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NEW QUESTION: 1
Which PowerShell commandlet should a Citrix Administrator use to enable event logging for the authentication service, stores and Receiver for Web?
A. Set-DSTraceLevel -All -TraceLevel Warning
B. Set-DSTraceLevel -All -TraceLevel Info
C. Set-DSTraceLevel -All -TraceLevel Verbose
D. Set-DSTraceLevel -All -TraceLevel Off
Answer: C
NEW QUESTION: 2
Which three are true about Smart Flash log?
A. LGWR will not wait for writes to Smart Flash log if the write to a disk based log file completes first.
B. Smart Flash Log is enabled by default, using 1024 MB of Rash storage on each storage server.
C. I/O Manager category plans can be used to enable or disable Smart Flash Log For different I/O categories.
D. I/O Resource Manager database plans can be used to enable or disable Smart Flash Log for different databases.
E. You can remove Smart Flash Log from a single storage server with the drop flashing command.
Answer: A,D,E
Explanation:
Explanation/Reference:
A: the Exadata I/O Resource Manager (IORM) has been enhanced to enable or disable Smart Flash Logging for the different databases running on the Database Machine.
B: Smart Flash Logging works as follows. When receiving a redo log write request, Exadata will do parallel writes to the on-disk redo logs as well as a small amount of space reserved in the flash hardware. When either of these writes has successfully completed the database will be immediately notified of completion. If the disk drives hosting the logs experience slow response times, then the Exadata Smart Flash Cache will provide a faster log write response time.
Conversely, if the Exadata Smart Flash Cache is temporarily experiencing slow response times (e.g., due to wear leveling algorithms), then the disk drive will provide a faster response time.
This algorithm will significantly smooth out redo write response times and provide overall better database performance.
D: Category plans are configured and enabled using the CellCLI utility on the cell. Only one category plan can be enabled at a time
Incorrect:
Not C: By default, 512 MB of the Exadata flash is allocated to Smart Flash Logging
NEW QUESTION: 3
You are developing a web page that performs CPU-intensive calculations. A web worker processes these calculations on a separate background thread. The process is instantiated from the web page.
You need to stop the web worker process after the calculations are completed.
Which two actions can you perform to achieve this goal? (Each correct answer presents a complete solution. Choose two.)
A. From the web worker process, call the close() method.
B. From the web page, call the close() method.
C. From the web worker process, call the terminate() method.
D. From the web page, call the terminate() method.
Answer: A,D
Explanation:
Terminating a worker
If you need to immediately terminate a running worker, you can do so by calling the worker's terminate() method:
myWorker.terminate();
The worker thread is killed immediately without an opportunity to complete its operations or clean up after itself.
Workers may close themselves by calling their own close method:
close();
Reference: Using Web Workers
NEW QUESTION: 4
You have a model with a large difference between the training and validation error values.
You must create a new model and perform cross-validation.
You need to identify a parameter set for the new model using Azure Machine Learning Studio.
Which module you should use for each step? To answer, drag the appropriate modules to the correct steps. Each module may be used once or more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: Split data
Box 2: Partition and Sample
Box 3: Two-Class Boosted Decision Tree
Box 4: Tune Model Hyperparameters
Integrated train and tune: You configure a set of parameters to use, and then let the module iterate over multiple combinations, measuring accuracy until it finds a "best" model. With most learner modules, you can choose which parameters should be changed during the training process, and which should remain fixed.
We recommend that you use Cross-Validate Model to establish the goodness of the model given the specified parameters. Use Tune Model Hyperparameters to identify the optimal parameters.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/partition-and-sample
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