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NEW QUESTION: 1
次のストアドプロシージャがあります。
ストアドプロシージャの実行には予想以上に時間がかかります。
ストアード・プロシージャーの実行計画を検討しているときに、クラスタード・インデックス・スキャン演算子に関する以下の述部が見つかりました。
パフォーマンスの問題を解決する必要があります。
あなたは何をするべきか？
A. Add a NOLOCK query hint to the SELECT statement.
B. Convert the table to a memory-optimized table.
C. Add a FORCESEEK query hint to the SELECT statement.
D. Change the @FileName parameter from nvarchar(512) to varchar(512).
E. Change the FileName column from varchar(512) to nvarchar(512).
Answer: D
Explanation:
When using a variable, make sure that the datatype matches the column's datatype. We suspect that the issue is that the variable is NVARCHAR (512) whilst the table column is VARCHAR (512). This is indicated by the CONVERT_IMPLICIT operator in the execution plan.
References: https://sqlserverperformance.wordpress.com/2009/02/02/beware-of-convert_implicit-in-a-sql-execution-plan/
NEW QUESTION: 2
Click the Exhibit button.
Referring to the exhibit, which LDP Layer 2 circuit configuration on PE1 maps traffic to an LSP destined to the secondary loopback address of PE2?
A. [edit protocols l2circuit]
user@PE1# show
neighbor 10.10.10.2 {
interface ge-1/1/1.1 {
psn-tunnel-endpoint 10.10.10.2;
virtual-circuit-id 1;
encapsulation-type ethernet-vlan;
}
}
B. [edit protocols l2circuit]
user@PE1# show
neighbor 10.10.10.1 {
interface ge-1/1/1.1 {
psn-tunnel-endpoint 10.10.10.2;
virtual-circuit-id 1;
encapsulation-type ethernet-vlan;
}
}
C. [edit protocols l2circuit]
user@PE1# show
neighbor 10.10.10.2 {
interface ge-1/1/1.1 {
virtual-circuit-id 1;
encapsulation-type ethernet-vlan;
}
}
D. [edit protocols l2circuit]
user@PE1# show
neighbor 10.10.10.2 {
interface ge-1/1/1.1 {
psn-tunnel-endpoint 10.10.10.1;
virtual-circuit-id 1;
encapsulation-type ethernet-vlan;
}
}
Answer: A
NEW QUESTION: 3
英語のテキストコンテンツをフランス語のテキストコンテンツに翻訳するための機械学習モデルを構築しています。
テキストコンテンツのシーケンスを学習するには、機械学習モデルを構築してトレーニングする必要があります。
どのタイプのニューラルネットワークを使用する必要がありますか？
A. Generative Adversarial Networks (GANs)
B. Convolutional Neural Networks (CNNs)
C. Multilayer Perceptions (MLPs)
D. Recurrent Neural Networks (RNNs)
Answer: D
Explanation:
Explanation
To translate a corpus of English text to French, we need to build a recurrent neural network (RNN).
Note: RNNs are designed to take sequences of text as inputs or return sequences of text as outputs, or both.
They're called recurrent because the network's hidden layers have a loop in which the output and cell state from each time step become inputs at the next time step. This recurrence serves as a form of memory. It allows contextual information to flow through the network so that relevant outputs from previous time steps can be applied to network operations at the current time step.
References:
https://towardsdatascience.com/language-translation-with-rnns-d84d43b40571