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NEW QUESTION: 1

A. Option B
B. Option D
C. Option A
D. Option C
Answer: D

NEW QUESTION: 2
Your team uses an agile development approach.
You need to recommend a branching strategy for the team's Git repository. The strategy must meet the following requirements.
Provide the ability to work on multiple independent tasks in parallel.
Ensure that checked-in code remains in a releasable state always.
Ensure that new features can be abandoned at any time.
Encourage experimentation.
What should you recommend?
A. a single long-running branch
B. multiple long-running branches
C. a single fork per team member
D. a single-running branch with multiple short-lived topic branches
Answer: D
Explanation:
Topic branches, however, are useful in projects of any size. A topic branch is a short-lived branch that you create and use for a single particular feature or related work. This is something you've likely never done with a VCS before because it's generally too expensive to create and merge branches. But in Git it's common to create, work on, merge, and delete branches several times a day.
Reference:
https://git-scm.com/book/en/v2/Git-Branching-Branching-Workflows

NEW QUESTION: 3
The manager of a call center reports that staffing the center is difficult because the number of calls is unpredictable.
You have historical data that contains information about the calls.
You need to build an Azure Machine Learning experiment to predict the number of total calls each hour.
Which model should you use?
A. Poisson Regression
B. Boosted Decision Tree Regression
C. Multiclass Logistic Regression
D. Decision Forest Regression
Answer: A
Explanation:
Explanation/Reference:
Explanation:
Poisson regression is intended for use in regression models that are used to predict numeric values, typically counts. Therefore, you should use this module to create your regression model only if the values you are trying to predict fit the following conditions:
The response variable has a Poisson distribution.
Counts cannot be negative. The method will fail outright if you attempt to use it with negative labels.
A Poisson distribution is a discrete distribution; therefore, it is not meaningful to use this method with non- whole numbers.
Incorrect Answers:
A: Logistic regression is a well-known method in statistics that is used to predict the probability of an outcome, and is particularly popular for classification tasks. The algorithm predicts the probability of occurrence of an event by fitting data to a logistic function.
B: Boosting means that each tree is dependent on prior trees. The algorithm learns by fitting the residual of the trees that preceded it. Thus, boosting in a decision tree ensemble tends to improve accuracy with some small risk of less coverage.
C: Decision trees are non-parametric models that perform a sequence of simple tests for each instance, traversing a binary tree data structure until a leaf node (decision) is reached.
References: https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/poisson- regression

NEW QUESTION: 4
Which of the following processes are within the Initiating Process Group?
A. Develop Project Charter and Develop Project Scope Statement
B. Develop Project Charter and Identify Stakeholders
C. Develop Project Management Plan and Identify Stakeholders
D. Develop Project Management Plan and Plan Communications
Answer: B
Explanation:
Explanation/Reference:
Explanation:
Initiating Process Group
4.1 Develop Project Charter
13.1 Identify Stakeholders