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NEW QUESTION: 1
You notice a performance change in your production Oracle database and you want to know which change has made
this performance difference.
You generate the Compare Period Automatic Database Diagnostic Monitor (ADDM) report to further investigation.
Which three findings would you get from the report?
A. It shows the resource usage for CPU, memory, and I/O in both time periods.
B. It gives information about statistics collection in both time periods.
C. It detects the top wait events causing performance degradation.
D. It identifies any workload change that caused a performance difference in both time periods.
E. It detects any configuration change that caused a performance difference in both time periods.
F. It shows the difference in the size of memory pools in both time periods.
Answer: A,D,E
Explanation:
Keyword: shows the difference.
* Full ADDM analysis across two AWR snapshot periods
Detects causes, measure effects, then correlates them
Causes: workload changes, configuration changes
Effects: regressed SQL, reach resource limits (CPU, I/O, memory, interconnect)
Makes actionable recommendations along with quantified impact
* Identify what changed
/ Configuration changes, workload changes
* Performance degradation of the database occurs when your database was performing optimally in the past, such as
6 months ago, but has gradually degraded to a point where it becomes noticeable to the users. The Automatic
Workload Repository (AWR) Compare Periods report enables you to compare database performance between two
periods of time.
While an AWR report shows AWR data between two snapshots (or two points in time), the AWR Compare Periods
report shows the difference (ABE) between two periods (or two AWR reports with a total of four snapshots). Using the
AWR Compare Periods report helps you to identify detailed performance attributes and configuration settings that
differ between two time periods.
References:

NEW QUESTION: 2
アップロードされた画像を受け入れるインタラクティブなWebサイトを構築し、各画像に基づいて一連の定義済みの質問をする必要があります。
どのサービスを使用する必要がありますか？回答するには、回答エリアで適切なオプションを選択します。
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: Azure Bot Service
Box 2: Computer Vision
The Computer Vision Analyze an image feature, returns information about visual content found in an image. Use tagging, domain-specific models, and descriptions in four languages to identify content and label it with confidence. Use Object Detection to get location of thousands of objects within an image. Apply the adult/racy settings to help you detect potential adult content. Identify image types and color schemes in pictures.
References:
https://azure.microsoft.com/en-us/services/cognitive-services/computer-vision/

NEW QUESTION: 3
From the variance F-test shown above, which of these conclusions is/are valid?
A. There are not enough data points to make any statistical conclusions
B. This test applies only to Non-normal Data at 99 % confidence
C. The variance between the class score distribution is not significantly different
D. This test applies only to Normal Distributed data at 99 % confidence
E. The variance between the class score distribution is significantly different
Answer: E

NEW QUESTION: 4
緊急道路対応車両が自動的に発送されるようにする必要があります。
処理システムをどのように設計する必要がありますか？回答するには、回答エリアで適切なオプションを選択します。
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:
Box1: API App

* Events generated from the IoT data sources are sent to the stream ingestion layer through Azure HDInsight Kafka as a stream of messages. HDInsight Kafka stores streams of data in topics for a configurable of time.
* Kafka consumer, Azure Databricks, picks up the message in real time from the Kafka topic, to process the data based on the business logic and can then send to Serving layer for storage.
* Downstream storage services, like Azure Cosmos DB, Azure SQL Data warehouse, or Azure SQL DB, will then be a data source for presentation and action layer.
* Business analysts can use Microsoft Power BI to analyze warehoused data. Other applications can be built upon the serving layer as well. For example, we can expose APIs based on the service layer data for third party uses.
Box 2: Cosmos DB Change Feed
Change feed support in Azure Cosmos DB works by listening to an Azure Cosmos DB container for any changes. It then outputs the sorted list of documents that were changed in the order in which they were modified.
The change feed in Azure Cosmos DB enables you to build efficient and scalable solutions for each of these patterns, as shown in the following image:

References:
https://docs.microsoft.com/bs-cyrl-ba/azure/architecture/example-scenario/data/realtime-analytics-vehicle-iot?view=azurermps-4.4.1