AI-900 Practice Test 4 — 25 Questions

Question 1

A bank is developing an AI system to automatically approve personal loans. To ensure the system does not discriminate against any group of applicants, which Microsoft responsible AI principle should the bank primarily focus on?

Accepted Answer

Fairness. Fairness is the correct principle because it directly addresses the need to prevent discrimination in AI systems, such as loan approval models. By focusing on fairness, the bank ensures that the model's predictions do not systematically disadvantage any group based on protected attributes like race, gender, or age, which is critical for ethical and legal compliance.

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Accountability

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Inclusiveness

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Reliability and Safety

Question 2

A company wants to build a customer service chatbot that can understand user intents (e.g., 'cancel order', 'track shipment') and extract relevant entities (e.g., order number, product name). Which Azure AI Language feature should they use?

Accepted Answer

Conversational Language Understanding (CLU). Conversational Language Understanding (CLU) is the correct Azure AI Language feature because it is specifically designed to understand user intents (e.g., 'cancel order') and extract relevant entities (e.g., order number) from natural language input. This makes it ideal for building a customer service chatbot that needs to interpret and act on user requests.

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Key phrase extraction

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Sentiment analysis

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Named entity recognition (NER)

Question 3

A customer insights team at a retail company has collected thousands of product reviews. They want to automatically extract the most frequently mentioned topics or aspects from these reviews, such as 'price', 'durability', and 'customer service', without any custom training. Which prebuilt Azure AI Language 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 extract the main points or topics from unstructured text, such as product reviews, without requiring any custom training or labeled data. It surfaces frequently mentioned aspects like 'price', 'durability', and 'customer service' by analyzing linguistic patterns and term frequency, making it ideal for this summarization task.

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Text Analytics for Health

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Conversational Language Understanding

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Entity Linking

Question 4

A customer service department wants to automatically extract the names of products mentioned in customer emails and the sentiment expressed about each product. For example, from the sentence 'The battery life of the X100 is excellent, but the screen is too dark,' they need to identify 'X100' and associate 'positive' sentiment with 'battery life' and 'negative' sentiment with 'screen'. Which Azure AI Language feature should they use?

Accepted Answer

Sentiment analysis with opinion mining. Option D is correct because sentiment analysis with opinion mining is specifically designed to extract both the sentiment (positive, negative, neutral) and the associated target (e.g., 'battery life', 'screen') from text. This feature goes beyond simple sentiment scoring by identifying the opinion target and the sentiment expressed toward it, which directly matches the requirement to extract product names and their associated sentiments from customer emails.

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Custom text classification

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Key phrase extraction

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Entity linking

Question 5

A bank uses an AI system to approve or deny personal loan applications. Several customers whose loans were denied have asked for an explanation of why their application was rejected. Which Microsoft responsible AI principle requires the bank to provide understandable reasons for the AI's decision?

Accepted Answer

Transparency. Transparency is the Microsoft responsible AI principle that requires AI systems to be understandable and interpretable. In this scenario, the bank must provide clear, understandable reasons for loan denials, which directly aligns with transparency's goal of enabling users to understand how and why decisions are made. This principle ensures that AI outcomes are not opaque black-box decisions but can be explained in human terms.

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Reliability and safety

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Fairness

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Privacy and security

Question 6

A company uses Azure OpenAI Service to automatically generate customer support email responses. They want to ensure that the model does not produce responses containing offensive language, hate speech, or biased content. Which Microsoft responsible AI principle is most directly addressed by implementing content filters that screen the model's output before it is sent?

Accepted Answer

D. Fairness. Implementing content filters to screen model outputs for offensive language, hate speech, or biased content directly addresses the Fairness principle, which requires AI systems to treat all people equitably and avoid reinforcing societal biases. By filtering out harmful or biased content, the organization ensures that the generated responses do not discriminate against or marginalize any group, aligning with Microsoft's commitment to fairness in AI.

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A. Transparency

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B. Reliability and Safety

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C. Inclusiveness

Question 7

A company needs to extract text from scanned invoices and receipts. Which Azure services are suitable for this task? (Select all that apply.)

Accepted Answer

Computer Vision. Computer Vision (A) is correct because its OCR (Optical Character Recognition) capability can extract printed and handwritten text from images, including scanned invoices and receipts. Form Recognizer (B) is correct because it is specifically designed to extract text, key-value pairs, and tables from forms and documents like invoices and receipts, using prebuilt models. Both services can handle the task, but Form Recognizer is more specialized for structured document extraction.

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Text Analytics

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Custom Vision

Question 8

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?

Accepted Answer

Prompt engineering with specific instructions to avoid stereotypes. Option B is correct because 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 9

A retail company wants to automatically determine whether customer reviews are positive, negative, or neutral. Which prebuilt Azure AI Language feature should they use?

Accepted Answer

Sentiment analysis. Sentiment analysis is the correct Azure AI Language feature because it is specifically designed to classify text into positive, negative, or neutral sentiment categories. This prebuilt capability analyzes customer reviews at the document and sentence level, returning a sentiment label and confidence scores, which directly meets the requirement of automatically determining review polarity.

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Key phrase extraction

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Language detection

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Entity recognition

Question 10

A company uses a generative AI model to create blog posts. They want to ensure that the model's output never contains offensive or harmful language before the content is published. They implement a system that checks the generated text against a list of prohibited terms and blocks or edits the content if necessary. Which type of safety measure is this?

Accepted Answer

Post-processing content filtering. Option C is correct because the described system operates after the model generates text, scanning the output against a prohibited terms list and blocking or editing it. This is a classic post-processing content filtering approach, distinct from modifying the model's training data, prompts, or weights. Azure AI Content Safety is an example of such a post-processing filter that can be applied to generative AI outputs.

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Pre-training data cleaning

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Prompt engineering with safety instructions

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Model fine-tuning on safe examples

Question 11

A data scientist is training a binary classification model to detect fraudulent transactions. The dataset contains only 1% fraudulent transactions. The model achieves 99% accuracy on the test set, but when deployed, it fails to detect most actual fraud cases. Which metric would best reveal this issue?

Accepted Answer

Recall. Recall (sensitivity) measures the proportion of actual positive cases correctly identified. In this highly imbalanced dataset (1% fraud), a model can achieve 99% accuracy by simply predicting 'non-fraud' for every transaction, which yields zero true positives. Recall reveals this failure because it focuses solely on how many fraudulent transactions were caught, ignoring the vast majority of non-fraud cases.

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Accuracy

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Precision

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F1 score

Question 12

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

Question 13

A hospital uses an AI system to analyze patient records and provide treatment recommendations. They want to ensure that individual patients cannot be re-identified from the data used to train the model. Which Microsoft responsible AI principle is most directly relevant to this requirement?

Accepted Answer

Privacy and security. The requirement to prevent re-identification of individual patients from training data directly aligns with the Privacy and Security principle. This principle mandates that data be anonymized or de-identified to protect personal information, ensuring that individuals cannot be traced back from the dataset. In AI systems, this involves techniques like differential privacy, which adds noise to data to obscure individual contributions while preserving overall statistical patterns.

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Fairness

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Inclusiveness

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Accountability

Question 14

A company builds an AI system to filter job applications and rank candidates. The system is trained on historical hiring data. To reduce potential bias, the company removes protected attributes such as gender and ethnicity from the training data. However, after deployment, the system still shows a statistically significant bias against female candidates. Which Microsoft responsible AI principle most directly requires the company to investigate and address this remaining bias, even when protected attributes are removed?

Accepted Answer

Fairness. The Fairness principle requires AI systems to treat all people fairly and avoid creating or reinforcing discriminatory outcomes. Even when protected attributes like gender are removed from training data, bias can persist through proxy variables (e.g., zip code, education history) that correlate with protected attributes. The company must investigate and mitigate this remaining bias because Fairness mandates proactive assessment and correction of disparate impact, not just removal of obvious features.

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Inclusiveness

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Reliability and safety

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Transparency

Question 15

A data scientist is training a model to classify customer reviews as positive, negative, or neutral. The dataset contains 10,000 reviews, but only 500 of them are negative. The data scientist wants to ensure the model performs well on the minority class (negative reviews). Which technique should the data scientist consider to address the class imbalance?

Accepted Answer

Use a resampling technique like SMOTE or random oversampling of the minority class. Option C is correct because resampling techniques like SMOTE (Synthetic Minority Oversampling Technique) or random oversampling directly address class imbalance by generating synthetic samples or duplicating existing samples from the minority class (negative reviews). This balances the training dataset, preventing the model from being biased toward the majority class (positive/neutral reviews) and improving recall for the minority class.

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Increase the learning rate

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Add more features to the model

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Use L1 regularization (Lasso)

Question 16

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 17

A data scientist is building a classification model to detect fraudulent transactions. The dataset has 1,000,000 legitimate transactions and only 1,000 fraudulent ones. The model achieves 99.9% accuracy on the test set, but it fails to catch most fraudulent cases. Which metric should the data scientist prioritize to better evaluate the model's performance on this imbalanced dataset?

Accepted Answer

Recall. Recall measures the proportion of actual positive cases (fraudulent transactions) correctly identified by the model. With only 1,000 fraud cases out of 1,001,000 total transactions, a model that predicts 'legitimate' for every transaction would achieve 99.9% accuracy but 0% recall, making recall the critical metric for imbalanced fraud detection.

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Accuracy

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Mean Squared Error

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R-squared

Question 18

A data scientist trains a binary classification model to detect a rare disease. The dataset contains 99% negative cases and only 1% positive cases. The model predicts all cases as negative, achieving an accuracy of 99% on the test set. However, the business requires the model to identify as many positive cases as possible. Which metric should the data scientist examine to best reveal that the model is failing to identify any positive cases?

Accepted Answer

Recall. Recall (sensitivity) measures the proportion of actual positive cases correctly identified by the model. With all predictions as negative, recall is 0%, directly revealing the model's failure to detect any positive cases despite the high accuracy.

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Precision

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F1 score

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AUC-ROC

Question 19

A data scientist is evaluating a binary classification model that predicts whether a transaction is fraudulent. The test set contains 1,000 transactions: 990 legitimate and 10 fraudulent. The model's predictions are shown in the confusion matrix below. Confusion matrix: Predicted Legitimate Predicted Fraudulent Actual Legitimate 942 48 Actual Fraudulent 2 8 Which metric should the data scientist prioritize if the business goal is to minimize the number of fraudulent transactions that are missed (false negatives)?

Accepted Answer

Recall. Recall (sensitivity) measures the proportion of actual positives correctly identified, calculated as TP/(TP+FN). With 2 false negatives (missed fraudulent transactions), recall is 8/(8+2)=0.80. Minimizing missed fraud directly corresponds to maximizing recall, making it the correct priority for this business goal.

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Precision

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Accuracy

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Specificity

Question 20

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