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NEW QUESTION: 1
Which among the following acts as a trigger for risk response process?
A. Risk level increases above risk appetite
B. Risk level equates the risk tolerance
C. Risk level equates risk appetite
D. Risk level increase above risk tolerance
Answer: D
Explanation:
Explanation/Reference:
Explanation:
The risk response process is triggered when a risk exceeds the enterprise's risk tolerance level. The acceptable variation relative to the achievement of an objective is termed as risk tolerance. In other words, risk tolerance is the acceptable deviation from the level set by the risk appetite and business objectives.
Risk tolerance is defined at the enterprise level by the board and clearly communicated to all stakeholders.
A process should be in place to review and approve any exceptions to such standards.
Incorrect Answers:
A, C: Risk appetite level is not relevant in triggering of risk response process. Risk appetite is the amount of risk a company or other entity is willing to accept in pursuit of its mission. This is the responsibility of the board to decide risk appetite of an enterprise. When considering the risk appetite levels for the enterprise, the following two major factors should be taken into account:
The enterprise's objective capacity to absorb loss, e.g., financial loss, reputation damage, etc.

The culture towards risk taking-cautious or aggressive. In other words, the amount of loss the

enterprise wants to accept in pursue of its objective fulfillment.
D: Risk response process is triggered when the risk level increases the risk tolerance level of the enterprise, and not when it just equates the risk tolerance level.

NEW QUESTION: 2
Which Big SQL statement can be used to store a single row into an HBase table?
A. LOAD HIVE DATA
B. LOAD USING ... INTO HBASE TABLE
C. CREATE HBASE TABLE
D. INSERT INTO HBASE
Answer: D
Explanation:
Reference:
https://www.ibm.com/support/knowledgecenter/en/SSPT3X_3.0.0/ com.ibm.swg.im.infosphere.biginsights.bigsql.doc/doc/bsql_insert.html

NEW QUESTION: 3
You are producing a multiple linear regression model in Azure Machine Learning Studio.
Several independent variables are highly correlated.
You need to select appropriate methods for conducting effective feature engineering on all the data.
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.

Answer:
Explanation:

Explanation:
Step 1: Use the Filter Based Feature Selection module
Filter Based Feature Selection identifies the features in a dataset with the greatest predictive power.
The module outputs a dataset that contains the best feature columns, as ranked by predictive power. It also outputs the names of the features and their scores from the selected metric.
Step 2: Build a counting transform
A counting transform creates a transformation that turns count tables into features, so that you can apply the transformation to multiple datasets.
Step 3: Test the hypothesis using t-Test
References:
https://docs.microsoft.com/bs-latn-ba/azure/machine-learning/studio-module-reference/filter-based-feature-selection
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/build-counting-transform