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Career Opportunities, Roles, and Salary

After obtaining the Microsoft Certified: Azure Data Scientist Associate certification, you become eligible for the data scientist role. Also, with this certification, you can expect to earn a decent remuneration of about $97k on average annually, as stated by PayScale.com. The expected career paths for a data scientist also include senior data scientist with a salary potential of $126,778 and data science manager with a salary potential of $136,781. Another possible path is that of a data science director with a potential pay of $157,095 annually. However, remember that besides, such a certificate work experience also matters.

Microsoft DP-100 Exam Syllabus Topics:

Topic	Details
Topic 1	Create An Azure Data Science Environment Configure Data Science Work Environments
Topic 2	Resolve Anomalies, Outliers, And Other Data Inconsistencies Standardize Data Formats
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	Determine Ideal Split Based On The Nature Of The Data Determine Number Of Splits Identify Data Imbalances
Topic 6	Analyze And Recommend Tools That Meet System Requirements Set Up Development Environment

Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q256-Q261):

NEW QUESTION # 256
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 model to predict the price of a student's artwork depending on the following variables: the student's length of education, degree type, and art form.
You start by creating a linear regression model.
You need to evaluate the linear regression model.
Solution: Use the following metrics: Accuracy, Precision, Recall, F1 score and AUC.
Does the solution meet the goal?

• A. Yes
• B. No

Answer: B Explanation:
Those are metrics for evaluating classification models, instead use: Mean Absolute Error, Root Mean Absolute Error, Relative Absolute Error, Relative Squared Error, and the Coefficient of Determination.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/evaluate-model
NEW QUESTION # 257
You need to configure the Feature Based Feature Selection module based on the experiment requirements and datasets.
How should you configure the module properties? To answer, select the appropriate options in the dialog box in the answer area.
NOTE: Each correct selection is worth one point.
Answer: ** Explanation:

Explanation

Box 1: Mutual Information.
The mutual information score is particularly useful in feature selection because it maximizes the mutual information between the joint distribution and target variables in datasets with many dimensions.
Box 2: MedianValue
MedianValue is the feature column, , it is the predictor of the dataset.
Scenario: The MedianValue and AvgRoomsinHouse columns both hold data in numeric format. You need to select a feature selection algorithm to analyze the relationship between the two columns in more detail.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/filter-based-feature-selection
NEW QUESTION # 258**
You create an experiment in Azure Machine Learning Studio- You add a training dataset that contains 10.000 rows. The first 9.000 rows represent class 0 (90 percent). The first 1.000 rows represent class 1 (10 percent).
The training set is unbalanced between two Classes. You must increase the number of training examples for class 1 to 4,000 by using data rows. You add the Synthetic Minority Oversampling Technique (SMOTE) module to the experiment.
You need to configure the module.
Which values should you use? To answer, select the appropriate options in the dialog box in the answer area.
NOTE: Each correct selection is worth one point.
Answer: ** Explanation:

NEW QUESTION # 259**
You are creating a classification model for a banking company to identify possible instances of credit card fraud. You plan to create the model in Azure Machine Learning by using automated machine learning.
The training dataset that you are using is highly unbalanced.
You need to evaluate the classification model.
Which primary metric should you use?

• A. accuracy
• B. AUC.weighted
• C. normalizedmeanabsolute_error
• D. normalizedrootmeansquarederror
• E. [spearman_correlation

Answer: B Explanation:
Explanation
AUC_weighted is a Classification metric.
Note: AUC is the Area under the Receiver Operating Characteristic Curve. Weighted is the arithmetic mean of the score for each class, weighted by the number of true instances in each class.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-understand-automated-ml
NEW QUESTION # 260
You are creating a binary classification by using a two-class logistic regression model.
You need to evaluate the model results for imbalance.
Which evaluation metric should you use?

• A. Relative Absolute Error
• B. Relative Squared Error
• C. Mean Absolute Error
• D. AUC Curve

Answer: D Explanation:
One can inspect the true positive rate vs. the false positive rate in the Receiver Operating Characteristic (ROC) curve and the corresponding Area Under the Curve (AUC) value. The closer this curve is to the upper left corner, the better the classifier's performance is (that is maximizing the true positive rate while minimizing the false positive rate). Curves that are close to the diagonal of the plot, result from classifiers that tend to make predictions that are close to random guessing.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio/evaluate-model-performance#evaluating-a-binary-classification-model
NEW QUESTION # 261
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