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NEW QUESTION: 1
Which statements are true regarding the default methods by which load-balancing takes place in the Interceptor appliance. (Select 2)
A. The Interceptor appliance can dynamically determine and direct traffic for optimization to the least-loaded Steelhead appliances based on Steelhead appliance resource usage
B. The Interceptor appliance uses hash assignment, for each TCP connection depending the hash value calculated from source and destination IP address, and is directed to a particular Steelhead appliance in the local cluster
C. The Interceptor appliance uses round robin, for each TCP connection, regardless of source or destination IP address, and is equally load-balanced between a group of Steelhead appliances
D. The Interceptor appliance uses peer affinity, a Steelhead appliance in the local cluster that has been previously paired with a remote Steelhead appliance is preferred
E. The Interceptor appliance uses peer affinity, a Steelhead appliance in the local cluster that has been previously paired with a destination IP address is preferred
F. The Interceptor appliance uses round robin, for each TCP connection, depending on source IP address, and is equally load-balanced between a group of Steelhead appliances
Answer: A,D
NEW QUESTION: 2
プロジェクトマネージャーは、非常に困難なクライアントとの大規模なプロジェクトに取り組んでいます。プロジェクトの過程で、プロジェクトマネージャーは、プロジェクトチームに小さな変更を加えるための時間と資金がもう少しあれば、クライアントは次のようなメリットを実感できることに気付きます。
プロジェクトの25％は早期に成果を上げます。
プロジェクトマネージャーはこれにどのように取り組むべきですか？
A. ビジネスケースを作成し、クライアントに提出します。
B. 現在のプロジェクトのマイルストーンに集中します。
C. すべての変更を加えてから、クライアントに結果を表示します。
D. この新しい作業のプロジェクト管理計画を更新します。
Answer: A
NEW QUESTION: 3
Under which of the following circumstances would you expect to see the highest rise in blood lactate?
A. During maximal exercise lasting between 60 and 180 seconds in the athlete
B. After moderate intensity cycling exercise in the client with Type 2 diabetes
C. Prior to treadmill walking in the cardiac patient
D. During maximal exercise lasting between 60 and 180 seconds in the untrained client
Answer: A
NEW QUESTION: 4
You work for a public transportation company and need to build a model to estimate delay times for multiple transportation routes. Predictions are served directly to users in an app in real time. Because different seasons and population increases impact the data relevance, you will retrain the model every month. You want to follow Google-recommended best practices. How should you configure the end-to-end architecture of the predictive model?
A. Write a Cloud Functions script that launches a training and deploying job on Ai Platform that is triggered by Cloud Scheduler
B. Use Cloud Composer to programmatically schedule a Dataflow job that executes the workflow from training to deploying your model
C. Use a model trained and deployed on BigQuery ML and trigger retraining with the scheduled query feature in BigQuery
D. Configure Kubeflow Pipelines to schedule your multi-step workflow from training to deploying your model.
Answer: D