AI-900 Practice Test 49 — 15 Questions

Question 1

A developer uses Azure OpenAI to generate Python code. They want the model to limit the length of the generated code to avoid overly long and complex functions. Which parameter should the developer set in the API call?

Accepted Answer

max_tokens. The `max_tokens` parameter controls the maximum number of tokens (words or subwords) the model can generate in a single response. By setting a lower `max_tokens` value, the developer can cap the length of the generated Python code, preventing overly long and complex functions. This is the correct parameter for limiting output length.

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temperature

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top_p

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frequency_penalty

Question 2

A healthcare research organization uses an AI system to analyze patient medical records for pattern discovery. The organization must ensure that the AI system does not expose individual patient identities when reporting results. Which Microsoft responsible AI principle is most directly relevant?

Accepted Answer

Privacy and security. The scenario requires that individual patient identities are not exposed when reporting results from an AI system analyzing medical records. This directly aligns with the Privacy and security principle, which mandates that AI systems protect personal data through mechanisms such as de-identification, differential privacy, and access controls. The organization must ensure that pattern discovery outputs cannot be reverse-engineered to re-identify specific patients, making privacy the core concern.

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Fairness

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Reliability and safety

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Inclusiveness

Question 3

A hospital deploys an AI system to assist in diagnosing diseases from medical images. The system is a complex deep learning model that provides a diagnosis without any explanation. Doctors are skeptical and want to understand why the system made a particular recommendation. The hospital decides to deploy the system without providing any interpretability. Which Microsoft responsible AI principle is most directly being violated?

Accepted Answer

Transparency. The system provides a diagnosis without any explanation of how it reached its conclusion, and the hospital decides to deploy it without interpretability. This directly violates the transparency principle, which requires AI systems to be understandable and for their decisions to be explainable to users, especially in high-stakes domains like healthcare.

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Fairness

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Reliability & Safety

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Inclusiveness

Question 4

What is the role of a validation dataset in machine learning?

Accepted Answer

To tune hyperparameters and monitor performance during training without using test data. Option B is correct because the validation dataset is used during model training to tune hyperparameters and monitor performance on unseen data, preventing overfitting without contaminating the test set. This allows iterative adjustments to model architecture or learning rate while keeping the test data reserved for final evaluation.

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To provide the primary examples for training the model's weights

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To provide the final, unbiased assessment of model performance

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To store the model's trained weights for later use

Question 5

A legal firm needs to automatically extract the names of organizations and monetary values from thousands of legal contracts. They want to use a prebuilt Azure AI Language feature without custom training. Which feature should they use?

Accepted Answer

Named entity recognition. Named entity recognition (NER) is the correct choice because it is specifically designed to identify and categorize entities such as organization names and monetary values from unstructured text. Azure AI Language's prebuilt NER model can extract these entity types without any custom training, making it ideal for processing legal contracts at scale.

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Key phrase extraction

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Sentiment analysis

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Text summarization

Question 6

What is a confusion matrix's 'false positive' in medical screening?

Accepted Answer

A patient predicted to have a disease who is actually healthy. In a confusion matrix, a false positive occurs when the model predicts a positive outcome (e.g., disease present) but the actual ground truth is negative (healthy). This is a Type I error, and in medical screening it represents a healthy patient incorrectly flagged as having the disease, leading to unnecessary follow-up tests and anxiety.

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A patient who tests positive and actually has the disease

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A patient who tests negative but actually has the disease

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A patient correctly identified as healthy by the model

Question 7

A law firm needs to automatically redact personal identifiable information (PII) such as names, addresses, and social security numbers from thousands of legal documents before making them public. They want to use a prebuilt Azure AI Language feature without custom training. Which feature should they use?

Accepted Answer

Personally Identifiable Information (PII) Detection. Option C is correct because PII Detection is a prebuilt Azure AI Language feature specifically designed to identify and redact personal identifiable information such as names, addresses, and social security numbers from text. It requires no custom training, making it ideal for the law firm's use case of automatically redacting PII from legal documents before public release.

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Key Phrase Extraction

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Sentiment Analysis

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Language Detection

Question 8

What is the Azure AI Custom Vision service's 'compact' domain used for?

Accepted Answer

Producing exportable models optimized for deployment on edge devices with limited compute. The Azure AI Custom Vision service's 'compact' domain is specifically designed to produce models that can be exported to formats like TensorFlow, ONNX, or CoreML for deployment on edge devices with limited compute, memory, and power. This domain trades some accuracy for a smaller model footprint, enabling real-time inference on devices such as cameras, drones, or IoT gateways.

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Training models on a compact (small) dataset with fewer than 50 images

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Creating more compact API responses with less metadata

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Training models that use less storage in Azure blob containers

Question 9

What is the Azure AI Language feature 'custom named entity recognition' used for?

Accepted Answer

Training models to recognize domain-specific entity types unique to your business. Custom named entity recognition (NER) in Azure AI Language allows you to train a model to identify and extract domain-specific entities that are not covered by the pre-built entity catalog. This is achieved by providing labeled example data, which the service uses to learn the unique entity types relevant to your business, such as product codes, internal document IDs, or specialized medical terms.

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Automatically renaming Azure resources with appropriate names

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Replacing personally identifiable information with pseudonyms

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Detecting when text contains company-specific named brands

Question 10

A hospital deploys an AI system to assist doctors in interpreting MRI scans. The system highlights the regions of interest and provides a numeric confidence score for its findings, along with a list of the image features that contributed to the diagnosis. Which responsible AI principle is being applied?

Accepted Answer

Transparency. The system provides a numeric confidence score and a list of image features that contributed to the diagnosis, which directly supports the principle of Transparency. Transparency in responsible AI requires that AI systems are understandable and that their decisions can be explained to users, enabling clinicians to interpret and trust the output.

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Fairness

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Privacy

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Accountability

Question 11

What does the Azure AI Foundry model catalog provide?

Accepted Answer

A curated collection of AI models from Microsoft and partners for evaluation and deployment. The Azure AI Foundry model catalog provides a curated collection of AI models from Microsoft and partners, including foundation models, industry-specific models, and open-source models like those from Hugging Face. This catalog enables users to evaluate, fine-tune, and deploy models directly within the Azure ecosystem, supporting generative AI workloads such as content generation and natural language processing.

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A library of pre-written Python code for common AI tasks

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A marketplace for purchasing training datasets from vendors

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A service for storing and versioning custom-trained models only

Question 12

What is the Whisper model available in Azure OpenAI used for?

Accepted Answer

Transcribing spoken audio to text with high accuracy across languages. The Whisper model in Azure OpenAI is a large-scale speech recognition system designed to transcribe spoken audio into text. It supports multiple languages and is optimized for high accuracy, making it the correct choice for audio-to-text tasks.

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Generating images from text descriptions

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Generating very quiet (whispering) text-to-speech audio

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Summarizing long documents into concise bullet points

Question 13

A financial services company uses an AI system to detect fraudulent credit card transactions. After deployment, the system incorrectly flags a significant number of legitimate transactions as fraudulent, causing customer dissatisfaction. The company wants to reduce these false positives while still catching most fraudulent transactions. Which Microsoft responsible AI principle should guide their redesign of the system?

Accepted Answer

Reliability and safety. Option A is correct because the Reliability and safety principle emphasizes that AI systems should perform reliably, safely, and consistently under normal conditions. In this scenario, the high rate of false positives indicates the system is not operating reliably for legitimate transactions, causing customer harm. Redesigning to reduce false positives while maintaining fraud detection aligns directly with improving the system's reliability and safety for end users.

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Fairness

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Transparency

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Privacy and security

Question 14

A developer is using Azure OpenAI to generate Python code snippets. They notice that the generated code often contains syntax errors because the model introduces too much randomness. Which parameter should the developer decrease to make the output more deterministic and reduce syntax errors?

Accepted Answer

Temperature. Temperature controls the randomness of the model's output. Lowering the temperature (e.g., from 1.0 to 0.2) reduces the probability of sampling less likely tokens, making the model more deterministic and less prone to generating syntactically incorrect code. By decreasing temperature, the developer forces the model to choose higher-probability tokens, which typically results in more predictable and syntactically valid Python code.

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Top_p

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Frequency_penalty

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Max_tokens

Question 15

A historical society has scanned hundreds of books printed in the 19th century. They want to convert the scanned images into searchable, editable text. Which Azure Computer Vision capability should they use?

Accepted Answer

Optical Character Recognition (OCR). Optical Character Recognition (OCR) is the Azure Computer Vision capability designed to extract printed or handwritten text from images and convert it into machine-readable, searchable, and editable text. For the historical society's scanned books, OCR can detect characters and words from the 19th-century prints and output them as digital text, enabling full-text search and editing.

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Object detection

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Image classification

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Facial detection