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NEW QUESTION: 1



A. Option D
B. Option B
C. Option A
D. Option C
E. Option E
Answer: B,C,E
Explanation:
A: The APPEND keyword tells SQL*Loader to preserve any preexisting data in the table. Other options allow you to delete preexisting data, or to fail with an error if the table is not empty to begin with.
B (not D): Note:
* SQL*Loader-00210: first data file is empty, cannot process the FIELD NAMES record Cause: The data file listed in the next message was empty. Therefore, the FIELD NAMES FIRST FILE directive could not be processed. Action: Check the listed data file and fix it. Then retry the operation
E:
*A comma-separated values (CSV) (also sometimes called character-separated values, because
the separator character does not have to be a comma) file stores tabular data (numbers and text)
in plain-text form. Plain text means that the file is a sequence of characters, with no data that has
to be interpreted instead, as binary numbers. A CSV file consists of any number of records,
separated by line breaks of some kind; each record consists of fields, separated by some other
character or string, most commonly a literal comma or tab. Usually, all records have an identical
sequence of fields.
*Fields with embedded commas must be quoted.
Example:
1997,Ford,E350,"Super, luxurious truck"
Note:
*SQL*Loader is a bulk loader utility used for moving data from external files into the Oracle database.

NEW QUESTION: 2
Die Informationssicherheitsrichtlinie, die besagt, dass "jeder Einzelne an jeder kontrollierten Tür seinen Ausweis lesen muss", befasst sich mit welcher der folgenden Angriffsmethoden?
A. Identitätswechsel
B. Müllcontainertauchen
C. Huckepack nehmen
D. Schulter surfen
Answer: C
Explanation:
Erläuterung:
Piggybacking bezieht sich auf unbefugte Personen, die autorisierten Personen entweder physisch oder virtuell in eingeschränkte Bereiche folgen. Diese Richtlinie behebt das höfliche Verhaltensproblem, Türen für einen Fremden offen zu halten, wenn jeder Mitarbeiter an jeder kontrollierten Tür seinen Ausweis vorlesen lassen muss, damit keine unbefugte Person den sensiblen Bereich betreten kann. Ein Blick über die Schulter eines Benutzers, um vertrauliche Informationen zu erhalten, kann von einer nicht autorisierten Person ausgeführt werden, die mithilfe von Huckepack in Bereiche gelangt ist. Diese Richtlinie bezieht sich jedoch speziell auf die physische Zugangskontrolle. Schulter-Surfen würde durch die Umsetzung dieser Richtlinie nicht verhindert. Das Mülleimertauchen, bei dem im Papierkorb eines Unternehmens nach wertvollen Informationen gesucht wird, kann außerhalb des physischen Bereichs des Unternehmens durchgeführt werden. Daher würde diese Richtlinie diese Angriffsmethode nicht ansprechen. Identitätswechsel bezieht sich auf einen Social Engineer, der als Mitarbeiter auftritt und versucht, die gewünschten Informationen abzurufen. Einige Formen von Social-Engineering-Angriffen können sich einem Identitätswechsel- und Huckepack-Angriff anschließen. Diese Informationssicherheitsrichtlinie behandelt jedoch nicht den Identitätswechselangriff.

NEW QUESTION: 3
モデルをトレーニングして、Azure Machine Learningワークスペースに登録します。これで、モデルをリアルタイムWebサービスとしてデプロイする準備が整いました。
モデルをAzure Kubernetes Service（AKS）推論クラスターにデプロイしますが、サービスがモデルのデプロイメントに関連付けられているエントリスクリプトを実行するとエラーが発生するため、デプロイメントは失敗します。
コードを更新するたびにサービスを再デプロイすることなく、コードを繰り返し変更してサービスをリロードすることにより、エラーをデバッグする必要があります。
あなたは何をするべきか？
A. ローカルWebサービスデプロイメント構成を作成し、モデルをローカルDockerコンテナーにデプロイします。
B. エントリスクリプトの最初の行にブレークポイントを追加し、サービスをAKSに再デプロイします。
C. モデルの新しいバージョンを登録し、エントリースクリプトを更新して、登録されたパスからモデルの新しいバージョンをロードします。
D. Azureコンテナーインスタンス（ACI）Webサービスデプロイメント構成を作成し、ACIにモデルをデプロイします。
E. AKSサービスの展開構成を変更して、アプリケーションの洞察を有効にし、AKSに再展開します。
Answer: D
Explanation:
Explanation
How to work around or solve common Docker deployment errors with Azure Container Instances (ACI) and Azure Kubernetes Service (AKS) using Azure Machine Learning.
The recommended and the most up to date approach for model deployment is via the Model.deploy() API using an Environment object as an input parameter. In this case our service will create a base docker image for you during deployment stage and mount the required models all in one call. The basic deployment tasks are:
1. Register the model in the workspace model registry.
2. Define Inference Configuration:
a. Create an Environment object based on the dependencies you specify in the environment yaml file or use one of our procured environments.
b. Create an inference configuration (InferenceConfig object) based on the environment and the scoring script.
3. Deploy the model to Azure Container Instance (ACI) service or to Azure Kubernetes Service (AKS).