- (Exam Topic 3)
You are using a Git repository to track work in an Azure Machine Learning workspace. You need to authenticate a Git account by using SSH.
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:
Graphical user interface, text, application, chat or text message Description automatically generated
Authenticate your Git Account with SSH: Step 1: Generating a public/private key pair Generate a new SSH key
* 1. Open the terminal window in the Azure Machine Learning Notebook Tab.
* 2. Paste the text below, substituting in your email address. ssh-keygen -t rsa -b 4096 -C "your_email@example.com"
This creates a new ssh key, using the provided email as a label.
> Generating public/private rsa key pair.
Step 2: Add the public key to the Git Account
In your terminal window, copy the contents of your public key file. Step 3: Clone the Git repository by using an SSH repository URL
* 1. Copy the SSH Git clone URL from the Git repo.
* 2. Paste the url into the git clone command below, to use your SSH Git repo URL. This will look something like:
git clone git@example.com:GitUser/azureml-example.git Cloning into 'azureml-example'.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/concept-train-model-git-integration

Does this meet the goal?

1. A. Yes
2. B. No

Correct Answer: A

- (Exam Topic 3)
You use the Azure Machine Learning SDK in a notebook to run an experiment using a script file in an experiment folder.
The experiment fails.
You need to troubleshoot the failed experiment.
What are two possible ways to achieve this goal? Each correct answer presents a complete solution.

1. A. Use the get_metrics() method of the run object to retrieve the experiment run logs.
2. B. Use the get_details_with_logs() method of the run object to display the experiment run logs.
3. C. View the log files for the experiment run in the experiment folder.
4. D. View the logs for the experiment run in Azure Machine Learning studio.
5. E. Use the get_output() method of the run object to retrieve the experiment run logs.

Correct Answer: BD
Use get_details_with_logs() to fetch the run details and logs created by the run.
You can monitor Azure Machine Learning runs and view their logs with the Azure Machine Learning studio. Reference:
https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.steprun https://docs.microsoft.com/en-us/azure/machine-learning/how-to-monitor-view-training-logs

- (Exam Topic 3)
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You are analyzing a numerical dataset which contain missing values in several columns.
You must clean the missing values using an appropriate operation without affecting the dimensionality of the feature set.
You need to analyze a full dataset to include all values.
Solution: Use the last Observation Carried Forward (IOCF) method to impute the missing data points. Does the solution meet the goal?

1. A. Yes
2. B. No

Correct Answer: B
Instead use the Multiple Imputation by Chained Equations (MICE) method.
Replace using MICE: For each missing value, this option assigns a new value, which is calculated by using a method described in the statistical literature as "Multivariate Imputation using Chained Equations" or "Multiple Imputation by Chained Equations". With a multiple imputation method, each variable with missing data is modeled conditionally using the other variables in the data before filling in the missing values.
Note: Last observation carried forward (LOCF) is a method of imputing missing data in longitudinal studies. If a person drops out of a study before it ends, then his or her last observed score on the dependent variable is used for all subsequent (i.e., missing) observation points. LOCF is used to maintain the sample size and to reduce the bias caused by the attrition of participants in a study.
References:
https://methods.sagepub.com/reference/encyc-of-research-design/n211.xml https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3074241/

- (Exam Topic 3)
You use Azure Machine Learning to train a model based on a dataset named dataset1. You define a dataset monitor and create a dataset named dataset2 that contains new data.
You need to compare dataset1 and dataset2 by using the Azure Machine Learning SDK for Python. Which method of the DataDriftDetector class should you use?

1. A. run
2. B. get
3. C. backfill
4. D. update

Correct Answer: C
A backfill run is used to see how data changes over time. Reference:
https://docs.microsoft.com/en-us/python/api/azureml-datadrift/azureml.datadrift.datadriftdetector.datadriftdetect

- (Exam Topic 3)
You create an Azure Machine Learning workspace named ML-workspace. You also create an Azure Databricks workspace named DB-workspace. DB-workspace contains a cluster named DB-cluster.
You must use DB-cluster to run experiments from notebooks that you import into DB-workspace.
You need to use ML-workspace to track MLflow metrics and artifacts generated by experiments running on DB-cluster. The solution must minimize the need for custom code.
What should you do?

1. A. From DB-cluster, configure the Advanced Logging option.
2. B. From DB-workspac
3. C. configure the Link Azure ML workspace option.
4. D. From ML-workspac
5. E. create an attached compute.
6. F. From ML-workspac
7. G. create a compute cluster.

Correct Answer: B
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-use-mlflow-azure-databricks