- (Exam Topic 3)
You are evaluating a completed binary classification machine learning model. You need to use the precision as the valuation metric.
Which visualization should you use?

1. A. Binary classification confusion matrix
2. B. box plot
3. C. Gradient descent
4. D. coefficient of determination

Correct Answer: A
References:
https://machinelearningknowledge.ai/confusion-matrix-and-performance-metrics-machine-learning/

- (Exam Topic 3)
You create a Python script that runs a training experiment in Azure Machine Learning. The script uses the Azure Machine Learning SDK for Python.
You must add a statement that retrieves the names of the logs and outputs generated by the script. You need to reference a Python class object from the SDK for the statement.
Which class object should you use?

1. A. Run
2. B. ScripcRunConfig
3. C. Workspace
4. D. Experiment

Correct Answer: A
A run represents a single trial of an experiment. Runs are used to monitor the asynchronous execution of a trial, log metrics and store output of the trial, and to analyze results and access artifacts generated by the trial.
The run Class get_all_logs method downloads all logs for the run to a directory. Reference:
https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.run(class)

- (Exam Topic 3)
You create a pipeline in designer to train a model that predicts automobile prices.
Because of non-linear relationships in the data, the pipeline calculates the natural log (Ln) of the prices in the training data, trains a model to predict this natural log of price value, and then calculates the exponential of the scored label to get the predicted price.
The training pipeline is shown in the exhibit. (Click the Training pipeline tab.)
Training pipeline

You create a real-time inference pipeline from the training pipeline, as shown in the exhibit. (Click the Real-time pipeline tab.)
Real-time pipeline

You need to modify the inference pipeline to ensure that the web service returns the exponential of the scored label as the predicted automobile price and that client applications are not required to include a price value in the input values.
Which three modifications must you make to the inference pipeline? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.

1. A. Connect the output of the Apply SQL Transformation to the Web Service Output module.
2. B. Replace the Web Service Input module with a data input that does not include the price column.
3. C. Add a Select Columns module before the Score Model module to select all columns other than price.
4. D. Replace the training dataset module with a data input that does not include the price column.
5. E. Remove the Apply Math Operation module that replaces price with its natural log from the data flow.
6. F. Remove the Apply SQL Transformation module from the data flow.

Correct Answer: ACE

- (Exam Topic 3)
You train a machine learning model.
You must deploy the model as a real-time inference service for testing. The service requires low CPU utilization and less than 48 MB of RAM. The compute target for the deployed service must initialize automatically while minimizing cost and administrative overhead.
Which compute target should you use?

1. A. Azure Kubernetes Service (AKS) inference cluster
2. B. Azure Machine Learning compute cluster
3. C. Azure Container Instance (ACI)
4. D. attached Azure Databricks cluster

Correct Answer: C
Azure Container Instances (ACI) are suitable only for small models less than 1 GB in size. Use it for low-scale CPU-based workloads that require less than 48 GB of RAM.
Note: Microsoft recommends using single-node Azure Kubernetes Service (AKS) clusters for dev-test of larger models.
Reference:
https://docs.microsoft.com/id-id/azure/machine-learning/how-to-deploy-and-where

- (Exam Topic 3)
You have an Azure Machine Learning workspace that contains a CPU-based compute cluster and an Azure Kubernetes Services (AKS) inference cluster. You create a tabular dataset containing data that you plan to use to create a classification model.
You need to use the Azure Machine Learning designer to create a web service through which client applications can consume the classification model by submitting new data and getting an immediate prediction as a response.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

Solution:
Step 1: Create and start a Compute Instance
To train and deploy models using Azure Machine Learning designer, you need compute on which to run the training process, test the model, and host the model in a deployed service.
There are four kinds of compute resource you can create:
Compute Instances: Development workstations that data scientists can use to work with data and models. Compute Clusters: Scalable clusters of virtual machines for on-demand processing of experiment code. Inference Clusters: Deployment targets for predictive services that use your trained models.
Attached Compute: Links to existing Azure compute resources, such as Virtual Machines or Azure Databricks clusters.
Step 2: Create and run a training pipeline..
After you've used data transformations to prepare the data, you can use it to train a machine learning model. Create and run a training pipeline
Step 3: Create and run a real-time inference pipeline
After creating and running a pipeline to train the model, you need a second pipeline that performs the same data transformations for new data, and then uses the trained model to inference (in other words, predict) label values based on its features. This pipeline will form the basis for a predictive service that you can publish for applications to use.
Reference:
https://docs.microsoft.com/en-us/learn/modules/create-classification-model-azure-machine-learning-designer/

Does this meet the goal?

1. A. Yes
2. B. No

Correct Answer: A