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Microsoft DP-100 Exam

Certification Provider: Microsoft
Exam Name: Designing and Implementing a Data Science Solution on Azure
Duration: 120 Minutes
Number of questions in our database: 265
Exam Version: Jan. 10, 2022
DP-100 Exam Official Topics:
  • Topic 1: Define And Prepare The Development Environment/ Select Development Environment
  • Topic 2: Assess The Deployment Environment Constraints/ Select The Development Environment Analyze And Recommend Tools That Meet System Requirements/ Set Up Development Environment Create An Azure Data Science Environment/ Configure Data Science Work Environments
  • Topic 3: Transform Data Into Usable Datasets/ Develop Data Structures/ Perform Exploratory Data Analysis (Eda)
  • Topic 4: Review Visual Analytics Data To Discover Patterns And Determine Next Steps/ Design A Data Sampling Strategy
  • Topic 5: Design The Data Preparation Flow/ Identify Anomalies, Outliers, And Other Data Inconsistencies
  • Topic 6: Resolve Anomalies, Outliers, And Other Data Inconsistencies/ Standardize Data Formats/ Perform Feature Extraction Algorithms On Numerical Data/ Perform Feature Extraction Algorithms On Non-Numerical Data
  • Topic 7: Select An Algorithmic Approach/ Consider Data Preparation Steps That Are Specific To The Selected Algorithms
  • Topic 8: Determine Appropriate Performance Metrics/ Implement Appropriate Algorithms
  • Topic 9: Determine Ideal Split Based On The Nature Of The Data/ Determine Number Of Splits/ Identify Data Imbalances
  • Topic 10: Determine Relative Size Of Splits/ Resample A Dataset To Impose Balance/ Adjust Performance Metric To Resolve Imbalances

Free Microsoft DP-100 Exam Actual Questions

The questions for DP-100 were last updated On Jan. 10, 2022

Question #1

You have a dataset that includes confidential dat

a. You use the dataset to train a model.

You must use a differential privacy parameter to keep the data of individuals safe and private.

You need to reduce the effect of user data on aggregated results.

What should you do?

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Correct Answer: C

Differential privacy tries to protect against the possibility that a user can produce an indefinite number of reports to eventually reveal sensitive data. A value known as epsilon measures how noisy, or private, a report is. Epsilon has an inverse relationship to noise or privacy. The lower the epsilon, the more noisy (and private) the data is.


https://docs.microsoft.com/en-us/azure/machine-learning/concept-differential-privacy

Question #2

You are evaluating a completed binary classification machine.

You need to use the precision as the evaluation metric.

Which visualization should you use?

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Correct Answer: C

Receiver operating characteristic (or ROC) is a plot of the correctly classified labels vs. the incorrectly classified labels for a particular model.

References:

https://docs.microsoft.com/en-us/azure/machine-learning/how-to-understand-automated-ml#confusion-matrix


Question #3

You are building a recurrent neural network to perform a binary classification. You review the training loss, validation loss, training accuracy, and validation accuracy for each training epoch.

You need to analyze model performance.

Which observation indicates that the classification model is over fitted?

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Correct Answer: B

Question #4

You use Azure Machine Learning Studio to build a machine learning experiment.

You need to divide data into two distinct datasets.

Which module should you use?

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Correct Answer: D

The Group Data into Bins module supports multiple options for binning data. You can customize how the bin edges are set and how values are apportioned into the bins.

References:

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


Question #5

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?

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Correct Answer: B

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.



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