- (Exam Topic 2)
You use Azure Machine Learning designer to publish an inference pipeline.
Which two parameters should you use to consume the pipeline? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.

1. A. the model name
2. B. the training endpoint
3. C. the authentication key
4. D. the REST endpoint

Correct Answer: AD
A: The trained model is stored as a Dataset module in the module palette. You can find it under My Datasets. Azure Machine Learning designer lets you visually connect datasets and modules on an interactive canvas to
create machine learning models.
D: You can consume a published pipeline in the Published pipelines page. Select a published pipeline and find the REST endpoint of it.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-run-batch-predictions-designer https://docs.microsoft.com/en-us/azure/machine-learning/concept-designer

- (Exam Topic 1)
To complete the sentence, select the appropriate option in the answer area.

Solution:
Privacy and security.
As AI becomes more prevalent, protecting privacy and securing important personal and business information is becoming more critical and complex. With AI, privacy and data security issues require especially close attention because access to data is essential for AI systems to make accurate and informed predictions and decisions about people. AI systems must comply with privacy laws that require transparency about the collection, use, and storage of data and mandate that consumers have appropriate controls to choose how their data is used. At Microsoft, we are continuing to research privacy and security breakthroughs (see next unit) and invest in robust compliance processes to ensure that data collected and used by our AI systems is handled responsibly.
Reference:
https://docs.microsoft.com/en-us/learn/modules/responsible-ai-principles/4-guiding-principles

Does this meet the goal?

1. A. Yes
2. B. No

Correct Answer: A

- (Exam Topic 1)
For a machine learning progress, how should you split data for training and evaluation?

1. A. Use features for training and labels for evaluation.
2. B. Randomly split the data into rows for training and rows for evaluation.
3. C. Use labels for training and features for evaluation.
4. D. Randomly split the data into columns for training and columns for evaluation.

Correct Answer: D
In Azure Machine Learning, the percentage split is the available technique to split the data. In this technique, random data of a given percentage will be split to train and test data.
Reference:
https://www.sqlshack.com/prediction-in-azure-machine-learning/