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NEW QUESTION: 1
次のAzure Stream Analyticsクエリがあります。

次の各文について、文が真の場合は「はい」を選択します。 それ以外の場合は、[いいえ]を選択します。
注：それぞれの正しい選択には1ポイントの価値があります。

Answer:
Explanation:
Explanation

Box 1: Yes
You can now use a new extension of Azure Stream Analytics SQL to specify the number of partitions of a stream when reshuffling the data.
The outcome is a stream that has the same partition scheme. Please see below for an example:
WITH step1 AS (SELECT * FROM [input1] PARTITION BY DeviceID INTO 10),
step2 AS (SELECT * FROM [input2] PARTITION BY DeviceID INTO 10)
SELECT * INTO [output] FROM step1 PARTITION BY DeviceID UNION step2 PARTITION BY DeviceID Note: The new extension of Azure Stream Analytics SQL includes a keyword INTO that allows you to specify the number of partitions for a stream when performing reshuffling using a PARTITION BY statement.
Box 2: Yes
When joining two streams of data explicitly repartitioned, these streams must have the same partition key and partition count.
Box 3: Yes
Streaming Units (SUs) represents the computing resources that are allocated to execute a Stream Analytics job. The higher the number of SUs, the more CPU and memory resources are allocated for your job.
In general, the best practice is to start with 6 SUs for queries that don't use PARTITION BY.
Here there are 10 partitions, so 6x10 = 60 SUs is good.
Note: Remember, Streaming Unit (SU) count, which is the unit of scale for Azure Stream Analytics, must be adjusted so the number of physical resources available to the job can fit the partitioned flow. In general, six SUs is a good number to assign to each partition. In case there are insufficient resources assigned to the job, the system will only apply the repartition if it benefits the job.
Reference:
https://azure.microsoft.com/en-in/blog/maximize-throughput-with-repartitioning-in-azure-stream-analytics/
https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-streaming-unit-consumption

NEW QUESTION: 2
The Chief Information Officer (CIO) is concerned with moving an application to a SaaS cloud provider. Which of the following can be implemented to provide for data confidentiality assurance during and after the migration to the cloud?
A. TPM technology
B. HPM technology
C. Full disk encryption
D. DLP policy
Answer: D
Explanation:
Data loss prevention (DLP) systems monitor the contents of systems (workstations, servers, and networks) to make sure that key content is not deleted or removed. They also monitor who is using the data (looking for unauthorized access) and transmitting the data. The Software as a Service (SaaS) applications are remotely run over the Web and as such requires DLP monitoring.

NEW QUESTION: 3
Which ROC curve represents a perfect model fit?
A:

B:

C:

D:

A. Option C
B. Option B
C. Option D
D. Option A
Answer: D