Microsoft Certified Azure Data Scientist Associate, the DP-100 exam measures your ability to accomplish technical tasks like:
- manage Azure resources for machine learning
- run experiments and train models
- deploy and operationalize machine learning solutions
- implement responsible machine learning
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).
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
You are using C-Support Vector classification to do a multi-class classification with an unbalanced training dataset. The C-Support Vector classification using Python code shown below:
from sklearn.svm import SVC import numpy as np svc=SVC(kernel='linear',class_weight='balanced',C=1.0, random_state=1) model=svc.fit(X_train,y)
Which evaluation statement should you use? To answer, select the appropriate options in the answer area.
- Automatically select the performance metrics for classification
- Automatically adjust weights directly proportional to class frequencies in the input data.
- Automatically adjust weights inversely proportional to class frequencies in the input
- Penalty parameter
- Degree of polynominal kernel function
- Size of the kernel cache
You need to modify the inputs for the global penalty event model to address the bias and variance issue. Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
- Built ratios
- Bin the new data
- Add a K-Means clustering module with 10 clusters
- Select the behavior data
- Select the location data
- Perform a Primary Component Analysis (PCA)
You need to define a process for penalty event detection. Which three actions should you perform in sequence? To answer, move the appropriate actions from
the list of actions to the answer area and arrange them in the correct order.
- Standardize to mono audio clips
- Vary the lenght of sliding windows between modeling epochs
- Vary the lenght of frequency bands between modeling epochs
- Use inverse Fourier transform on frequency changes over time
- Use Fast Fourier transform on frequency changes over time
- Standardize to stereo audio clips
You need to define an evaluation strategy for the crowd sentiment models. Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
- Add new features for retraining supervised models
- Filter labeled cases for retraining using the shortest distance from centroids
- Evaluate the changes in correlation between model error rate and centroid distance
- Impute unavailable features with centroid aligned models
- Filter labeled cases for retraining using the longest distance from centroids
- Remove features before retraining supervised models
You need to implement a scaling strategy for the local penalty detection data. Which normalization type should you use?
You need to implement a feature engineering strategy for the crowd sentiment local models. What should you do?
- Apply an analysis of variance (ANOVA).
- Apply a Pearson correlation coefficient.
- Apply a Spearman correlation coefficient.
- Apply a linear discriminant analysis.