Examprepwebinar - Leading source of IT Certification Exam Learning/Practice

Using the updated DP-100 dumps helps you prepare for the Designing and Implementing a Data Science Solution on Azure exam.

Pass4itSure collects all relevant Microsoft DP-100 exam information and launches the latest DP-100 dumps: https://www.pass4itsure.com/dp-100.html it is a Microsoft DP-100 exam study material that will help you pass the exam.

DP-100: Designing and Implementing a Data Science Solution on Azure exam, what is it like?

There are 40-60 exam questions in the DP-100 exam that you need to answer within 120 minutes. Exam questions have multiple-choice questions (multiple-choice and multiple-choice). The test languages are: English, Japanese, Chinese (Simplified), Korean, German, Chinese (Traditional), French, Spanish, Portuguese (Brazil), Russian, Arabic (Saudi Arabia), Italian, and Indonesian (Indonesia). It costs $165.

Pass the DP-100 exam to earn the Assistant Certification: Microsoft Certified: Azure Data Scientist Associate.

Is the DP-100 exam difficult to pass?

Yes, the Microsoft DP-100 exam is a moderately difficult exam. It’s hard not to put in the effort. You’ll need the right learning materials to help you pass the Designing and Implementing a Data Science Solution on Azure exam.

How to prepare for exam DP-100?

Getting valid DP-100 exam study materials is a great way to prepare. You can come to the Pass4itSure website to download the latest DP-100 dumps study materials (PDF+VCE) to help you prepare.

DP-100 free dumps (actual exam questions, answers, and explanations)

Download the latest DP-100 dumps for free – Drive: https://drive.google.com/file/d/15l_NZE6py6DO_QKeejxlLmQ_VzXQxX-o/view?usp=sharing

Online DP-100 Test Questions

QUESTION 1

You need to define a modeling strategy for ad response. 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.

Step 1: Implement a K-Means Clustering model
Step 2: Use the cluster as a feature in a Decision jungle model.
Decision jungles are non-parametric models, which can represent non-linear decision boundaries.
Step 3: Use the raw score as a feature in a Score Matchbox Recommender model

The goal of creating a recommendation system is to recommend one or more “items” to “users” of the system. Examples of an item could be a movie, restaurant, book, or song. A user could be a person, group of persons or other entity with item preferences.

Scenario:
Ad response rated declined.
Ad response models must be trained at the beginning of each event and applied during the sporting event.
Market segmentation models must optimize for similar ad response history.
Ad response models must support non-linear boundaries of features.

References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/multiclass-decision-jungle
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/score-matchbox-recommender

QUESTION 2

You are planning to make use of Azure Machine Learning designer to train models. You need to choose a suitable compute type. Recommendation: You choose the Inference cluster. Will the requirements be satisfied?

A. Yes
B. No

Correct Answer: B

Reference: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-create-attach-compute-studio

QUESTION 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 on 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:

Does the solution meet the goal?

A. Yes
B. No

Correct Answer: B

Note: Data used in the pipeline can be produced by one step and consumed in another step by providing a PipelineData object as an output of one step and input of one or more subsequent steps.
Compare with this example, the pipeline train step depends on the process_step_output output of the pipeline process step:

from azure ml.pipeline.core import Pipeline, PipelineData from azure ml.pipeline.steps import PythonScriptStep datastore = was.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-mlpy

QUESTION 4

You are a data scientist working for a bank and have used Azure ML to train and register a machine learning model that predicts whether a customer is likely to repay a loan. You want to understand how your model is making selections and must be sure that the model does not violate government regulations such as denying loans based on where an applicant lives.

You need to determine the extent to which each feature in the customer data is influencing predictions.
What should you do?

A. Enable data drift monitoring for the model and its training dataset.
B. Score the model against some test data with known label values and use the results to calculate a confusion matrix.
C. Use the Hyperdrive library to test the model with multiple hyperparameter values.
D. Use the interpretability package to generate an explainer for the model.
E. Add tags to the model registration indicating the names of the features in the training dataset.

Correct Answer: D

When you compute model explanations and visualize them, you\\’re not limited to an existing model for an automated ML model. You can also get a for your model with different test data. The steps in this section show you how to compute and visualize engineered feature importance based on your test data.

Incorrect Answers:
A: In the context of machine learning, data drift is the change in model input data that leads to model performance degradation. It is one of the top reasons why model accuracy degrades over time, thus monitoring data drift helps detect model performance issues.
B: A confusion matrix is used to describe the performance of a classification model. Each row displays the instances of the true, or actual class in your dataset, and each column represents the instances of the class that was predicted by the model.
C: Hyperparameters are adjustable parameters you choose for model training that guide the training process. The HyperDrive package helps you automate choosing these parameters.

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

QUESTION 5

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 azure ml.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?

A. Yes
B. No

Correct Answer: B

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

QUESTION 6

DRAG DROP
An organization uses Azure Machine Learning services and wants to expand its use of machine learning.
You have the following compute environments. The organization does not want to create another computing environment.

You need to determine which compute environment to use for the following scenarios. Which compute types should you use? To answer, drag the appropriate compute environments to the correct scenarios.
Each computing environment may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content. NOTE: Each correct selection is worth one point.

Box 1: nb_server Box 2: mlc_cluster With Azure Machine Learning, you can train your model on a variety of resources or environments, collectively referred to as compute targets. A compute target can be a local machine or a cloud resource, such as an Azure Machine Learning Compute, Azure HDInsight, or a remote virtual machine.

Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/concept-compute-target
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-set-up-training-targets

QUESTION 7

You need to select a feature extraction method. Which method should you use?

A. Mutual information
B. Pearson\’s correlation
C. Spearman correlation
D. Fisher Linear Discriminant Analysis

Correct Answer: C

Spearman\’s rank correlation coefficient assesses how well the relationship between two variables can be described using a monotonic function.

Note: Both Spearman\’s and Kendall\’s can be formulated as special cases of a more general correlation coefficient, and they are both appropriate in this scenario.

Scenario: The MedianValue and AvgRoomsInHouse columns both hold data in numeric format. You need to select a feature selection algorithm to analyze the relationship between the two columns in more detail.

Incorrect Answers:
B: The Spearman correlation between two variables is equal to the Pearson correlation between the rank values of those two variables; while Pearson\’s correlation assesses linear relationships, Spearman\’s correlation assesses monotonic relationships (whether linear or not).

References: https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/feature-selectionmodules

QUESTION 8

HOTSPOT
You need to identify the methods for dividing the data according to the testing requirements. Which properties should you select? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Hot Area:

Correct Answer:

Scenario: Testing
You must produce multiple partitions of a dataset based on sampling using the Partition and Sample module in Azure Machine Learning Studio.

Box 1: Assign to folds
Use Assign to folds option when you want to divide the dataset into subsets of the data. This option is also useful when you want to create a custom number of folds for cross-validation, or to split rows into several groups.

Not Head: Use Head mode to get only the first n rows. This option is useful if you want to test a pipeline on a small number of rows, and don\\’t need the data to be balanced or sampled in any way.
Not Sampling: The Sampling option supports simple random sampling or stratified random sampling. This is useful if you want to create a smaller representative sample dataset for testing.

Box 2: Partition evenly
Specify the partitioner method: Indicate how you want data to be apportioned to each partition, using these options: Partition evenly: Use this option to place an equal number of rows in each partition. To specify the number of output partitions, type a whole number in the Specify number of folds to split evenly into the text box.

Reference: https://docs.microsoft.com/en-us/azure/machine-learning/algorithm-module-reference/partition-and-sample

QUESTION 9

You create a binary classification model. You need to evaluate the model performance. Which two metrics can you use? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point.

A. relative absolute error
B. precision
C. accuracy
D. mean absolute error
E. coefficient of determination

Correct Answer: BC

The evaluation metrics available for binary classification models are Accuracy, Precision, Recall, F1 Score, and AUC.
Note: A very natural question is: `Out of the individuals whom the model, how many were classified correctly (TP)?\\’ This question can be answered by looking at the Precision of the model, which is the proportion of positives that are classified correctly

Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio/evaluate-model-performance

QUESTION 10

HOTSPOT
You deploy a model in Azure Container Instance. You must use the Azure Machine Learning SDK to call the model API. You need to invoke the deployed model using native SDK classes and methods. How should you complete the command? To answer, select the appropriate options in the answer areas.

NOTE: Each correct selection is worth one point.
Hot Area:

Correct Answer:

QUESTION 11

HOTSPOT
You have a dataset that contains 2,000 rows. You are building a machine learning classification model by using Azure Learning Studio. You add a Partition and Sample module to the experiment.

You need to configure the module. You must meet the following requirements:
1. Divide the data into subsets
2. Assign the rows into folds using a round-robin method
3. Allow rows in the dataset to be reused

How should you configure the module? To answer, select the appropriate options in the dialog box in the answer area. NOTE: Each correct selection is worth one point.
Hot Area:

Correct Answer:

Use the Split data into partitions option when you want to divide the dataset into subsets of the data. This option is also useful when you want to create a custom number of folds for cross-validation, or to split rows into several groups.

Add the Partition and Sample module to your experiment in Studio (classic), and connect the dataset.
For Partition or sample mode, select Assign to Folds. Use replacement in the partitioning: Select this option if you want the sampled row to be put back into the pool of rows for potential reuse. As a result, the same row might be assigned to several folds.

If you do not use a replacement (the default option), the sampled row is not put back into the pool of rows for potential reuse. As a result, each row can be assigned to only one fold. Randomized split: Select this option if you want rows to be randomly assigned to folds. If you do not select this option, rows are assigned to folds using the round-robin method.

References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/partition-and-sample

QUESTION 12

HOTSPOT
You need to configure the Edit Metadata module so that the structure of the datasets matches.
Which configuration options should you select? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.
Hot Area:

Correct Answer:

Box 1: Floating point
Need floating point for Median values.
Scenario: An initial investigation shows that the datasets are identical in structure apart from the MedianValue column. The smaller Paris dataset contains the MedianValue in text format, whereas the larger London dataset contains the MedianValue in numerical format.

Box 2: Unchanged Note: Select the Categorical option to specify that the values in the selected columns should be treated as categories. For example, you might have a column that contains the numbers 0,1, and 2, but know that the numbers actually mean “Smoker”, “Nonsmoker” and “Unknown”. In that case, by flagging the column as categorical you can ensure that the values are not used in numeric calculations, only to group data.

QUESTION 13

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 on the review screen.

You are a data scientist using Azure Machine Learning Studio.
You need to normalize values to produce an output column into bins to predict a target column.
Solution: Apply a Quantiles binning mode with a PQuantile normalization. Does the solution meet the goal?

A. Yes
B. No

Correct Answer: B

Use the Entropy MDL binning mode which has a target column.

References: https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/group-data-into-bins

Read more exam questions on this website.