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Microsoft DP-100 Exam Syllabus Topics:

SectionObjectives
Topic 1: Explore and analyze data- Ingest and prepare data for modeling
- Perform exploratory data analysis
Topic 2: Deploy and consume models- Monitor deployed models and endpoints
- Deploy models to endpoints
Topic 3: Design and prepare a machine learning solution- Manage compute and data assets
- Plan and configure Azure Machine Learning workspace
- Select appropriate Azure services for machine learning workloads
Topic 4: Optimize and manage models- Improve model performance
- Track experiments and manage model lifecycle
Topic 5: Train machine learning models- Train models using Azure Machine Learning
- Tune hyperparameters and evaluate models

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Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q36-Q41):

NEW QUESTION # 36
You register a model that you plan to use in a batch inference pipeline.
The batch inference pipeline must use a ParallelRunStep step to process files in a file dataset. The script has the ParallelRunStep step runs must process six input files each time the inferencing function is called.
You need to configure the pipeline.
Which configuration setting should you specify in the ParallelRunConfig object for the PrallelRunStep step?

Answer: B

Explanation:
node_count is the number of nodes in the compute target used for running the ParallelRunStep.
Reference:
https://docs.microsoft.com/en-us/python/api/azureml-contrib-pipeline-steps/azureml.contrib.pipeline.steps.parallelrunconfig?view=azure-ml-py


NEW QUESTION # 37
You train a classification model by using a decision tree algorithm.
You create an estimator by running the following Python code. The variable feature_names is a list of all feature names, and class_names is a list of all class names.
from interpret.ext.blackbox import TabularExplainer

You need to explain the predictions made by the model for all classes by determining the importance of all features.
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-machine-learning-interpretability-aml


NEW QUESTION # 38
You have a dataset that includes home sales data for a city. The dataset includes the following columns.

Each row in the dataset corresponds to an individual home sales transaction.
You need to use automated machine learning to generate the best model for predicting the sales price based on the features of the house.
Which values should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:

Box 1: Regression
Regression is a supervised machine learning technique used to predict numeric values.
Box 2: Price
Reference:
https://docs.microsoft.com/en-us/learn/modules/create-regression-model-azure-machine-learning-designer


NEW QUESTION # 39
You create an Azure Machine Learning workspace and install the MLflow library.
You need to tog different types of data by using the MLflow library.
Which method should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation


NEW QUESTION # 40
You perform hyper parameter tuning with Azure Machine Learning.
You create the following Python code:

For each of the following statements, select Yes if the statement is true. Otherwise, select No.

Answer:

Explanation:


NEW QUESTION # 41
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