Microsoft DP-100 Dumps - Designing and Implementing a Data Science Solution on Azure PDF Sample Questions

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Exam Code:
DP-100
Exam Name:
Designing and Implementing a Data Science Solution on Azure
407 Questions
Last Update Date : 21 May, 2024
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Realexamdumps Providing most updated Microsoft Azure Question Answers. Here are a few exams:


Sample Questions

Realexamdumps Providing most updated Microsoft Azure Question Answers. Here are a few sample questions:

Microsoft DP-100 Sample Question 1

You need to select an environment that will meet the business and data requirements.

Which environment should you use?


Options:

A. Azure HDInsight with Spark MLlib
B. Azure Cognitive Services
C. Azure Machine Learning Studio
D. Microsoft Machine Learning Server

Answer: E

Microsoft DP-100 Sample Question 2

You need to implement a new cost factor scenario for the ad response models as illustrated in the

performance curve exhibit.

Which technique should you use?


Options:

A. Set the threshold to 0.5 and retrain if weighted Kappa deviates +/- 5% from 0.45.
B. Set the threshold to 0.05 and retrain if weighted Kappa deviates +/- 5% from 0.5.
C. Set the threshold to 0.2 and retrain if weighted Kappa deviates +/- 5% from 0.6.
D. Set the threshold to 0.75 and retrain if weighted Kappa deviates +/- 5% from 0.15.

Answer: A Explanation: Explanation: Scenario:Performance curves of current and proposed cost factor scenarios are shown in the following diagram:The ad propensity model uses a cut threshold is 0.45 and retrains occur if weighted Kappa deviated from 0.1 +/- 5%.

Microsoft DP-100 Sample Question 3

You need to implement a model development strategy to determine a user’s tendency to respond to an ad.

Which technique should you use?


Options:

A. Use a Relative Expression Split module to partition the data based on centroid distance.
B. Use a Relative Expression Split module to partition the data based on distance travelled to the event.
C. Use a Split Rows module to partition the data based on distance travelled to the event.
D. Use a Split Rows module to partition the data based on centroid distance.

Answer: A Explanation: Explanation: Split Data partitions the rows of a dataset into two distinct sets.The Relative Expression Split option in the Split Data module of Azure Machine Learning Studio is helpful when you need to divide a dataset into training and testing datasets using a numerical expression.Relative Expression Split: Use this option whenever you want to apply a condition to a number column. The number could be a date/time field, a column containing age or dollar amounts, or even a percentage. For example, you might want to divide your data set depending on the cost of the items, group people by age ranges, or separate data by a calendar date.Scenario:Local market segmentation models will be applied before determining a user’s propensity to respond to an advertisement.The distribution of features across training and production data are not consistentReferences:https://docs.microsoft.com/en-us/azure/machine-learning/studi o-module-reference/split-datb

Microsoft DP-100 Sample Question 4

You need to implement a feature engineering strategy for the crowd sentiment local models.

What should you do?


Options:

A. Apply an analysis of variance (ANOVA).
B. Apply a Pearson correlation coefficient.
C. Apply a Spearman correlation coefficient.
D. Apply a linear discriminant analysis.

Answer: D Explanation: Explanation: The linear discriminant analysis method works only on continuous variables, not categorical or ordinal variables.Linear discriminant analysis is similar to analysis of variance (ANOVA) in that it works by comparing the means of the variables.Scenario:Data scientists must build notebooks in a local environment using automatic feature engineering and model building in machine learning pipelines.Experiments for local crowd sentiment models must combine local penalty detection data.All shared features for local models are continuous variables.

Microsoft DP-100 Sample Question 5

You are determining if two sets of data are significantly different from one another by using Azure Machine Learning Studio.

Estimated values in one set of data may be more than or less than reference values in the other set of data. You must produce a distribution that has a constant Type I error as a function of the correlation.

You need to produce the distribution.

Which type of distribution should you produce?


Options:

A. Paired t-test with a two-tail option
B. Unpaired t-test with a two tail option
C. Paired t-test with a one-tail option
D. Unpaired t-test with a one-tail option

Answer: A Explanation: Explanation: Choose a one-tail or two-tail test. The default is a two-tailed test. This is the most common type of test, in which the expected distribution is symmetric around zero.Example: Type I error of unpaired and paired two-sample t-tests as a function of the correlation. The simulated random numbers originate from a bivariate normal distribution with a variance of 1.Reference: [Reference:, https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/test-hypothesis-using-t-test, , https://en.wikipedia.org/wiki/Student%27s_t-test, , ]

Microsoft DP-100 Sample Question 6

You use the Azure Machine Learning SDK in a notebook to run an experiment using a script file in an experiment folder.

The experiment fails.

You need to troubleshoot the failed experiment.

What are two possible ways to achieve this goal? Each correct answer presents a complete solution.


Options:

A. Use the get_metrics() method of the run object to retrieve the experiment run logs.
B. Use the get_details_with_logs() method of the run object to display the experiment run logs.
C. View the log files for the experiment run in the experiment folder.
D. View the logs for the experiment run in Azure Machine Learning studio.
E. Use the get_output() method of the run object to retrieve the experiment run logs.

Answer: B, D Explanation: Explanation: Use get_details_with_logs() to fetch the run details and logs created by the run.You can monitor Azure Machine Learning runs and view their logs with the Azure Machine Learning studio.Reference: [Reference:, https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.steprun, , https://docs.microsoft.com/en-us/azure/machine-learning/how-to-monitor-view-training-logs, , , ]

Microsoft DP-100 Sample Question 7

You are conducting feature engineering to prepuce data for further analysis.

The data includes seasonal patterns on inventory requirements.

You need to select the appropriate method to conduct feature engineering on the data.

Which method should you use?


Options:

A. Exponential Smoothing (ETS) function.
B. One Class Support Vector Machine module
C. Time Series Anomaly Detection module
D. Finite Impulse Response (FIR) Filter module.

Answer: E

Microsoft DP-100 Sample Question 8

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 are creating a model to predict the price of a student’s artwork depending on the following variables: the student’s length of education, degree type, and art form.

You start by creating a linear regression model.

You need to evaluate the linear regression model.

Solution: Use the following metrics: Accuracy, Precision, Recall, F1 score and AUC.

Does the solution meet the goal?


Options:

A. Yes
B. No

Answer: B Explanation: Explanation: Those are metrics for evaluating classification models, instead use: Mean Absolute Error, Root Mean Absolute Error, Relative Absolute Error, Relative Squared Error, and the Coefficient of Determination.References:https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/evaluate-modem

Microsoft DP-100 Sample Question 9

You create an Azure Machine Learning compute resource to train models. The compute resource is configured as follows:

  • Minimum nodes: 2
  • Maximum nodes: 4

You must decrease the minimum number of nodes and increase the maximum number of nodes to the following values:

  • Minimum nodes: 0
  • Maximum nodes: 8

You need to reconfigure the compute resource.

What are three possible ways to achieve this goal? Each correct answer presents a complete solution.

NOTE: Each correct selection is worth one point.


Options:

A. Use the Azure Machine Learning studio.
B. Run the update method of the AmlCompute class in the Python SDK.
C. Use the Azure portal.
D. Use the Azure Machine Learning designer.
E. Run the refresh_state() method of the BatchCompute class in the Python SDK

Answer: A, B, C Explanation: Reference: [Reference:, https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.compute.amlcompute(class), , , , ]

Microsoft DP-100 Sample Question 10

You create a machine learning model by using the Azure Machine Learning designer. You publish the model as a real-time service on an Azure Kubernetes Service (AKS) inference compute cluster. You make no changes to the deployed endpoint configuration.

You need to provide application developers with the information they need to consume the endpoint.

Which two values should you provide to application developers? Each correct answer presents part of the solution.

NOTE: Each correct selection is worth one point.


Options:

A. The name of the AKS cluster where the endpoint is hosted.
B. The name of the inference pipeline for the endpoint.
C. The URL of the endpoint.
D. The run ID of the inference pipeline experiment for the endpoint.
E. The key for the endpoint.

Answer: C, E Explanation: Explanation: Deploying an Azure Machine Learning model as a web service creates a REST API endpoint. You can send data to this endpoint and receive the prediction returned by the model.You create a web service when you deploy a model to your local environment, Azure Container Instances, Azure Kubernetes Service, or field-programmable gate arrays (FPGA). You retrieve the URI used to access the web service by using the Azure Machine Learning SDK. If authentication is enabled, you can also use the SDK to get the authentication keys or tokens.Example:# URL for the web servicescoring_uri = '# If the service is authenticated, set the key or tokenkey = 'Reference: [Reference:, https://docs.microsoft.com/en-us/azure/machine-learning/how-to-consume-web-service, , , , ]


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