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NEW QUESTION: 1
A developer is using the WebSphere Server application Developer Tools to convert their existing JPA project to an OSGi JPA bundle. The original JPA project has a persistence.xml file that is by default located in the src/META-INF folder of the project.
The developer invokes the action to convert the JPA project to an OSGi bundle project.
Which changes are expected to be performed by the tools to the original project when it is converted to an OSGi bundle?
A. The MANIFEST.MF file is generated under a new META-INF folder in the root of the
project. The following entry is added to the MANIFEST.MF file:
Meta-Persistence:
B. The MANIFEST.MF file is generated in the BundleContext/META-INF folder. The
persistence.xml is moved from the src folder to the same location as the MANIFEST.MF.
C. The MANIFEST.MF file is generated in the src/META-INF folder which contains the
persistence.xml file. The following entry is added to the MANIFEST.MF file:
Meta-Persistence:
D. The MANIFEST.MF file is generated under a new META-INF folder without a
Meta-Persistence:
Entry
Answer: C
NEW QUESTION: 2
Refer to the exhibit.
Which three options are potential reasons the FXO port is not receiving Caller ID? (Choose three.)
A. The NTFY message contains a Hung Up parameter.
B. "Enable Caller ID" was not configured on the Cisco Unified Communications Manager configuration.
C. The Timing Guard-out parameter is incorrectly set to 1500 ms.
D. The FXO port was configured to "loop-start" instead of "ground-start".
E. Gateway with IOS 12.4(24T) was used and does not support this feature.
F. "Connection polar opx immediate" was used and does support caller ID.
Answer: B,C,E
Explanation:
Explanation/Reference:
Explanation:
NEW QUESTION: 3
技術要件を満たすために、リアルタイム処理のためにどのカウンタを監視する必要がありますか？
A. 同時ユーザー
B. SU％使用率
C. データ変換エラー
Answer: B
Explanation:
Scenario:
Real-time processing must be monitored to ensure that workloads are sized properly based on actual usage patterns.
The sales data including the documents in JSON format, must be gathered as it arrives and analyzed online by using Azure Stream Analytics.
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. This capacity lets you focus on the query logic and abstracts the need to manage the hardware to run your Stream Analytics job in a timely manner.
References: