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The latest DP-100 exam questions in the Microsoft DP-100 exam

QUESTION 1
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 new experiment in Azure Machine Learning Studio.
One class has a much smaller number of observations than tin- other classes in the training set.
You need to select an appropriate data sampling strategy to compensate for the class imbalance.
Solution: You use the Principal Components Analysis (PCA) sampling mode.
Does the solution meet the goal?
A. Yes
B. No
Correct Answer: B

QUESTION 2
DRAG DROP
YOU have a data-set that contains over 150 features. You use the dataset to train a Support Vector Machine (SVM)
binary classifirer.
You need to use the Permutation Feature Importance module in Azure Machine Learning Studio to compute a set of
feature importance scores for the dataset.
In which order should you perform the actions? To answer move al actions from from the list of Actions to the answer
area and arrange them in the correct order.
Select and Place:

gailemartinenaite dp-100 exam questions-q2

Correct Answer:

gailemartinenaite dp-100 exam questions-q2-2

QUESTION 3
You are retrieving data from a large datastore by using Azure Machine Learning Studio.
You must create a subset of the data for testing purposes using a random sampling seed based on the system clock.
You add the Partition and Sample module to your experiment.
You need to select the properties for the module.
Which values should you select? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Hot Area:

gailemartinenaite dp-100 exam questions-q3

Correct Answer:

gailemartinenaite dp-100 exam questions-q3-2

Box 1: Sampling Create a sample of data This option supports simple random sampling or stratified random sampling.
This is useful if you want to create a smaller representative sample dataset for testing.
1.
Add the Partition and Sample module to your experiment in Studio, and connect the dataset.
2.
Partition or sample mode: Set this to Sampling.
3.
Rate of sampling. See box 2 below.
Box 2: 0
3. Rate of sampling. Random seed for sampling: Optionally, type an integer to use as a seed value.
This option is important if you want the rows to be divided the same way every time. The default value is 0, meaning that
a starting seed is generated based on the system clock. This can lead to slightly different results each time you run the
experiment.
References: https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/partition-and-sample

QUESTION 4
You are developing deep learning models to analyze semi-structured, unstructured, and structured data types.
You have the following data available for model building:
Video recordings of sporting events
Transcripts of radio commentary about events
Logs from related social media feeds captured during sporting events
You need to select an environment for creating the model.
Which environment should you use?
A. Azure Cognitive Services
B. Azure Data Lake Analytics
C. Azure HDInsight with Spark MLib
D. Azure Machine Learning Studio
Correct Answer: A
Azure Cognitive Services expand on Microsoft\\’s evolving portfolio of machine learning APIs and enable developers to
easily add cognitive features ?such as emotion and video detection; facial, speech, and vision recognition; and speech
and language understanding ?into their applications. The goal of Azure Cognitive Services is to help developers create
applications that can see, hear, speak, understand, and even begin to reason. The catalog of services within Azure
Cognitive Services can be categorized into five main pillars – Vision, Speech, Language, Search, and Knowledge.
References: https://docs.microsoft.com/en-us/azure/cognitive-services/welcome

QUESTION 5
You are a data scientist in a company that provides data science for professional sporting events. Models will be global
and local market data to meet the following business goals:
-Understand sentiment of mobile device users at sporting events based on audio from crowd reactions.
-Access a user\\’s tendency to respond to an advertisement.
-Customize styles of ads served on mobile devices.
-Use video to detect penalty events. Current environment Requirements
-Media used for penalty event detection will be provided by consumer devices. Media may include images and videos
captured during the sporting event and snared using social media. The images and videos will have varying sizes and
formats.
– The data available for model building comprises of seven years of sporting event media.The sporting event media
includes: recorded videos, transcripts of radio commentary, and logs from related social media feeds feeds captured
during the sporting events.
-Crowd sentiment will include audio recordings submitted by event attendees in both mono and stereo Formats.
Advertisements
-Ad response models must be trained at the beginning of each event and applied during the sporting event.
-Market segmentation nxxlels must optimize for similar ad resporr.r history.
-Sampling must guarantee mutual and collective exclusivity local and global segmentation models that share the same
features.
-Local market segmentation models will be applied before determining a user\\’s propensity to respond to an
advertisement.
-Data scientists must be able to detect model degradation and decay.
-Ad response models must support non linear boundaries features.

The ad propensity model uses a cut threshold is 0.45 and retrains occur if weighted Kappa deviates from 0.1 +/-5%.

The ad propensity model uses cost factors shown in the following diagram:

gailemartinenaite dp-100 exam questions-q5

The ad propensity model uses proposed cost factors shown in the following diagram:

gailemartinenaite dp-100 exam questions-q5-2

Performance curves of current and proposed cost factor scenarios are shown in the following diagram:

gailemartinenaite dp-100 exam questions-q5-3

Penalty detection and sentiment Findings
-Data scientists must build an intelligent solution by using multiple machine learning models for penalty event detection.
-Data scientists must build notebooks in a local environment using automatic feature engineering and model building in
machine learning pipelines.
-Notebooks must be deployed to retrain by using Spark instances with dynamic worker allocation
-Notebooks must execute with the same code on new Spark instances to recode only the source of the data;
-Global penalty detection models must be trained by using dynamic runtime graph computation during training.
-Local penalty detection models must be written by using BrainScript.
Experiments for local crowd sentiment models must combine local penalty detection data;
Crowd sentiment models must identify known sounds such as cheers and known catch phrases.
Individual crowd sentiment models will detect similar sounds.
All shared features for local models are continuous variables.
Shared features must use double precision. Subsequent layers must have aggregate running mean and standard
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deviation metrics Available.
segments
During the initial weeks in production, the following was observed:
-Ad response rates declined.
-*rops were not consistent across ad styles.
-The distribution of features across training and production data are not consistent.
Analysis shows that of the 100 numeric features on user location and behavior, the 47 features that come from location
sources are being used as raw features. A suggested experiment to remedy the bias and variance issue is to engineer
10
linearly uncorrected features.
Penalty detection and sentiment
-Initial data discovery shows a wide range of densities of target states in training data used for crowd sentiment models.
-*ll penalty detection models show inference phases using a Stochastic Gradient Descent (SGD) are running too stow.
-*udio samples show that the length of a catch phrase varies between 25%-47%, depending on region.
-The performance of the global penalty detection models show lower variance but higher bias when comparing training
and validation sets. Before implementing any feature changes, you must confirm the bias and variance using all training
and validation cases.
You need to resolve the local machine learning pipeline performance issue. What should you do?
A. Increase Graphic Processing Units (GPUs).
B. Increase the learning rate.
C. Increase the training iterations.
D. Increase Central Processing Units (CPUs).
Correct Answer: A

QUESTION 6
HOTSPOT
You need to set up the Permutation Feature Importance module according to the model training 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:

gailemartinenaite dp-100 exam questions-q6

Correct Answer:

gailemartinenaite dp-100 exam questions-q6-2

Box 1: Accuracy
Scenario: You want to configure hyperparameters in the model learning process to speed the learning phase by using
hyperparameters. In addition, this configuration should cancel the lowest performing runs at each evaluation interval,
thereby directing effort and resources towards models that are more likely to be successful.
Box 2: R-Squared

QUESTION 7
You need to replace the missing data in the AccessibilityToHighway columns.
How should you configure the Clean Missing Data module? To answer, select the appropriate options in the answer
area.
NOTE: Each correct selection is worth one point.
Hot Area:

gailemartinenaite dp-100 exam questions-q7

Correct Answer:

gailemartinenaite dp-100 exam questions-q7-2

Box 1: Replace using MICE
Replace using MICE: For each missing value, this option assigns a new value, which is calculated by using a method
described in the statistical literature as “Multivariate Imputation using Chained Equations” or “Multiple Imputation by
Chained Equations”. With a multiple imputation method, each variable with missing data is modeled conditionally using
the other variables in the data before filling in the missing values.
Scenario: The AccessibilityToHighway column in both datasets contains missing values. The missing data must be
replaced with new data so that it is modeled conditionally using the other variables in the data before filling in the
missing
values.
Box 2: Propagate
Cols with all missing values indicate if columns of all missing values should be preserved in the output.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/clean-missing-data

QUESTION 8
You are creating a new experiment in Azure Machine Learning Studio. You have a small dataset that has missing
values in many columns. The data does not require the application of predictors for each column. You plan to use the
Clean
Missing Data module to handle the missing data.
You need to select a data cleaning method.
Which method should you use?
A. Synthetic Minority Oversampling Technique (SMOTE)
B. Replace using MICE
C. Replace using; Probabilistic PCA
D. Normalization
Correct Answer: A

QUESTION 9
You are analyzing a dataset containing historical data from a local taxi company. You arc developing a regression a
regression model.
You must predict the fare of a taxi trip.
You need to select performance metrics to correctly evaluate the- regression model.
Which two metrics can you use? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
A. an F1 score that is high
B. an R Squared value dose to 1
C. an R-Squared value close to 0
D. a Root Mean Square Error value that is high
E. a Root Mean Square Error value that is tow
F. an F 1 score that is low.
Correct Answer: DF


QUESTION 10
You are performing feature engineering on a dataset.
You must add a feature named CityName and populate the column value with the text London.
You need to add the new feature to the dataset.
Which Azure Machine Learning Studio module should you use?
A. Edit Metadata
B. Preprocess Text
C. Execute Python Script
D. Latent Dirichlet Allocation
Correct Answer: A
Typical metadata changes might include marking columns as features.
References: https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/edit-metadata

QUESTION 11
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 analyzing a numerical dataset which contains missing values in several columns.
You must clean the missing values using an appropriate operation without affecting the dimensionality of the feature
set.
You need to analyze a full dataset to include all values.
Solution: Calculate the column median value and use the median value as the replacement for any missing value in the
column.
Does the solution meet the goal?
A. Yes
B. No
Correct Answer: B
Use the Multiple Imputation by Chained Equations (MICE) method.
References:
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3074241/
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/clean-missing-data

QUESTION 12
HOTSPOT
You are creating a machine learning model in Python. The provided dataset contains several numerical columns and
one text column.
1.
Biker
2.
Cars
3.
Vans
4.
Boats
You are building a regression model using the scikit- learn Python package.
You need to transform the text data to be compatible with the scikit-learn Python package
How should you complete the code segment? To answer, select the appropriate options in the answer area;NOTE: Each correct selection is worth one point.
Hot Area:

gailemartinenaite dp-100 exam questions-q12

Correct Answer:

gailemartinenaite dp-100 exam questions-q12-2

QUESTION 13
HOTSPOT
You need to build a feature extraction strategy for the local models.
How should you complete the code segment? To answer, select the appropriate options in the answer area;
NOTE: Each correct selection is worth one point.
Hot Area:

gailemartinenaite dp-100 exam questions-q13

Correct Answer:

gailemartinenaite dp-100 exam questions-q13-2

Microsoft DP-100 Exam Practice Questions | Youtu.be

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