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NEW QUESTION: 1
Which of these replication statements is true?
A. The access level given to a server in an ACL determines what, if any, changes that server can replicate to the replica.
B. The server's common name should be used for replication. This applies to all instances of server-to- server replication.
C. Access levels assigned to servers in a database ACL do not control what information the server can replicate within a database.
D. Database Authors assigned to a database control what information the server can replicate within a database.
Answer: A
Explanation:
Explanation/Reference:
Explanation:
NEW QUESTION: 2
Which of the following is true of the Navigator?
A. When searching for components, wildcards are not supported.
B. When a component is selected, dependent components are shown.
C. Components from different catalogs can be compared.
D. Library components (such as hierarchies) cannot be navigated.
Answer: B
NEW QUESTION: 3
A. Option C
B. Option B
C. Option A
D. Option D
Answer: C
NEW QUESTION: 4
You train a classification model by using a decision tree algorithm.
You create an estimator by running the following Python code. The variable feature_names is a list of all feature names, and class_names is a list of all class names.
from interpret.ext.blackbox import TabularExplainer
You need to explain the predictions made by the model for all classes by determining the importance of all features.
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation
Box 1: Yes
TabularExplainer calls one of the three SHAP explainers underneath (TreeExplainer, DeepExplainer, or KernelExplainer).
Box 2: Yes
To make your explanations and visualizations more informative, you can choose to pass in feature names and output class names if doing classification.
Box 3: No
TabularExplainer automatically selects the most appropriate one for your use case, but you can call each of its three underlying explainers underneath (TreeExplainer, DeepExplainer, or KernelExplainer) directly.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-machine-learning-interpretability-aml