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NEW QUESTION: 1
For a nonissuer, a control deficiency would be considered a material weakness when the likelihood and
magnitude of potential financial statement misstatements are:
A. Option B
B. Option A
C. Option C
D. Option D
Answer: B
Explanation:
Explanation/Reference:
Explanation:
Choice "A" is correct. A material weakness is a significant deficiency, or combination of significant
deficiencies, that results in more than a remote likelihood that a material misstatement of the entity's
financial statements will not be prevented or detected.
Choices "B", "C", and "D" are incorrect, based on the Explanation: above.

NEW QUESTION: 2
You are creating an experiment by using Azure Machine Learning Studio.
You must divide the data into four subsets for evaluation. There is a high degree of missing values in the data. You must prepare the data for analysis.
You need to select appropriate methods for producing the experiment.
Which three modules should you run in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
NOTE: More than one order of answer choices is correct. You will receive credit for any of the correct orders you select.

Answer:
Explanation:

Explanation:
The Clean Missing Data module in Azure Machine Learning Studio, to remove, replace, or infer missing values.
Incorrect Answers:
Latent Direchlet Transformation: Latent Dirichlet Allocation module in Azure Machine Learning Studio, to group otherwise unclassified text into a number of categories. Latent Dirichlet Allocation (LDA) is often used in natural language processing (NLP) to find texts that are similar. Another common term is topic modeling.
Build Counting Transform: Build Counting Transform module in Azure Machine Learning Studio, to analyze training data. From this data, the module builds a count table as well as a set of count-based features that can be used in a predictive model.
Missing Value Scrubber: The Missing Values Scrubber module is deprecated.
Feature hashing: Feature hashing is used for linguistics, and works by converting unique tokens into integers.
Replace discrete values: the Replace Discrete Values module in Azure Machine Learning Studio is used to generate a probability score that can be used to represent a discrete value. This score can be useful for understanding the information value of the discrete values.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/clean-missing-data

NEW QUESTION: 3

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