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Free Microsoft AI-900 Exam Braindumps (page: 8)

Page 8 of 70
PDF with 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



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