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Microsoft AI-900 Exam (page: 4)
Microsoft Azure AI Fundamentals
Updated on: 25-Aug-2025

Viewing Page 4 of 36
AI-900 PDF 273 Questions

A smart device that responds to the question “What is the stock price of Contoso. Ltd.?” is an example of which AI workload?

  1. knowledge mining
  2. natural language processing
  3. computer vision
  4. anomaly detection

Answer(s): B



HOTSPOT (Drag and Drop is not supported)
Select the answer that correctly completes the sentence.
Hot Area:

  1. See Explanation section for answer.

Answer(s): A

Explanation:




Box: anomaly detection
Sending an alert when website traffic is greater than usual is an example of      ,
Anomaly Detector is an AI service with a set of APIs, which enables you to monitor and detect anomalies in your time series data with little machine learning (ML) knowledge, either batch validation or real-time inference.
Anomaly Detector capabilities
With Anomaly Detector, you can either detect anomalies in one variable using Univariate Anomaly Detector, or detect anomalies in multiple variables with Multivariate Anomaly Detector.
Univariate Anomaly Detection
Detect anomalies in one variable, like revenue, cost, etc. The model was selected automatically based on your data pattern.
Multivariate Anomaly Detection
Detect anomalies in multiple variables with correlations, which are usually gathered from equipment or other complex system. The underlying model used is a Graph Attention Network.


Reference:

https://learn.microsoft.com/en-us/azure/ai-services/anomaly-detector/overview



You have an Azure Machine Learning model that uses clinical data to predict whether a patient has a disease. You clean and transform the clinical data.
You need to ensure that the accuracy of the model can be verified. What should you do next?

  1. Train the model by using the clinical data.
  2. Split the clinical data into two datasets.
  3. Train the model by using automated machine learning (automated ML).
  4. Validate the model by using the clinical data.

Answer(s): B

Explanation:

For prediction a regression model is used.
Train a regression model with AutoML and Python (SDK v1)
You can train a regression model with the Azure Machine Learning Python SDK using Azure Machine Learning automated ML.
This process accepts training data and configuration settings, and automatically iterates through combinations
of different feature normalization/standardization methods, models, and hyperparameter settings to arrive at the best model.



Split the data into train and test sets
Split the data into training and test sets by using the train_test_split function in the scikit-learn library. This function segregates the data into the x (features) data set for model training and the y (values to predict) data set for testing.
The purpose of this step is to have data points to test the finished model that haven't been used to train the model, in order to measure true accuracy.
In other words, a well-trained model should be able to accurately make predictions from data it hasn't already seen. You now have data prepared for auto-training a machine learning model.
Incorrect:
Not C:
Next step after splitting the data set:
Automatically train a model
To automatically train a model, take the following steps:
1. Define settings for the experiment run. Attach your training data to the configuration, and modify settings that control the training process.
2. Submit the experiment for model tuning. After submitting the experiment, the process iterates through different machine learning algorithms and hyperparameter settings, adhering to your defined constraints. It chooses the best-fit model by optimizing an accuracy metric.


Reference:

https://learn.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-models-v1



HOTSPOT (Drag and Drop is not supported)
Select the answer that correctly completes the sentence.
Hot Area:

  1. See Explanation section for answer.

Answer(s): A

Explanation:




Box: Azure Data Factory
can be used to host automated machine learning (automated ML) models in production.
Process data from automated machine learning models by using data flows.
Automated machine learning (AutoML) is adopted by machine learning projects to train, tune, and gain the best models automatically by using target metrics you specify for classification, regression, and time-series forecasting.
One challenge for AutoML is that raw data from a data warehouse or a transactional database would be a huge dataset, possibly 10 GB. A large dataset requires a longer time to train models, so we recommend that you optimize data processing before you train Azure Machine Learning models. You can use Azure Data Factory to partition a dataset into AutoML files for a Machine Learning dataset.
Incorrect:
Not: Azure Kubernetes
You can use any of the following resources for a training compute target for most jobs. Not all resources can be used for automated machine learning, machine learning pipelines, or designer. Azure Databricks can be used as a training resource for local runs and machine learning pipelines, but not as a remote target for other training.


Reference:

https://learn.microsoft.com/en-us/azure/data-factory/scenario-dataflow-process-data-aml-models https://learn.microsoft.com/en-us/azure/machine-learning/concept-compute-target



You have an Internet of Things (IoT) device that monitors engine temperature.
The device generates an alert if the engine temperature deviates from expected norms. Which type of AI workload does the device represent?

  1. knowledge mining
  2. computer vision
  3. natural language processing (NLP)
  4. anomaly detection

Answer(s): D

Explanation:

Anomaly Detector is an AI service with a set of APIs, which enables you to monitor and detect anomalies in your time series data with little machine learning (ML) knowledge, either batch validation or real-time inference.
Anomaly Detector capabilities
With Anomaly Detector, you can either detect anomalies in one variable using Univariate Anomaly Detector, or detect anomalies in multiple variables with Multivariate Anomaly Detector.
Univariate Anomaly Detection
Detect anomalies in one variable, like revenue, cost, etc. The model was selected automatically based on your data pattern.
Multivariate Anomaly Detection
Detect anomalies in multiple variables with correlations, which are usually gathered from equipment or other complex system. The underlying model used is a Graph Attention Network.


Reference:

https://learn.microsoft.com/en-us/azure/ai-services/anomaly-detector/overview



Which service should you use to extract text, key/value pairs, and table data automatically from scanned documents?

  1. Azure AI Face
  2. Azure AI Language
  3. Azure AI Document Intelligence
  4. Azure AI Custom Vision

Answer(s): C

Explanation:

Correct:
* Azure AI Document Intelligence (best answer)
Form Recognizer (old, replaced by Azure AI Document Intelligence)
Incorrect:
* Azure AI Custom Vision
* Azure AI Language
* Custom Vision
* Azure AI Face
Text Analytics
Note:
Azure AI Document Intelligence
AI Document Intelligence is an AI service that applies advanced machine learning to extract text, key-value pairs, tables, and structures from documents automatically and accurately. Turn documents into usable data and shift your focus to acting on information rather than compiling it. Start with prebuilt models or create custom models tailored to your documents both on premises and in the cloud with the AI Document Intelligence studio or SDK.
Form Recognizer
Accelerate your business processes by automating information extraction. Form Recognizer applies advanced machine learning to accurately extract text, key/value pairs, and tables from documents. With just a few samples, Form Recognizer tailors its understanding to your documents, both on-premises and in the cloud.
Turn forms into usable data at a fraction of the time and cost, so you can focus more time acting on the information rather than compiling it.


Reference:

https://azure.microsoft.com/en-us/products/ai-services/ai-document-intelligence



You have an Azure Machine Learning pipeline that contains a Split Data module. The Split Data module outputs to a Train Model module and a Score Model module. What is the function of the Split Data module?

  1. scaling numeric variables so that they are within a consistent numeric range
  2. creating training and validation datasets
  3. diverting records that have missing data
  4. selecting columns that must be included in the model

Answer(s): B



Which statement is an example of a Microsoft responsible AI principle?

  1. AI systems must use only publicly available data.
  2. AI systems must be understandable.
  3. AI systems must keep personal details public.
  4. AI systems must protect the interests of the company.

Answer(s): B



Viewing Page 4 of 36



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