Microsoft Designing and Implementing a Data Science Solution on Azure DP-100 Exam Practice Test

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Total 506 questions
Question 1

You are creating a new Azure Machine Learning pipeline using the designer.

The pipeline must train a model using data in a comma-separated values (CSV) file that is published on a

website. You have not created a dataset for this file.

You need to ingest the data from the CSV file into the designer pipeline using the minimal administrative effort.

Which module should you add to the pipeline in Designer?



Answer : D

The preferred way to provide data to a pipeline is a Dataset object. The Dataset object points to data that lives in or is accessible from a datastore or at a Web URL. The Dataset class is abstract, so you will create an instance of either a FileDataset (referring to one or more files) or a TabularDataset that's created by from one or more files with delimited columns of data.

Example:

from azureml.core import Dataset

iris_tabular_dataset = Dataset.Tabular.from_delimited_files([(def_blob_store, 'train-dataset/iris.csv')])


https://docs.microsoft.com/en-us/azure/machine-learning/how-to-create-your-first-pipeline

Question 2

You create a binary classification model by using Azure Machine Learning Studio.

You must tune hyperparameters by performing a parameter sweep of the model. The parameter sweep must meet the following requirements:

iterate all possible combinations of hyperparameters

minimize computing resources required to perform the sweep

You need to perform a parameter sweep of the model.

Which parameter sweep mode should you use?



Answer : D

Maximum number of runs on random grid: This option also controls the number of iterations over a random sampling of parameter values, but the values are not generated randomly from the specified range; instead, a matrix is created of all possible combinations of parameter values and a random sampling is taken over the matrix. This method is more efficient and less prone to regional oversampling or undersampling.

If you are training a model that supports an integrated parameter sweep, you can also set a range of seed values to use and iterate over the random seeds as well. This is optional, but can be useful for avoiding bias introduced by seed selection.

Incorrect Answers:

B: If you are building a clustering model, use Sweep Clustering to automatically determine the optimum number of clusters and other parameters.

C: Entire grid: When you select this option, the module loops over a grid predefined by the system, to try different combinations and identify the best learner. This option is useful for cases where you don't know what the best parameter settings might be and want to try all possible combination of values.

E: If you choose a random sweep, you can specify how many times the model should be trained, using a random combination of parameter values.


https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/tune-model-hyperparameters

Question 3

You train and publish a machine teaming model.

You need to run a pipeline that retrains the model based on a trigger from an external system.

What should you configure?



Answer : C


Question 4

You ate reviewing model benchmarks in Azure Al Foundry.

You must use a large language model based on the proficiency of the model to generate the most linguistically correct text. You need to select the model benchmark. Which benchmark metric should you focus on?



Answer : A


Question 5

You use the Azure Machine learning SDK foe Python to create a pipeline that includes the following step:

The output of the step run must be cached and reused on subsequent runs when the source.directory value has not changed.

You need to define the step.

What should you include in the step definition?



Answer : A


Question 6

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?



Answer : A

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.


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

Question 7

You manage an Azure Machine Learning workspace named workspaces

You K v2 code to attach an Azure Synapse Spark pool as a compute target in workspaces The code must invoke the constructor of the SynapseSparkCompute class.

You need to invoke the constructor.

What should you use?



Answer : B


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