (OCR). Option A is correct because Azure Computer Vision's Optical Character Recognition (OCR) API is specifically designed to extract printed or handwritten text from images. In this scenario, the handwritten shipping labels contain textual data (destination address, sender name, package weight), and OCR can read and digitize that text for automated processing. The other options address different visual tasks—object detection, classification, or facial recognition—none of which extract text content.

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Object detection

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Image classification

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Facial recognition

Question 6

A manufacturing company wants to use Azure Computer Vision to inspect products on an assembly line for defects. They have a labeled dataset with images of defective and non-defective products. They need to not only classify products as defective or not, but also identify the exact location of the defect (e.g., a crack) in the image. Which Azure Computer Vision capability should they use?

Accepted Answer

Custom Vision object detection. Custom Vision object detection is the correct choice because it not only classifies images (defective vs. non-defective) but also localizes defects by drawing bounding boxes around them. The labeled dataset with defect locations directly supports training a model to output both class labels and spatial coordinates, which is exactly what object detection provides.

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Custom Vision image classification

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Azure Face API

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Optical Character Recognition (OCR)

Question 7

A developer wants to build a virtual assistant that can understand user intents such as 'Book a flight' or 'Check weather' and extract relevant entities like destination and date. The developer has a small set of labeled example utterances. Which Azure AI Language feature should the developer use?

Accepted Answer

Conversational Language Understanding (CLU). Conversational Language Understanding (CLU) is the correct Azure AI Language feature because it is specifically designed to extract both intents (e.g., 'Book a flight') and entities (e.g., destination, date) from user utterances. The developer has a small set of labeled examples, which CLU can use to train a custom model for intent recognition and entity extraction, making it ideal for building a virtual assistant.

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Custom Question Answering

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Custom Text Classification

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Named Entity Recognition (NER)

Question 8

A museum wants to automatically transcribe handwritten labels on historical artifacts. The handwriting varies in style and may include numbers and special characters. Which Azure Computer Vision capability should they use?

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, including numbers and special characters. Azure Computer Vision's OCR API can handle varied handwriting styles and convert them into machine-readable text, making it ideal for transcribing historical artifact labels.

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Image captioning

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Facial recognition

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Object detection

Question 9

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 10

A developer is using Azure OpenAI Service to generate product descriptions. They want the output to be highly focused and deterministic, with less randomness. 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 by reducing the probability of sampling less likely tokens, resulting in more focused and predictable responses.

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Max tokens

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Top-p

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Frequency penalty