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NEW QUESTION: 1
In your multitenant container database (CDB) containing pluggable databases (PDB), users complain about performance degradation.
How does real-time Automatic database Diagnostic Monitor (ADDM) check performance degradation and provide solutions?
A. It collects data from both SGA and PGA, analyzes it, and provides a report.
B. It collects data from SGA and compares it with the latest snapshot.
C. It collects data from SGA, analyzes it, and provides a report.
D. It collects data from SGA and compares it with a preserved snapshot.
Answer: C
Explanation:
Note:
*The multitenant architecture enables an Oracle database to function as a multitenant container database (CDB) that includes zero, one, or many customer-created pluggable databases (PDBs). A PDB is a portable collection of schemas, schema objects, and nonschema objects that appears to an Oracle Net client as a non-CDB. All Oracle databases before Oracle Database 12c were non-CDBs.
*The System Global Area (SGA) is a group of shared memory areas that are dedicated to an Oracle "instance" (an instance is your database programs and RAM).
*The PGA (Program or Process Global Area) is a memory area (RAM) that stores data and control information for a single process.

NEW QUESTION: 2
Azure Machine Learningのハイパードライブ機能を使用して、モデルをトレーニングするときに最適なハイパーパラメーター値を決定することを計画しています。
次のハイパーパラメータ値の組み合わせを試すには、ハイパードライブを使用する必要があります。早期終了ポリシーを適用してはなりません。
Learning_rate：0.001〜0.1の任意の値
* batch_size：16、32、または64
ハイパードライブ実験のサンプリング方法を構成する必要がありますどの2つのサンプリング方法を使用できますか？それぞれの正解は完全な解決策です。
注：正しい選択はそれぞれ1ポイントの価値があります。
A. サンプリングなし
B. グリッドサンプリング
C. ランダムサンプリング
D. ベイジアンサンプリング
Answer: C,D
Explanation:
Explanation
C: Bayesian sampling is based on the Bayesian optimization algorithm and makes intelligent choices on the hyperparameter values to sample next. It picks the sample based on how the previous samples performed, such that the new sample improves the reported primary metric.
Bayesian sampling does not support any early termination policy
Example:
from azureml.train.hyperdrive import BayesianParameterSampling
from azureml.train.hyperdrive import uniform, choice
param_sampling = BayesianParameterSampling( {
"learning_rate": uniform(0.05, 0.1),
"batch_size": choice(16, 32, 64, 128)
}
)
D: In random sampling, hyperparameter values are randomly selected from the defined search space. Random sampling allows the search space to include both discrete and continuous hyperparameters.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters

NEW QUESTION: 3
There is a request to add two new disks to an existing VM residing on a VxRail vSAN that you manage One disk is 250 GB and the other is 500 GB The VM is using the vSAN default storage policy.
How many vSAN objects will be created?
A. 0
B. 1
C. 2
D. 3
Answer: B

NEW QUESTION: 4
In which technique does an application maintain limited functionality, even when some of the modules or supporting services of that application become unavailable?
A. Retry logic
B. Persistent state model
C. Graceful degradation
D. Checkpointing
Answer: D