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NEW QUESTION: 1
Observation sessions should be scheduled so that the representativeness of the data can be maximized.
Considering behaviors occurring in a school setting, which of the following options is consistent with this
recommendation?
A. Observations should be made only when the behavior is likely to occur most frequently during a day,
for instance, during a particular academic class.
B. Behaviors are given letter-codes that represent the complete topographical definition of the behaviors,
e.g., "on-task academic behavior" is represented by "O" on the recording sheet.
C. Representativeness can be assured by scheduling observations in sessions of 60 minutes or more in
the morning and afternoon school schedule.
D. Recordings of behavior during the whole school day may be needed initially to assess the
representativeness of samples within the day.
Answer: D
NEW QUESTION: 2
RMAN> BACKUP VALIDATE DATABASE;
RMAN> RECOVER CORRUPTION LIST;
A. Option A
B. Option C
C. Option D
D. Option B
Answer: B
NEW QUESTION: 3
You have an Azure Stream Analytics job that receives clickstream data from an Azure event hub.
You need to define a query in the Stream Analytics job. The query must meet the following requirements:
* Count the number of clicks within each 10-second window based on the country of a visitor.
* Ensure that each click is NOT counted more than once.
How should you define the query?
A. SELECT Country, Avg(*) AS Average
FROM ClickStream TIMESTAMP BY CreatedAt
GROUP BY Country, SlidingWindow(second, 10)
B. SELECT Country, Count(*) AS Count
FROM ClickStream TIMESTAMP BY CreatedAt
GROUP BY Country, TumblingWindow(second, 10)
C. SELECT Country, Count(*) AS Count
FROM ClickStream TIMESTAMP BY CreatedAt
GROUP BY Country, SessionWindow(second, 5, 10)
D. SELECT Country, Avg(*) AS Average
FROM ClickStream TIMESTAMP BY CreatedAt
GROUP BY Country, HoppingWindow(second, 10, 2)
Answer: B
Explanation:
Tumbling window functions are used to segment a data stream into distinct time segments and perform a function against them, such as the example below. The key differentiators of a Tumbling window are that they repeat, do not overlap, and an event cannot belong to more than one tumbling window.
Example:
Reference:
https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-window-functions