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NEW QUESTION: 1
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 scenario, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
Your company has a Microsoft Exchange Server 2019 hybrid deployment that contains two Mailbox servers named MBX1 and MBX2.
The company has the departments shown in the following table.

From the on-premises organization, outbound email is sent directly to the internet by using DNS lookups.
You are informed that some sales department users send email messages that are identified as spam.
You need to automatically block the sales department users from repeatedly sending spam.
Solution: You modify the outbound spam filter policy in Exchange Online.
Does this meet the goal?
A. Yes
B. No
Answer: B
Explanation:
Explanation
References:
https://docs.microsoft.com/en-us/office365/securitycompliance/configure-the-outbound-spam-policy

NEW QUESTION: 2
You plan to use automated machine learning to train a regression model. You have data that has features which have missing values, and categorical features with few distinct values.
You need to configure automated machine learning to automatically impute missing values and encode categorical features as part of the training task.
Which parameter and value pair should you use in the AutoMLConfig class?
A. exclude_nan_labels = True
B. enable_voting_ensemble = True
C. task = 'classification'
D. featurization = 'auto'
E. enable_tf = True
Answer: D
Explanation:
Featurization str or FeaturizationConfig
Values: 'auto' / 'off' / FeaturizationConfig
Indicator for whether featurization step should be done automatically or not, or whether customized featurization should be used.
Column type is automatically detected. Based on the detected column type preprocessing/featurization is done as follows:
Categorical: Target encoding, one hot encoding, drop high cardinality categories, impute missing values.
Numeric: Impute missing values, cluster distance, weight of evidence.
DateTime: Several features such as day, seconds, minutes, hours etc.
Text: Bag of words, pre-trained Word embedding, text target encoding.
Reference:
https://docs.microsoft.com/en-us/python/api/azureml-train-automl-client/azureml.train.automl.automlconfig.automlconfig

NEW QUESTION: 3
User jack, whose account is configured to use the korn shell, logs in and examines the value of his PATH environment variable:
jack@solaris: echo $PATH
/usr/gnu/bin:/usr/bin:/usr/sbin:/sbin
There is a shell script in jack's home directory called useradd:
-r-xr-xr-x 2 jack other 1239 2012-01-05 11:42 useradd
While in his home directory, jack attempts to run the script:
jack@solaris: useradd What will happen, and why?
A. He will get a "file not found" error, because his home directory is not in his search path.
B. The command /user/sbin/useradd will execute, because it is the first match in the search path.
C. He will get a "file not found" error, because the current directory is not in his seaech path.
D. The command /user/sbin/useradd will execute, because it is the last match in the search path.
E. The useradd script will execute, because jack is in the same directory that the script is located in.
Answer: D

NEW QUESTION: 4
Which data forms the basis for calculating the capacity requirement for an operation?
(Choose three.)
A. Operation control key (routing)
B. Transport time (routing)
C. In-house production time (material master)
D. Standard values (routing)
E. Formulas (work center)
Answer: A,D,E