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NEW QUESTION: 1
You have a database that contains the following tables: tblEmployees and tblSalesSummary. Each record contains approximately one million records.
You use Microsoft SQL Server Management Studio (SSMS) to run two queries. The Include Actual Execution Plan option is enabled.
Both queries return the same results. SSMS generates the execution plans shown in the exhibit. (Click the Exhibit button.) You need to troubleshoot the queries.
How should you interpret the execution plans? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.


Answer:
Explanation:

Explanation

References:
https://docs.microsoft.com/en-us/sql/relational-databases/showplan-logical-and-physical-operators-reference?vie

NEW QUESTION: 2
Elastic Load Balancer（ELB）を活用するWebアプリケーションがあります。 Auto Scaling Groupを使用して展開されたWebサーバーの前で、データベースはリレーショナルデータベースサービス（RDS）で実行されています。アプリケーションは一般的に技術記事とそれらへの応答を提供し、記事に対する応答よりも記事のビューが多くあります。場合によっては、サイト上の記事が非常に人気になり、トラフィックが大幅に増加してサイトがダウンすることがあります。
これらのイベント中に可用性を維持しながら、インフラストラクチャへの圧力を軽減するために何ができますか？ （3つ選択してください。）
A. 最も頻繁に使用されるデータをキャッシュするためにElastiCacheを活用します。
B. リレーショナルデータベースへの読み取りトラフィック用のRDS読み取りレプリカを追加します
C. 記事の配信にCloudFrontを活用します。
D. SOSを使用して、技術投稿のリクエストをキューに入れ、それらをキューから配信します。
E. Route53ヘルスチェックを使用して、エラーページのS3バケットにフェールオーバーします。
Answer: A,B,C

NEW QUESTION: 3
Which two statements about Layer 2 switches are true? (Choose two.)
A. They process the source MAC addresses of frames they receive.
B. They process the destination MAC addresses of frames they receive.
C. They process the destination IP addresses of frames they receive.
D. They make forwarding decisions based on the source MAC addresses.
Answer: A,B

NEW QUESTION: 4
You plan to use the Hyperdrive feature of Azure Machine Learning to determine the optimal hyperparameter values when training a model.
You must use Hyperdrive to try combinations of the following hyperparameter values. You must not apply an early termination policy.
learning_rate: any value between 0.001 and 0.1
* batch_size: 16, 32, or 64
You need to configure the sampling method for the Hyperdrive experiment Which two sampling methods can you use? Each correct answer is a complete solution.
NOTE: Each correct selection is worth one point.
A. Bayesian sampling
B. No sampling
C. Grid sampling
D. Random sampling
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.
Incorrect Answers:
B: Grid sampling can be used if your hyperparameter space can be defined as a choice among discrete values and if you have sufficient budget to exhaustively search over all values in the defined search space. Additionally, one can use automated early termination of poorly performing runs, which reduces wastage of resources.
Example, the following space has a total of six samples:
from azureml.train.hyperdrive import GridParameterSampling
from azureml.train.hyperdrive import choice
param_sampling = GridParameterSampling( {
"num_hidden_layers": choice(1, 2, 3),
"batch_size": choice(16, 32)
}
)
Answer: A,D
Explanation:
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters