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Free DP-100 Exam Braindumps (page: 16)

Page 15 of 127
PDF with 512 Questions

You are solving a classification task.
The dataset is imbalanced.
You need to select an Azure Machine Learning Studio module to improve the classification accuracy.
Which module should you use?

  1. Permutation Feature Importance
  2. Filter Based Feature Selection
  3. Fisher Linear Discriminant Analysis
  4. Synthetic Minority Oversampling Technique (SMOTE)

Answer(s): D

Explanation:

Use the SMOTE module in Azure Machine Learning Studio (classic) to increase the number of underrepresented cases in a dataset used for machine learning.
SMOTE is a better way of increasing the number of rare cases than simply duplicating existing cases.
You connect the SMOTE module to a dataset that is imbalanced. There are many reasons why a dataset might be imbalanced: the category you are targeting might be very rare in the population, or the data might simply be difficult to collect. Typically, you use SMOTE when the class you want to analyze is under- represented.


Reference:

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



DRAG DROP (Drag and Drop is not supported)
You configure a Deep Learning Virtual Machine for Windows.
You need to recommend tools and frameworks to perform the following:
-Build deep neural network (DNN) models
-Perform interactive data exploration and visualization
Which tools and frameworks should you recommend? To answer, drag the appropriate tools to the correct tasks. Each tool 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.
Select and Place:

  1. See Explanation section for answer.

Answer(s): A

Explanation:


Box 1: Vowpal Wabbit
Use the Train Vowpal Wabbit Version 8 module in Azure Machine Learning Studio (classic), to create a machine learning model by using Vowpal Wabbit.
Box 2: PowerBI Desktop
Power BI Desktop is a powerful visual data exploration and interactive reporting tool
BI is a name given to a modern approach to business decision making in which users are empowered to find, explore, and share insights from data across the enterprise.


Reference:

https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/train-vowpal-wabbit-version-8-model https://docs.microsoft.com/en-us/azure/architecture/data-guide/scenarios/interactive-data-exploration



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?

  1. Assign Data to Clusters
  2. Load Trained Model
  3. Partition and Sample
  4. Tune Model-Hyperparameters

Answer(s): C

Explanation:

Partition and Sample with the Stratified split option outputs multiple datasets, partitioned using the rules you specified.


Reference:

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



DRAG DROP (Drag and Drop is not supported)
You are creating an experiment by using Azure Machine Learning Studio.
You must divide the data into four subsets for evaluation. There is a high degree of missing values in the data. You must prepare the data for analysis.
You need to select appropriate methods for producing the experiment.
Which three modules should you run in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
NOTE: More than one order of answer choices is correct. You will receive credit for any of the correct orders you select.
Select and Place:

  1. See Explanation section for answer.

Answer(s): A

Explanation:



The Clean Missing Data module in Azure Machine Learning Studio, to remove, replace, or infer missing values.
Incorrect Answers:
- Latent Direchlet Transformation: Latent Dirichlet Allocation module in Azure Machine Learning Studio, to group otherwise unclassified text into a number of categories. Latent Dirichlet Allocation (LDA) is often used in natural language processing (NLP) to find texts that are similar. Another common term is topic modeling.
- Build Counting Transform: Build Counting Transform module in Azure Machine Learning Studio, to analyze training data. From this data, the module builds a count table as well as a set of count-based features that can be used in a predictive model.
Missing Value Scrubber: The Missing Values Scrubber module is deprecated.

- Feature hashing: Feature hashing is used for linguistics, and works by converting unique tokens into integers.
- Replace discrete values: the Replace Discrete Values module in Azure Machine Learning Studio is used to generate a probability score that can be used to represent a discrete value. This score can be useful for understanding the information value of the discrete values.


Reference:

https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/clean-missing-data






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