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NEW QUESTION: 1
Which two are prerequisites for creating a standby database using Enterprise Manager cloud control?
(Choose two.)
A. A backup of the primary database must exist.
B. The primary database instance must be started using an SPFILE.
C. The primary database must be in archivelog mode.
D. The primary database must have flashback enabled.
E. The primary database must have FORCE LOGGING enabled.
F. The primary host and the proposed standby database host must run the same operating system.
Answer: C,E
Explanation:
Explanation/Reference:
Explanation:
A: Before you create a standby database you must first ensure the primary database is properly configured.
Place the primary database in FORCE LOGGING mode.
B: If archiving is not enabled, issue the following SQL statements to put the primary database in ARCHIVELOG mode and enable automatic archiving:
SQL> SHUTDOWN IMMEDIATE;
SQL> STARTUP MOUNT;
SQL> ALTER DATABASE ARCHIVELOG;
SQL> ALTER DATABASE OPEN;
References: https://docs.oracle.com/database/121/SBYDB/create_ps.htm#SBYDB4719

NEW QUESTION: 2
次のように定義された6つのデータポイントを含むPython NumPy配列を評価しています。
データ= [10、20、30、40、50、60]
Python Scikit-learn機械学習ライブラリのk-foldアルゴリズムの埋め込みを使用して、次の出力を生成する必要があります。
train: [10 40 50 60], test: [20 30]
train: [20 30 40 60], test: [10 50]
train: [10 20 30 50], test: [40 60]
出力を生成するには、相互検証を実装する必要があります。
どのようにコードセグメントを完成させるべきですか？回答するには、回答領域のダイアログボックスで適切なコードセグメントを選択します。
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: k-fold
Box 2: 3
K-Folds cross-validator provides train/test indices to split data in train/test sets. Split dataset into k consecutive folds (without shuffling by default).
The parameter n_splits ( int, default=3) is the number of folds. Must be at least 2.
Box 3: data
Example: Example:
>>>
>>> from sklearn.model_selection import KFold
>>> X = np.array([[1, 2], [3, 4], [1, 2], [3, 4]])
>>> y = np.array([1, 2, 3, 4])
>>> kf = KFold(n_splits=2)
>>> kf.get_n_splits(X)
2
>>> print(kf)
KFold(n_splits=2, random_state=None, shuffle=False)
>>> for train_index, test_index in kf.split(X):
print("TRAIN:", train_index, "TEST:", test_index)
X_train, X_test = X[train_index], X[test_index]
y_train, y_test = y[train_index], y[test_index]
TRAIN: [2 3] TEST: [0 1]
TRAIN: [0 1] TEST: [2 3]
References:
https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html

NEW QUESTION: 3
Which two appliances support logical routed interfaces within a virtual router? (Choose two. )
A. FP-4100-senes
B. FP-8000-series
C. Firepower services for ASA-5500-X
D. FP-7000-series
E. FP-9300 series
Answer: B,D

NEW QUESTION: 4
SensorDataコレクションを設計する必要があります。
何をお勧めしますか？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: Eventual
Traffic data insertion rate must be maximized.
Sensor data must be stored in a Cosmos DB named treydata in a collection named SensorData With Azure Cosmos DB, developers can choose from five well-defined consistency models on the consistency spectrum. From strongest to more relaxed, the models include strong, bounded staleness, session, consistent prefix, and eventual consistency.
Box 2: License plate
This solution reports on all data related to a specific vehicle license plate. The report must use data from the SensorData collection.
References:
https://docs.microsoft.com/en-us/azure/cosmos-db/consistency-levels