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Microsoft DP-100 Practice Test - Questions Answers, Page 15

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Question 141

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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 create an Azure Machine Learning service datastore in a workspace. The datastore contains the following files:

/data/2018/Q1.csv

/data/2018/Q2.csv

/data/2018/Q3.csv

/data/2018/Q4.csv

/data/2019/Q1.csv

All files store data in the following format:

id,f1,f2,I

1,1,2,0

2,1,1,1

3,2,1,0

4,2,2,1

You run the following code:

Microsoft DP-100 image Question 32 89132 10022024015825000000

You need to create a dataset named training_data and load the data from all files into a single data frame by using the following code:

Microsoft DP-100 image Question 32 89132 10022024015825000000

Solution: Run the following code:

Microsoft DP-100 image Question 32 89132 10022024015825000000

Does the solution meet the goal?

Yes
Yes
No
No
Suggested answer: A
Explanation:

Use two file paths.

Use Dataset.Tabular_from_delimeted as the data isn't cleansed.

Note:

A TabularDataset represents data in a tabular format by parsing the provided file or list of files. This provides you with the ability to materialize the data into a pandas or Spark DataFrame so you can work with familiar data preparation and training libraries without having to leave your notebook. You can create a TabularDataset object from .csv, .tsv, .parquet, .jsonl files, and from SQL query results.

Reference:

https://docs.microsoft.com/en-us/azure/machine-learning/how-to-create-register-datasets

asked 02/10/2024
Freddie Lewis
39 questions

Question 142

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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 choice expression for learning_rate
a choice expression for learning_rate
a uniform expression for learning_rate
a uniform expression for learning_rate
a normal expression for batch_size
a normal expression for batch_size
a choice expression for batch_size
a choice expression for batch_size
a uniform expression for batch_size
a uniform expression for batch_size
Suggested answer: B, D
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

asked 02/10/2024
daniel valdera carrasco
47 questions

Question 143

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You run an automated machine learning experiment in an Azure Machine Learning workspace. Information about the run is listed in the table below:

Microsoft DP-100 image Question 34 89134 10022024015825000000

You need to write a script that uses the Azure Machine Learning SDK to retrieve the best iteration of the experiment run.

Which Python code segment should you use?

Suggested answer: D
Explanation:

The get_output method on automl_classifier returns the best run and the fitted model for the last invocation. Overloads on get_output allow you to retrieve the best run and fitted model for any logged metric or for a particular iteration.

In [ ]:

best_run, fitted_model = local_run.get_output()

Reference:

https://notebooks.azure.com/azureml/projects/azureml-getting-started/html/how-to-use-azureml/automated-machine-learning/classification-with-deployment/auto-ml-classification-with-deployment.ipynb

asked 02/10/2024
Dylan Brons
39 questions

Question 144

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You have a comma-separated values (CSV) file containing data from which you want to train a classification model.

You are using the Automated Machine Learning interface in Azure Machine Learning studio to train the classification model. You set the task type to Classification.

You need to ensure that the Automated Machine Learning process evaluates only linear models.

What should you do?

Add all algorithms other than linear ones to the blocked algorithms list.
Add all algorithms other than linear ones to the blocked algorithms list.
Set the Exit criterion option to a metric score threshold.
Set the Exit criterion option to a metric score threshold.
Clear the option to perform automatic featurization.
Clear the option to perform automatic featurization.
Clear the option to enable deep learning.
Clear the option to enable deep learning.
Set the task type to Regression.
Set the task type to Regression.
Suggested answer: A
Explanation:

Automatic featurization can fit non-linear models.

Reference: https://econml.azurewebsites.net/spec/estimation/dml.html https://docs.microsoft.com/en-us/azure/machine-learning/how-to-use-automated-ml-for-ml-models

asked 02/10/2024
Alexandre BOUCHER
46 questions

Question 145

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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:

from azureml.core import Run

import pandas as pd

run = Run.get_context()

data = pd.read_csv('data.csv')

label_vals = data['label'].unique()

# Add code to record metrics here

run.complete()

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.upload_file('outputs/labels.csv', './data.csv')

Does the solution meet the goal?

Yes
Yes
No
No
Suggested answer: B
Explanation:

label_vals has the unique labels (from the statement label_vals = data['label'].unique()), and it has to be logged.

Note:

Instead use the run_log function to log the contents in label_vals:

for label_val in label_vals: run.log('Label Values', label_val)

Reference:

https://www.element61.be/en/resource/azure-machine-learning-services-complete-toolbox-ai

asked 02/10/2024
martin lopez
36 questions

Question 146

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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:

from azureml.core import Run

import pandas as pd

run = Run.get_context()

data = pd.read_csv('data.csv')

label_vals = data['label'].unique()

# Add code to record metrics here

run.complete()

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_table('Label Values', label_vals)

Does the solution meet the goal?

Yes
Yes
No
No
Suggested answer: B
Explanation:

Instead use the run_log function to log the contents in label_vals:

for label_val in label_vals: run.log('Label Values', label_val)

Reference:

https://www.element61.be/en/resource/azure-machine-learning-services-complete-toolbox-ai

asked 02/10/2024
Kinshuk Choubisa
48 questions

Question 147

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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:

from azureml.core import Run

import pandas as pd run = Run.get_context() data = pd.read_csv('data.csv') label_vals = data['label'].unique() # Add code to record metrics here run.complete()

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:

for label_val in label_vals:

run.log('Label Values', label_val)

Does the solution meet the goal?

Yes
Yes
No
No
Suggested answer: A
Explanation:

The run_log function is used to log the contents in label_vals:

for label_val in label_vals: run.log('Label Values', label_val)

Reference: https://www.element61.be/en/resource/azure-machine-learning-services-complete-toolbox-ai

asked 02/10/2024
Maxime ESSIS
41 questions

Question 148

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You are solving a classification task.

You must evaluate your model on a limited data sample by using k-fold cross-validation. You start by configuring a k parameter as the number of splits.

You need to configure the k parameter for the cross-validation.

Which value should you use?

k=0.5
k=0.5
k=0.01
k=0.01
k=5
k=5
k=1
k=1
Suggested answer: C
Explanation:

Leave One Out (LOO) cross-validation

Setting K = n (the number of observations) yields n-fold and is called leave-one out cross-validation (LOO), a special case of the K-fold approach.

LOO CV is sometimes useful but typically doesn't shake up the data enough. The estimates from each fold are highly correlated and hence their average can have high variance. This is why the usual choice is K=5 or 10. It provides a good compromise for the bias-variance tradeoff.

asked 02/10/2024
Robert Petty
54 questions

Question 149

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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 create a model to forecast weather conditions based on historical data.

You need to create a pipeline that runs a processing script to load data from a datastore and pass the processed data to a machine learning model training script.

Solution: Run the following code:

Microsoft DP-100 image Question 40 89140 10022024015825000000

Does the solution meet the goal?

Yes
Yes
No
No
Suggested answer: B
Explanation:

The two steps are present: process_step and train_step

The training data input is not setup correctly.

Note:

Data used in pipeline can be produced by one step and consumed in another step by providing a PipelineData object as an output of one step and an input of one or more subsequent steps.

PipelineData objects are also used when constructing Pipelines to describe step dependencies. To specify that a step requires the output of another step as input, use a PipelineData object in the constructor of both steps.

For example, the pipeline train step depends on the process_step_output output of the pipeline process step:

from azureml.pipeline.core import Pipeline, PipelineData

from azureml.pipeline.steps import PythonScriptStep

datastore = ws.get_default_datastore()

process_step_output = PipelineData("processed_data", datastore=datastore)

process_step = PythonScriptStep(script_name="process.py",

arguments=["--data_for_train", process_step_output],

outputs=[process_step_output],

compute_target=aml_compute,

source_directory=process_directory)

train_step = PythonScriptStep(script_name="train.py",

arguments=["--data_for_train", process_step_output],

inputs=[process_step_output],

compute_target=aml_compute,

source_directory=train_directory)

pipeline = Pipeline(workspace=ws, steps=[process_step, train_step])

Reference:

https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.pipelinedata?view=azure-ml-py

asked 02/10/2024
IOSSIF ZINGUER
53 questions

Question 150

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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 create a model to forecast weather conditions based on historical data.

You need to create a pipeline that runs a processing script to load data from a datastore and pass the processed data to a machine learning model training script.

Solution: Run the following code:

Microsoft DP-100 image Question 41 89141 10022024015825000000

Does the solution meet the goal?

Yes
Yes
No
No
Suggested answer: B
Explanation:

train_step is missing.

Reference:

https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.pipelinedata?view=azure-ml-py

asked 02/10/2024
Slawomir Kucharski
36 questions
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