AI-900 Practice Test 42 — 15 Questions

Question 1

A company uses a GPT-based model to generate marketing copy. They notice the model occasionally produces text that includes harmful stereotypes. They want to reduce these harmful outputs without retraining the model. Which approach is most appropriate?

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

Accepted Answer

Prompt engineering with specific instructions to avoid stereotypes. Prompt engineering allows you to guide the model's behavior at inference time without modifying its weights. By including explicit instructions in the prompt (e.g., 'Avoid harmful stereotypes'), you can steer the output toward safer content. This is the most appropriate approach when retraining is not an option, as it directly addresses the undesired outputs through input design.

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Fine-tuning the model on a curated dataset

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Reducing the temperature parameter to zero

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Increasing the maximum output length

Question 2

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

Accepted Answer

Optical Character Recognition (OCR). Optical Character Recognition (OCR), because the task requires extracting text (destination address, sender name, package weight) from handwritten shipping forms. Azure's OCR API, part of Computer Vision, is specifically designed to detect and read printed and handwritten text from images, making it the appropriate capability for this document processing scenario.

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Image Analysis

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Face detection

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Custom Vision

Question 3

A data scientist is building a classification model to predict customer churn. The dataset has only 5% churn cases. The model achieves 95% accuracy on the test set, but upon investigation, the data scientist finds the model predicts 'not churn' for nearly every customer. Which metric should the data scientist primarily use to evaluate the model's performance on this imbalanced dataset?

Accepted Answer

F1 score. In an imbalanced dataset with only 5% churn, a model that predicts 'not churn' for every case achieves 95% accuracy by always guessing the majority class. This accuracy is misleading because it fails to identify any churn cases. The F1 score (option B) is the harmonic mean of precision and recall, making it the primary metric for evaluating classification performance on imbalanced data, as it penalizes both false positives and false negatives and is not skewed by class imbalance.

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Accuracy

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Mean Absolute Error (MAE)

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R-squared

Question 4

A hospital deploys an AI system to recommend treatment plans for patients. After deployment, the system is found to have significantly lower accuracy for patients from certain racial and ethnic groups because historical medical data for those groups is sparse. Which Microsoft responsible AI principle should the hospital prioritize to address this issue?

Accepted Answer

Fairness. Fairness. The AI system's lower accuracy for certain racial and ethnic groups is a direct fairness issue, as it produces biased outcomes due to sparse historical data. Microsoft's Fairness principle requires AI systems to treat all groups equitably and mitigate disparities in performance, which is the core problem here.

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Inclusiveness

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Transparency

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Accountability

Question 5

A company uses Azure OpenAI Service to generate product descriptions for an e-commerce site. They want to ensure that the generated descriptions never contain offensive, violent, or hateful content. Which built-in feature should the developer enable in the Azure OpenAI Service?

Accepted Answer

Content Filtering. Content Filtering is a built-in safety feature in Azure OpenAI Service that automatically detects and blocks harmful content categories such as hate, violence, sexual, and self-harm. It operates at the input prompt and output completion level, ensuring generated product descriptions remain compliant with content policies without requiring custom development.

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Prompt Engineering

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Fine-tuning

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Token Limit

Question 6

A logistics company uses drone imagery to monitor a busy container yard. They need to count the exact number of individual shipping containers, even when containers are partially stacked on top of each other or overlapping in the image. Which Azure Computer Vision capability should they choose to achieve the most accurate individual object separation?

Accepted Answer

Instance segmentation. Instance segmentation is the correct choice because it not only detects each individual object in an image but also generates a pixel-level mask for each instance, allowing the model to distinguish between overlapping or stacked objects like shipping containers. This capability provides the most accurate separation of individual containers, even when they partially occlude each other, by assigning unique masks to each instance rather than grouping all containers into a single class.

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Image classification

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Object detection

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Semantic segmentation

Question 7

What is the 'model catalogue' in Azure AI Foundry/AI Studio?

Accepted Answer

A curated collection of AI models from multiple providers available for deployment in Azure. The model catalogue in Azure AI Foundry (formerly AI Studio) is a curated collection of AI models from multiple providers, including OpenAI, Meta, Hugging Face, and Microsoft, that can be deployed and fine-tuned directly within the Azure environment. It simplifies the process of discovering, comparing, and deploying foundation models for generative AI workloads without requiring manual setup or external registries.

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A product listing of Azure AI hardware accelerators available for purchase

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A directory of all Azure AI customer support contacts organised by model type

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A registry of all models that have passed Microsoft's responsible AI certification

Question 8

A hospital deploys a machine learning model to screen patients for a rare disease. Only 0.1% of patients actually have the disease. The model correctly identifies most positive cases but also flags many healthy patients as potentially having the disease. The hospital wants to minimize the number of healthy patients who are incorrectly told they might have the disease. Which metric should the model optimize?

Accepted Answer

Precision. Precision measures the proportion of positive identifications that are actually correct. In this scenario, the hospital wants to minimize false positives (healthy patients incorrectly told they might have the disease). Optimizing precision directly reduces false positives, which is the stated goal.

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Recall

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F1 score

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Accuracy

Question 9

A developer is using Azure OpenAI to generate creative product descriptions. The outputs are often repetitive and lack variety. The developer wants to increase the diversity of the generated text while still keeping it coherent. Which parameter should the developer increase?

Accepted Answer

Temperature. Increasing the temperature parameter makes the model's output more random by amplifying the probability of less likely tokens, which increases diversity and reduces repetition. A higher temperature (e.g., 0.9) flattens the probability distribution, allowing the model to choose more varied words while still maintaining coherence, as long as the temperature is not set too high (e.g., above 1.0).

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Top_p

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Max_tokens

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Frequency_penalty

Question 10

A developer uses Azure OpenAI Service to generate short product descriptions. The developer notices that the model sometimes produces nonsensical or very low-probability words that make the output less coherent. The developer wants to reduce the chance of such outputs while still allowing some creative variability. Which parameter should the developer adjust in the API request?

Accepted Answer

Set the top_p parameter to a value like 0.9. Setting `top_p` to 0.9 (nucleus sampling) instructs the model to consider only the tokens whose cumulative probability mass reaches 90%, thereby cutting off very low-probability (nonsensical) tokens while still allowing creative variability from the top 90% of likely tokens. This directly addresses the developer's goal of reducing incoherent outputs without fully deterministic generation.

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Decrease the temperature parameter to 0.1

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Increase the stop parameter to include more stop sequences

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Increase the max_tokens parameter to allow longer descriptions

Question 11

A data scientist trains a machine learning model on a dataset of housing prices. The model achieves 98% accuracy on the training data but only 72% accuracy on a separate test set. What is the most likely problem with this model?

Accepted Answer

Overfitting. The model's high accuracy on training data (98%) but significantly lower accuracy on test data (72%) is a classic symptom of overfitting, where the model learns noise and specific patterns in the training set rather than generalizing to new, unseen data. In Azure Machine Learning, this often occurs when the model is too complex (e.g., deep decision trees or high-degree polynomial features) relative to the amount of training data, and regularization techniques like L1/L2 regularization or early stopping are not applied.

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Underfitting

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Data leakage

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Class imbalance

Question 12

A data scientist trains a machine learning model to predict house prices based on features like square footage, number of bedrooms, and location. The model achieves a very low error on the training data but performs poorly on a held-out test set. Which term best describes this situation?

Accepted Answer

Overfitting. The model performs exceptionally well on training data but poorly on test data, which is the classic symptom of overfitting. Overfitting occurs when the model learns noise and specific patterns in the training set rather than generalizing to unseen data. In Azure Machine Learning, this can be detected by monitoring the gap between training and validation metrics, and mitigated using techniques like regularization or early stopping.

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Underfitting

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High bias

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High variance

Question 13

A retail store wants to analyze customer movement patterns, such as dwell time in front of displays and foot traffic heatmaps, using existing surveillance cameras. Which Azure Computer Vision capability is most suitable?

Accepted Answer

Spatial analysis. Spatial analysis is the correct choice because it is specifically designed to analyze people's presence, movement, and interactions within a physical space using video feeds. It can measure dwell time in front of displays and generate foot traffic heatmaps by tracking individuals across camera frames, which directly matches the retail store's requirements.

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Object detection

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Optical character recognition (OCR)

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Image classification

Question 14

A data scientist trains a linear regression model to predict house prices. The model's training error is very high, and its test error is nearly as high. Which term best describes this situation?

Accepted Answer

Underfitting. Underfitting occurs when a model is too simple to capture the underlying patterns in the data, resulting in high training error and similarly high test error. In this linear regression scenario, the model fails to learn the relationship between features and house prices, leading to poor performance on both training and test sets.

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Overfitting

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High bias

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High variance

Question 15

A data scientist trains a model to predict house prices. The model achieves 99% accuracy on the training data but only 80% accuracy on new test data. Which technique is most likely to help improve the model's generalization?

Accepted Answer

Apply regularization to the model. The model is overfitting: it has memorized the training data (99% accuracy) but fails to generalize to new data (80% accuracy). Regularization (e.g., L1 or L2) penalizes large weights, reducing the model's complexity and forcing it to learn simpler patterns that generalize better. This directly addresses the variance problem without discarding useful information.

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Reduce the amount of training data

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Remove some features from the dataset

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Increase the number of layers in the neural network