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NEW QUESTION: 1
Solutions Architectは、複数のAPIゲートウェイ、AWS Lambda関数、Amazon S3バケット、AmazonDynamoDBテーブルで構成されるサーバーレスアプリケーションを管理します。顧客は、動的イメージのロード中にいくつかのアプリケーションコンポーネントが遅くなり、「504ゲートウェイタイムアウト」エラーでタイムアウトするものもあると言います。シナリオのトラブルシューティング中に、ソリューションアーキテクトはDynamoDBモニタリングメトリックが許容レベルにあることを確認します。
これらのアプリケーションの問題をデバッグするのに最適な手順は次のうちどれですか？ （2つ選択してください。）
A. VPCフローログを解析して、Lambda関数とS3の間にパケット損失があるかどうかを判断します。
B. Amazon API GatewayのHTTPログでHTTPエラーを解析して、エラーの根本原因を特定します。
C. Amazon CloudWatch Logsを解析して、指定された間隔でリクエストされた画像の処理時間を決定します。
D. S3アクセスログを解析して、アクセスされているオブジェクトが特定のIPアドレスからのものであるかどうかを判断し、地理的な遅延の問題に範囲を絞り込みます。
E. AWS X-Rayトレースを解析し、HTTPメソッドを分析して、HTTPエラーの根本原因を特定します。
Answer: C,E
Explanation:
Firstly "A 504 Gateway Timeout Error means your web server didn't receive a timely response from another server upstream when it attempted to load one of your web pages. Put simply, your web servers aren't communicating with each other fast enough". This specific issue is addressed in the AWS article "Tracing, Logging and Monitoring an API Gateway API". https://docs.amazonaws.cn/en_us/apigateway/latest/developerguide/monitoring_overview.html

NEW QUESTION: 2
A customer plans to deploy Microsoft SharePoint Server.
Which two additional products should be licensed for the deployment? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
A. Windows Server
B. Microsoft SQL Server
C. Microsoft Project Server
D. Microsoft System Center
Answer: A,B

NEW QUESTION: 3
Which of these are examples of a value in a sparse vector? (Select 2 answers.)
A. [0, 0, 0, 1, 0, 0, 1]
B. [1, 0, 0, 0, 0, 0, 0]
C. [0, 1]
D. [0, 5, 0, 0, 0, 0]
Answer: B,C
Explanation:
Explanation
Categorical features in linear models are typically translated into a sparse vector in which each possible value has a corresponding index or id. For example, if there are only three possible eye colors you can represent
'eye_color' as a length 3 vector: 'brown' would become [1, 0, 0], 'blue' would become [0, 1, 0] and 'green' would become [0, 0, 1]. These vectors are called "sparse" because they may be very long, with many zeros, when the set of possible values is very large (such as all English words).
[0, 0, 0, 1, 0, 0, 1] is not a sparse vector because it has two 1s in it. A sparse vector contains only a single 1.
[0, 5, 0, 0, 0, 0] is not a sparse vector because it has a 5 in it. Sparse vectors only contain 0s and 1s.
Reference: https://www.tensorflow.org/tutorials/linear#feature_columns_and_transformations