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NEW QUESTION: 1
회사에서 애플리케이션의 데이터를 암호화 해야 하는 개발자를 지원하기 위해 확장 가능한 키 관리 인프라를 구축하려고 합니다. 솔루션 아키텍트는 운영 부담을 줄이기 위해 무엇을 해야 합니까?
A. MFA (Multi-Factor Authentication)를 사용하여 암호화 키를 보호합니다.
B. AWS Certificate Manager (ACM)를 사용하여 암호화 키 생성, 저장 및 할당
C. IAM 정책을 사용하여 암호화 키를 보호하기 위한 액세스 권한이 있는 사용자의 범위를 제한합니다.
D. AWS Key Management Service (AWS KMS)를 사용하여 암호화 키 보호
Answer: D

NEW QUESTION: 2
Sie aktivieren und konfigurieren die PowerShell-Skriptblockprotokollierung.
Sie müssen mithilfe von Windows PowerShell-Skripts anzeigen, welche Skriptblöcke ausgeführt wurden.
Was tun?
A. Zeigen Sie das Windows PowerShell-Ereignisprotokoll an.
B. Öffnen Sie die Protokolldateien in% SYSTEMROOT% \\ Logs.
C. Öffnen Sie die Protokolldateien in% LocalAppData% \\ Microsoft \\ Windows \\ PowerShell.
D. Zeigen Sie das Microsoft-Windows-PowerShell / Operational-Ereignisprotokoll an.
Answer: D
Explanation:
Explanation
https://docs.microsoft.com/en-us/powershell/wmf/5.0/audit_scriptAfter you enable detailed script tracing, Windows PowerShell logs all script blocks to the event log, MicrosoftWindows-PowerShell/Operational.

NEW QUESTION: 3
You need to record the row count as a metric named row_count that can be returned using the get_metrics method of the Run object after the experiment run completes. Which code should you use?
A. run.log_row('row_count', rows)
B. run.upload_file('row_count', './data.csv')
C. run.log('row_count', rows)
D. run.log_table('row_count', rows)
E. run.tag('row_count', rows)
Answer: C
Explanation:
Log a numerical or string value to the run with the given name using log(name, value, description=''). Logging a metric to a run causes that metric to be stored in the run record in the experiment. You can log the same metric multiple times within a run, the result being considered a vector of that metric.
Example: run.log("accuracy", 0.95)
Incorrect Answers:
E: Using log_row(name, description=None, **kwargs) creates a metric with multiple columns as described in kwargs. Each named parameter generates a column with the value specified. log_row can be called once to log an arbitrary tuple, or multiple times in a loop to generate a complete table.
Example: run.log_row("Y over X", x=1, y=0.4)
Reference:
https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.run