Question: 1
You need to select an environment that will meet the business and data requirements.
Which environment should you use?
A. Azure HDInsight with Spark MLlib
B. Azure Cognitive Services
C. Azure Machine Learning Studio
D. Microsoft Machine Learning Server
Answer: D
Question: 2
You need to implement a scaling strategy for the local penalty detection data.
Which normalization type should you use?
A. Streaming
B. Weight
C. Batch
D. Cosine
Answer: C
Explanation:
Post batch normalization statistics (PBN) is the Microsoft Cognitive Toolkit (CNTK) version of how to
evaluate the population mean and variance of Batch Normalization which could be used in inference
Original Paper.
In CNTK, custom networks are defined using the BrainScriptNetworkBuilder and described in the
CNTK network description language "BrainScript."
Scenario:
Local penalty detection models must be written by using BrainScript.
References:
https://docs.microsoft.com/en-us/cognitive-toolkit/post-batch-normalization-statistics
Question: 3
You need to implement a feature engineering strategy for the crowd sentiment local models.
What should you do?
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:
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.
Incorrect Answers:
B: The Pearson correlation coefficient, sometimes called Pearson’s R test, is a statistical value that
measures the linear relationship between two variables. By examining the coefficient values, you can
infer something about the strength of the relationship between the two variables, and whether they
are positively correlated or negatively correlated.
C: Spearman’s correlation coefficient is designed for use with non-parametric and non-normally
distributed data. Spearman's coefficient is a nonparametric measure of statistical dependence
between two variables, and is sometimes denoted by the Greek letter rho. The Spearman’s
coefficient expresses the degree to which two variables are monotonically related. It is also called
Spearman rank correlation, because it can be used with ordinal variables.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/fisherlineardiscriminantanalysis
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/computelinearcorrelation
https://www.dumpspass4sure.com/microsoft/dp-100-dumps.html
https://docs.microsoft.com/en-us/learn/certifications/exams/dp-100
You need to select an environment that will meet the business and data requirements.
Which environment should you use?
A. Azure HDInsight with Spark MLlib
B. Azure Cognitive Services
C. Azure Machine Learning Studio
D. Microsoft Machine Learning Server
Answer: D
Question: 2
You need to implement a scaling strategy for the local penalty detection data.
Which normalization type should you use?
A. Streaming
B. Weight
C. Batch
D. Cosine
Answer: C
Explanation:
Post batch normalization statistics (PBN) is the Microsoft Cognitive Toolkit (CNTK) version of how to
evaluate the population mean and variance of Batch Normalization which could be used in inference
Original Paper.
In CNTK, custom networks are defined using the BrainScriptNetworkBuilder and described in the
CNTK network description language "BrainScript."
Scenario:
Local penalty detection models must be written by using BrainScript.
References:
https://docs.microsoft.com/en-us/cognitive-toolkit/post-batch-normalization-statistics
Question: 3
You need to implement a feature engineering strategy for the crowd sentiment local models.
What should you do?
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:
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.
Incorrect Answers:
B: The Pearson correlation coefficient, sometimes called Pearson’s R test, is a statistical value that
measures the linear relationship between two variables. By examining the coefficient values, you can
infer something about the strength of the relationship between the two variables, and whether they
are positively correlated or negatively correlated.
C: Spearman’s correlation coefficient is designed for use with non-parametric and non-normally
distributed data. Spearman's coefficient is a nonparametric measure of statistical dependence
between two variables, and is sometimes denoted by the Greek letter rho. The Spearman’s
coefficient expresses the degree to which two variables are monotonically related. It is also called
Spearman rank correlation, because it can be used with ordinal variables.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/fisherlineardiscriminantanalysis
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/computelinearcorrelation
Related links:
https://www.dumpspass4sure.com/microsoft/dp-100-dumps.html
https://docs.microsoft.com/en-us/learn/certifications/exams/dp-100
