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ユニーク-100％合格率のAIOps-Foundation試験解答試験-試験の準備方法AIOps-Foundation受験対策書

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Peoplecert AIOps-Foundation 認定試験の出題範囲：

トピック
出題範囲

トピック 1

AIOps の影響の評価: この試験セクションでは、専門家のスキルを測定し、AIOps 導入の有効性を測定する方法について説明します。効率性の向上や運用コストの削減などの潜在的なメリットを評価する方法について説明します。

トピック 2

コアテクノロジー: 機械学習 (ML): この試験セクションでは、機械学習実践者のスキルを測定し、AIOps における AI と機械学習の役割を取り上げます。教師あり学習と教師なし学習、ML と分析の違い、実際のアプリケーションのためのトレーニングモデルなどについて説明します。評価される重要なスキルは、機械学習技術を適用して運用効率を高める方法を理解することです。

トピック 3

AIOps と運用メトリクス: 試験のこのセクションでは、パフォーマンスアナリストのスキルを測定し、AIOps ソリューションの実装の結果を定量化するために使用される業界標準のメトリクスを取り上げます。

トピック 4

AIOps の基礎: この試験セクションでは、IT 運用プロフェッショナルのスキルを測定し、IT 運用分析との違いを踏まえて AIOps の進化について取り上げます。また、AIOps システムの現在の段階と、現代の IT 環境におけるその重要性についても検討します。評価される重要なスキルは、AIOps の導入を促進する基本概念を理解することです。

トピック 5

組織における AIOps: この試験セクションでは、組織リーダーのスキルを測定し、AIOps を既存のフレームワークに統合する方法を取り上げます。DevOps の実践、サイトの信頼性、セキュリティ対策、システムの複雑さの管理に対する AIOps の影響について説明します。評価される重要なスキルは、AIOps の実装を成功させるために必要な組織の変更を認識することです。

>> AIOps-Foundation試験解答 <<

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Peoplecert DevOps Institute AIOps Foundation V1.0 認定 AIOps-Foundation 試験問題 (Q34-Q39):

質問 # 34
Which definition BEST describes Big Data?

A. Data sets of structured data that have grown over time
B. Data sets that are "too large" or diverse causing traditional data processing techniques to be ineffective
C. Data sets thatlive in data warehouses or lakes
D. Data sets that are so large they can only be interrogated using Al

正解：B

解説：
Big Data refers to data sets that are so large, fast, or complex that traditional data processing methods are inadequate to handle them. This concept is characterized by the Five V's:
* Volume: The sheer amount of data generated.
* Velocity: The speed at which new data is produced and needs to be processed.
* Variety: The different types of data (structured, unstructured, semi-structured).
* Veracity: The quality and accuracy of the data.
* Value: The usefulness of the data for decision-making.
In the context of AIOps, understanding Big Data is crucial as it involves combining big data analytics with machine learning algorithms to enhance IT operations.

質問 # 35
At which stage does the data pipeline deduplicate data?

A. Storage
B. Enrichment/filtering
C. Extraction/collection
D. Cleaning/integration

正解：D

解説：
In a data pipeline, deduplication occurs during the cleaning and integration stage. This process involves identifying and removing duplicate records to ensure data quality and accuracy. By eliminating redundancies, organizations can maintain a single source of truth, leading to morereliable analytics and decision-making.
The DevOps Institute's AIOps Foundation course underscores the importance of data cleaning and integration in preparing data for effective analysis and operational use.

質問 # 36
What is a big advantage of AlOps over ITOA?

A. It can predict the future
B. It helps operations be reactive
C. It works with large datasets
D. It can understand the past

正解：A

解説：
A significant advantage ofAIOps (Artificial Intelligence for IT Operations)over traditionalIT Operations Analytics (ITOA)is its ability topredict future events. While ITOA focuses on analyzing historical data to understand past incidents, AIOps leverages advanced machine learning algorithms to forecast potential issues before they occur. This predictive capability enables proactive problem resolution, reducing downtime and improving system reliability. The DevOps Institute's AIOps Foundation course highlights this forward- looking approach as a key benefit of implementing AIOps in modern IT environments.

質問 # 37
Which of the MELT data types is specific to a microservices based system?

A. Metrics
B. Logs
C. Traces
D. Events

正解：C

解説：
In microservices-based systems, "Traces" are a specific MELT (Metrics, Events, Logs, Traces) data type.
Traces track the flow of requests through various services, providing visibility into the interactions and performance of microservices. This tracing is crucial for diagnosing issues, understanding system behavior, and optimizing performance in complex, distributed environments. The DevOps Institute's AIOps Foundation course emphasizes the role of traces in observability practices, enabling teams to monitor and improve microservices architectures effectively.
For more detailed information, refer to the DevOps Institute's AIOps Foundation course materials.

質問 # 38
Which of these data comes from monitoring rather than application or infrastructure telemetry?

A. Metrics
B. Alerts
C. Traces
D. Logs

正解：B

解説：
In IT operations, monitoring tools generate alerts to notify teams of significant events or anomalies that may require attention. These alerts are distinct from application or infrastructure telemetry data, such as metrics, logs, or traces, which provide detailed insights into system performance and behavior.
Alerts serve as a higher-level indication that something within the system deviates from the norm, prompting further investigation or action. In the AIOps Foundation course, the importance of effective alert management is emphasized to reduce noise and improve incident response.
In the context of IT operations and AIOps (Artificial Intelligence for IT Operations), it's essential to distinguish between different types of data sources:
* Metrics:These are numerical data points that represent the performance of systems over time. Metrics are typically collected from applications and infrastructure components to monitor aspects like CPU usage, memory consumption, and response times. They provide insights into the health and performance of the system.
* Logs:Logs are detailed, time-stamped records of events generated by applications, infrastructure, and other systems. They capture a wide range of information, including errors, warnings, and informational messages, which are crucial for troubleshooting and understanding system behavior.
* Alerts:Alerts are notifications generated by monitoring tools when specific conditions or thresholds are met. They are derived from the analysis of metrics, logs, and other telemetry data. Alerts serve as signals to IT operations teams that something requires attention.
* Traces:Traces track the flow of requests through various components of an application, providing visibility into the execution path and performance of distributed systems. They are essential for understanding the interactions between different services and identifying bottlenecks.
Among these,alertsare the data that come specifically from monitoring activities. Monitoring systems analyze metrics, logs, and traces to detect anomalies or threshold breaches and generate alerts accordingly. Therefore, alerts are a product of monitoring rather than raw telemetry data from applications or infrastructure.
This distinction is crucial in AIOps, where integrating and analyzing various data types enable proactive IT operations management. By understanding the origins and roles of metrics, logs, alerts, and traces, organizations can implement more effective monitoring strategies and leverage AIOps platforms to enhance system reliability and performance.
For a deeper understanding of these concepts, the DevOps Institute's AIOps Foundation course provides comprehensive coverage of data sources and types, as well as their roles in modern IT operations

質問 # 39
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