Online Access Free Microsoft.AI-900.v2026-09-24.q146 Practice Test (Page 12)

AI-900 Exam Question 51

To complete the sentence, select the appropriate option in the answer area.

AI-900 Exam Question 52

In which two scenarios can you use the Form Recognizer service? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.

A.Extract the invoice number from an invoice.

B.Translate a form from French to English.

C.Find image of product in a catalog.

D.Identity the retailer from a receipt.

Correct Answer: A,D

The correct answers are A and D because both scenarios involve extracting structured information from documents, which is exactly what Azure Form Recognizer is designed to do.
According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn module "Explore computer vision", Form Recognizer is an Azure Cognitive Service that uses advanced Optical Character Recognition (OCR) and machine learning to extract key-value pairs, tables, and text from structured and semi-structured documents such as receipts, invoices, business cards, and forms. It allows organizations to automate data entry and digitize document processing.
* A. Extract the invoice number from an invoice # CorrectForm Recognizer can identify fields such as invoice number, total amount, date, vendor name, and billing address directly from invoices. It uses prebuilt models for invoices and receipts that automatically detect and extract relevant information without requiring extensive manual labeling. As stated in Microsoft Learn, "Form Recognizer extracts information from documents like receipts and invoices and returns structured data including key-value pairs."
* D. Identify the retailer from a receipt # CorrectThe prebuilt receipt model in Form Recognizer can read printed or scanned receipts and extract data points such as retailer name, transaction date, total amount, and tax information. This makes it ideal for expense reporting, auditing, or financial reconciliation.
The following options are incorrect:
* B. Translate a form from French to English # This task involves language translation, which is performed by Azure Translator, not Form Recognizer.
* C. Find an image of a product in a catalog # This requires object detection or image classification, which are part of Computer Vision, not Form Recognizer.
Therefore, based on Microsoft's AI-900 learning objectives and documentation, the two correct scenarios are:
# A. Extract the invoice number from an invoice
# D. Identify the retailer from a receipt

AI-900 Exam Question 53

For a machine learning progress, how should you split data for training and evaluation?

A.Use features for training and labels for evaluation.

B.Randomly split the data into rows for training and rows for evaluation.

C.Use labels for training and features for evaluation.

D.Randomly split the data into columns for training and columns for evaluation.

Correct Answer: B

https://docs.microsoft.com/en-us/azure/machine-learning/algorithm-module-reference/split-data The correct answer is B. Randomly split the data into rows for training and rows for evaluation.
According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and the Microsoft Learn module "Describe fundamental principles of machine learning on Azure", the process of developing a machine learning model involves dividing the available dataset into two or more parts-commonly training data and evaluation (or testing) data. The goal is to ensure that the model can learn patterns from one subset of the data (training set) and then be objectively tested on unseen data (evaluation set) to measure how well it generalizes to new situations.
The training dataset contains both features (the measurable inputs) and labels (the target outputs). The model learns from the patterns and relationships between these features and labels. The evaluation dataset also contains features and labels, but it is kept separate during the training phase. Once the model has been trained, it is tested on this unseen evaluation data to calculate metrics like accuracy, precision, recall, or F1 score.
Microsoft emphasizes that the data split should be random and based on rows, not columns. Each row represents a complete observation (for example, one customer record, one transaction, or one image).
Randomly splitting ensures that both subsets represent the same distribution of data, avoiding bias. Splitting by columns would separate features themselves, which would make the model training invalid.
The AI-900 materials often illustrate this using Azure Machine Learning's data preparation workflow, where data is randomly divided (commonly 70% for training and 30% for testing). This ensures the model learns from diverse examples and is fairly evaluated.
Therefore, the verified and correct approach, as per Microsoft's official guidance, is B. Randomly split the data into rows for training and rows for evaluation.