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NEW QUESTION: 1
Your client wants to minimize the validation rules that execute against a time card and only under certain circumstances they want certain validation rules, if required, to display validation messages.
Which option will accomplish your customer's goals?
A. Create a formula that incorporates program logic to check for certain time card entries and, when present, execute the validation rule. Create the new template and rule. Place the rule that uses the formula in a rule set and assign the rule set using an appropriate profile.
B. Create a new validation formula/template and rule that accesses HCM data to only execute if certain HR data exists for the worker and processes the validation logic on the time entries.
Place the rule that uses the formula in a rule set and assign the rule set using an appropriate profile.
C. Create a new formula/template and rule that uses an input parameter to check for certain values defined in the rule using a time category input parameter. When present, complete execution of the rule. Place the rule that uses the formula in a rule set and assign the rule set using an appropriate profile.
D. Place the validation rules in a rule set, assigning a properly formatted time category on the same rows in the rule set. Assign the rule set to the workers using an appropriate profile.
Answer: D

NEW QUESTION: 2
モデル開発戦略を実装して、ユーザーが広告に反応する傾向を判断する必要があります。
どのテクニックを使用する必要がありますか？
A. 行の分割モジュールを使用して、イベントまでの移動距離に基づいてデータを分割します。
B. 相対式分割モジュールを使用して、イベントまでの移動距離に基づいてデータを分割します。
C. 行の分割モジュールを使用して、重心距離に基づいてデータを分割します。
D. 相対式分割モジュールを使用して、重心距離に基づいてデータを分割します。
Answer: D
Explanation:
Explanation
Split Data partitions the rows of a dataset into two distinct sets.
The Relative Expression Split option in the Split Data module of Azure Machine Learning Studio is helpful when you need to divide a dataset into training and testing datasets using a numerical expression.
Relative Expression Split: Use this option whenever you want to apply a condition to a number column. The number could be a date/time field, a column containing age or dollar amounts, or even a percentage. For example, you might want to divide your data set depending on the cost of the items, group people by age ranges, or separate data by a calendar date.
Scenario:
Local market segmentation models will be applied before determining a user's propensity to respond to an advertisement.
The distribution of features across training and production data are not consistent References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/split-data

NEW QUESTION: 3
HOTSPOT


Answer:
Explanation:

Explanation:

An example of enabling ExtranetLockout feature with maximum of 10 number of bad password attempts and 30 mins soft-lockout duration is as follows:
Set-AdfsProperties-EnableExtranetLockout $true -ExtranetLockoutThreshold 10 - ExtranetObservationWindow (new-timespan -Minutes 30) References: https://blogs.msdn.microsoft.com/luzhao1/2015/06/24/demystify-extranet- lockout-feature-in-ad-fs-3-0/