AI-900 Practice Test 6 — 10 Questions

Question 1

What is the difference between face detection and face identification?

Accepted Answer

Face detection finds face locations; face identification determines who the person is from an enrolled database. Face detection is a computer vision task that locates human faces in an image or video, returning bounding box coordinates. Face identification (or recognition) goes a step further by matching a detected face against a database of enrolled individuals to determine a specific identity. Option B correctly distinguishes these two operations: detection finds where faces are, while identification determines who the person is.

Answer

Face detection identifies who the person is; face identification counts how many faces are present

Answer

Face detection works on videos; face identification works on static images only

Answer

They are the same operation with different names

Question 2

What is the purpose of Azure AI Speech's 'batch transcription' capability?

Accepted Answer

Asynchronous processing of large volumes of audio files for cost-efficient transcription at scale. Azure AI Speech's batch transcription is designed for asynchronous processing of large volumes of pre-recorded audio files. It allows you to submit multiple audio files for transcription without requiring real-time interaction, making it cost-efficient for scenarios like call center analytics or media captioning where immediate results are not needed.

Answer

Real-time transcription of live audio streams for immediate use

Answer

Synchronizing speech transcription across multiple languages simultaneously

Answer

Training a custom speech recognition model on audio samples

Question 3

What is 'label imbalance' in a classification dataset and how does it affect model training?

Accepted Answer

When one class greatly outnumbers others, causing models to be biased toward the majority class. Label imbalance refers to a situation in classification datasets where one class (the majority class) has significantly more samples than other classes (minority classes). This causes the model to become biased toward predicting the majority class, as it minimizes overall loss by ignoring minority classes, leading to poor generalization and low recall for underrepresented classes.

Answer

When labels in the training data contain spelling errors

Answer

When training labels are applied inconsistently by different human annotators

Answer

When a model produces predictions that don't match any of the training labels

Question 4

What is the Azure AI Translator's 'document translation' capability?

Accepted Answer

Asynchronously translating complete documents while preserving layout and formatting. Azure AI Translator's document translation capability is designed for asynchronous batch translation of entire documents (e.g., Word, PDF, HTML) while preserving the original layout, structure, and formatting. This is achieved through the Document Translation API, which processes files in their entirety rather than translating individual text snippets in real time.

Answer

Translating short text snippets from a chat interface

Answer

Creating translated copies of database records

Answer

Converting documents from one file format to another in a different language

Question 5

A data scientist is preparing a dataset to train a model that predicts customer churn. The dataset includes a column 'CustomerID' which is a unique identifier for each customer. Should the data scientist include the 'CustomerID' column as a feature in the training data?

Accepted Answer

No, because the CustomerID is a random unique identifier with no predictive power for churn.. CustomerID is a unique identifier that does not contain any meaningful pattern or relationship with the target variable (churn). Including such a column would introduce noise and risk overfitting, as the model could memorize each ID rather than learning generalizable patterns. In Azure Machine Learning, features should be predictive attributes, not arbitrary labels.

Answer

Yes, because it uniquely identifies each customer and helps the model differentiate them.

Answer

Yes, because the model can learn patterns from the numeric values.

Answer

No, because the CustomerID column contains too many missing values.

Question 6

A parking lot management company uses security cameras to monitor vehicles. They need to both detect the presence of license plates in an image and read the alphanumeric characters on those plates. Which Azure Computer Vision capability should they use to achieve both requirements?

Accepted Answer

Optical Character Recognition (OCR) - Read API. (OCR - Read API) is correct because Azure's Read API is specifically designed to both detect the presence of text (including license plates) in an image and extract the alphanumeric characters from that text. This meets both requirements—detecting the plate and reading its characters—in a single call, using deep-learning-based recognition models optimized for printed and handwritten text.

Answer

Image Analysis (describe image and detect objects)

Answer

Face API

Answer

Custom Vision (object detection)

Question 7

A self-driving car company tests its AI navigation system in a new city. The system fails to detect a temporary construction barrier and causes a collision. The company wants to ensure that their AI system is robust to unexpected and unusual environmental conditions. Which Microsoft responsible AI principle is most directly relevant to this requirement?

Accepted Answer

Reliability and safety. The requirement is to ensure the AI system is robust to unexpected and unusual environmental conditions, which directly falls under the responsible AI principle of Reliability and safety. This principle focuses on building systems that operate consistently and safely under a wide range of conditions, including edge cases like temporary construction barriers, and that fail gracefully when they cannot perform as expected.

Answer

Fairness

Answer

Privacy and security

Answer

Transparency

Question 8

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.

Answer

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

Answer

A marketplace for purchasing training datasets from vendors

Answer

A service for storing and versioning custom-trained models only

Question 9

A company wants to build a chatbot that can answer questions based on its internal policy documents. The documents are stored in Azure Blob Storage. They plan to use Azure OpenAI to generate answers. Which approach should they use to ensure the answers are grounded in the actual policy content?

Accepted Answer

Use Azure AI Search to index the documents and provide relevant passages as context to GPT-4. Azure AI Search can index the policy documents stored in Azure Blob Storage, enabling retrieval of relevant passages based on the user's query. These passages are then provided as context in the prompt to GPT-4, ensuring the generated answer is grounded in the actual policy content rather than relying on the model's pre-trained knowledge.

Answer

Fine-tune GPT-4 on all policy documents

Answer

Include the entire policy document text in the prompt each time

Answer

Use DALL-E to visualize policy concepts

Question 10

A financial company develops an AI system that recommends loan amounts based on historical data. The historical data includes years of discriminatory lending practices against certain minority groups. As a result, the AI system disproportionately denies loans to members of those groups. Which Microsoft responsible AI principle is most directly violated by this scenario?

Accepted Answer

Fairness. The AI system's loan recommendations are based on historical data that contains discriminatory lending practices, leading to disproportionate denials for minority groups. This directly violates the Fairness principle, which requires AI systems to treat all people equitably and avoid reinforcing existing biases. The system's outputs are not fair because they perpetuate historical inequities, making fairness the most relevant principle.

Answer

Reliability and Safety

Answer

Privacy and Security

Answer

Inclusiveness