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100% Pass Quiz Microsoft - DP-100 - Designing and Implementing a Data Science Solution on Azure–The Best Exam Objectives

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Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q107-Q112):

NEW QUESTION # 107
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 plan to use a Python script to run an Azure Machine Learning experiment. The script creates a reference to the experiment run context, loads data from a file, identifies the set of unique values for the label column, and completes the experiment run:

The experiment must record the unique labels in the data as metrics for the run that can be reviewed later.
You must add code to the script to record the unique label values as run metrics at the point indicated by the comment.
Solution: Replace the comment with the following code:
run.log_list('Label Values', label_vals)
Does the solution meet the goal?

A. No
B. Yes

Answer: B

Explanation:
Explanation
run.log_list log a list of values to the run with the given name using log_list.
Example: run.log_list("accuracies", [0.6, 0.7, 0.87])
Note:
Data= pd.read_csv('data.csv')
Data is read into a pandas.DataFrame, which is a two-dimensional, size-mutable, potentially heterogeneous tabular data.
label_vals =data['label'].unique
label_vals contains a list of unique label values.
Reference:
https://www.element61.be/en/resource/azure-machine-learning-services-complete-toolbox-ai
https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.run(class)
https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html

NEW QUESTION # 108
You plan to use the Hyperdrive feature of Azure Machine Learning to determine the optimal hyperparameter values when training a model.
You must use Hyperdrive to try combinations of the following hyperparameter values:
- learning_rate: any value between 0.001 and 0.1
- batch_size: 16, 32, or 64
You need to configure the search space for the Hyperdrive experiment.
Which two parameter expressions should you use? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.

A. a choice expression for batch_size
B. a uniform expression for learning_rate
C. a normal expression for batch_size
D. a choice expression for learning_rate
E. a uniform expression for batch_size

Answer: A,B

Explanation:
B: Continuous hyperparameters are specified as a distribution over a continuous range of values.
Supported distributions include:
uniform(low, high) - Returns a value uniformly distributed between low and high D: Discrete hyperparameters are specified as a choice among discrete values. choice can be:
one or more comma-separated values
a range object
any arbitrary list object
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters

NEW QUESTION # 109
You are implementing hyperparameter tuning for a model training from a notebook. The notebook is in an Azure Machine Learning workspace. You add code that imports all relevant Python libraries.
You must configure Bayesian sampling over the search space for the num_hidden_layers and batch_size hyperparameters.
You need to complete the following Python code to configure Bayesian sampling.
Which code segments should you use? To answer, select the appropriate options in the answer area NOTE: Each correct selection is worth one point.

Answer:

Explanation:

NEW QUESTION # 110
You use an Azure Machine Learning workspace.
You create the following Python code:

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/python/api/azureml-core/azureml.core.scriptrunconfig
https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.environment.environment

NEW QUESTION # 111
You monitor an Azure Machine Learning classification training experiment named train-classification on Azure Notebooks.
You must store a table named table as an artifact in Azure Machine Learning Studio during model training.
You need to collect and list the metrics by using MLfow.
how should you complete the code segment? To answer, select the appropriate option in the answer area.
NOTE: Each correct selection is worth on* point.

Answer:

Explanation:

NEW QUESTION # 112
......

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Sophia Murphy Sophia Murphy

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100% Pass Quiz Microsoft - DP-100 - Designing and Implementing a Data Science Solution on Azure–The Best Exam Objectives

New DP-100 Dumps Book & DP-100 Reliable Exam Testking

Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q107-Q112):

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