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1z0-1110-25テストサンプル問題 & 1z0-1110-25日本語参考

Oracleの1z0-1110-25のオンラインサービスのスタディガイドを買いたかったら、CertShikenを買うのを薦めています。CertShikenは同じ作用がある多くのサイトでリーダーとしているサイトで、最も良い品質と最新のトレーニング資料を提供しています。弊社が提供したすべての勉強資料と他のトレーニング資料はコスト効率の良い製品で、サイトが一年間の無料更新サービスを提供します。ですから、弊社のトレーニング製品はあなたが試験に合格することを助けにならなかったら、全額で返金することを保証します。

Oracle 1z0-1110-25 認定試験の出題範囲：

トピック
出題範囲

トピック 1

Create and Manage Projects and Notebook Sessions: This part assesses the skills of Cloud Data Scientists and focuses on setting up and managing projects and notebook sessions within OCI Data Science. It also covers managing Conda environments, integrating OCI Vault for credentials, using Git-based repositories for source code control, and organizing your development environment to support streamlined collaboration and reproducibility.

トピック 2

Use Related OCI Services: This final section measures the competence of Machine Learning Engineers in utilizing OCI-integrated services to enhance data science capabilities. It includes creating Spark applications through OCI Data Flow, utilizing the OCI Open Data Service, and integrating other tools to optimize data handling and model execution workflows.

トピック 3

OCI Data Science - Introduction & Configuration: This section of the exam measures the skills of Machine Learning Engineers and covers foundational concepts of Oracle Cloud Infrastructure (OCI) Data Science. It includes an overview of the platform, its architecture, and the capabilities offered by the Accelerated Data Science (ADS) SDK. It also addresses the initial configuration of tenancy and workspace setup to begin data science operations in OCI.

トピック 4

Implement End-to-End Machine Learning Lifecycle: This section evaluates the abilities of Machine Learning Engineers and includes an end-to-end walkthrough of the ML lifecycle within OCI. It involves data acquisition from various sources, data preparation, visualization, profiling, model building with open-source libraries, Oracle AutoML, model evaluation, interpretability with global and local explanations, and deployment using the model catalog.

トピック 5

Apply MLOps Practices: This domain targets the skills of Cloud Data Scientists and focuses on applying MLOps within the OCI ecosystem. It covers the architecture of OCI MLOps, managing custom jobs, leveraging autoscaling for deployed models, monitoring, logging, and automating ML workflows using pipelines to ensure scalable and production-ready deployments.

>> 1z0-1110-25テストサンプル問題 <<

Oracle 1z0-1110-25 Exam | 1z0-1110-25テストサンプル問題 - 高効率日本語参考ために　1z0-1110-25 準備

ブラウジング中の支払いのセキュリティが心配ですか？ 1z0-1110-25テストトレントは、購入プロセスのセキュリティ、製品のダウンロード、インストールを安全でウイルスのないものにすることができます。この点について疑問がある場合は、専門の担当者がインストールと使用をリモートでガイドします。 1z0-1110-25テスト回答の購入プロセスは非常に簡単で、単純な人にとっては大きな恩恵です。1z0-1110-25学習教材の合格率は彼らのものよりもはるかに高いことを保証できます。そしてこれが最も重要です。以前のデータによると、1z0-1110-25トレーニング質問を使用する人の98％〜99％が試験に合格しました。あなたが私たちに信頼を与えてくれるなら、私たちはあなたに成功を与えます。

Oracle Cloud Infrastructure 2025 Data Science Professional 認定 1z0-1110-25 試験問題 (Q15-Q20):

質問 # 15
Which statement about resource principals is true?

A. When you authenticate using a resource principal, you need to create and manage credentials to access OCI resources.
B. A resource principal is a feature of IAM that enables resources to be authorized principal actors.
C. The Data Science service does not provide authentication via a notebook session's or job run's resource principal to access other OCI resources.
D. A resource principal is not a secure way to authenticate to resources, compared to the OCI configuration and API key approach.

正解：B

解説：
Detailed Answer in Step-by-Step Solution:
* Define Resource Principals: They allow OCI resources (e.g., notebook sessions) to authenticate to other OCI services without user credentials.
* Evaluate Options:
* A: False-Resource principals eliminate manual credential management.
* B: False-They're secure, leveraging IAM policies, not less secure than API keys.
* C: False-Data Science supports resource principals for accessing resources (e.g., Object Storage).
* D: True-Resource principals are an IAM feature authorizing resources as actors.
* Reasoning: D captures the essence of resource principals as an IAM mechanism.
* Conclusion: D is correct.
OCI documentation states: "A resource principal is an IAM feature that enables OCI resources, such as compute instances or notebook sessions, to act as principal actors and authenticate to other OCI services using policies." This refutes A (no credentials needed), B (secure method), and C (supported in Data Science), making D the accurate statement.
Oracle Cloud Infrastructure IAM Documentation, "Resource Principals".

質問 # 16
Which technique can be used for feature engineering in the machine learning lifecycle?

A. Principal Component Analysis (PCA)
B. Support Vector Machines (SVM)
C. Gradient boosting
D. K-means clustering

正解：A

解説：
Detailed Answer in Step-by-Step Solution:
* Objective: Identify a feature engineering technique in ML.
* Understand Feature Engineering: Transforms raw data into model-ready features.
* Evaluate Options:
* A. PCA: Reduces dimensionality-feature engineering-correct.
* B. K-means: Clustering model-not feature engineering.
* C. SVM: Classification model-not feature engineering.
* D. Gradient boosting: Model training-not feature engineering.
* Reasoning: PCA creates new features via transformation-fits definition.
* Conclusion: A is correct.
OCI documentation states: "Feature engineering techniques like Principal Component Analysis (PCA) (A) transform data into new features to enhance model performance." B, C, and D are modeling techniques-only A aligns with OCI's feature engineering stage.
Oracle Cloud Infrastructure Data Science Documentation, "Feature Engineering Techniques".

質問 # 17
Which OCI service provides a scalable environment for developers and data scientists to run Apache Spark applications at scale?

A. Data Science
B. Anomaly Detection
C. Data Flow
D. Data Labeling

正解：C

解説：
Detailed Answer in Step-by-Step Solution:
* Objective: Identify the OCI service for scalable Spark applications.
* Evaluate Options:
* A: Data Science-ML platform, not Spark-focused.
* B: Anomaly Detection-Specific ML service, not general Spark.
* C: Data Labeling-Annotation tool, not Spark-related.
* D: Data Flow-Managed Spark service for big data.
* Reasoning: Data Flow is OCI's Spark execution engine.
* Conclusion: D is correct.
OCI Data Flow "provides a fully managed environment to run Apache Spark applications at scale, ideal for data processing and ML tasks." Data Science (A) supports Spark in notebooks, but Data Flow (D) is the dedicated, scalable solution-B and C are unrelated.
Oracle Cloud Infrastructure Data Flow Documentation, "Overview".

質問 # 18
How can you collaborate with team members in OCI Data Science Workspace?

A. By sharing the workspace instance with other users
B. By using version control systems integrated with the workspace
C. By granting access to specific notebooks and files
D. By enabling chat and video conferencing within the workspace

正解：B

解説：
Detailed Answer in Step-by-Step Solution:
* Objective: Determine collaboration method in OCI Data Science (Notebook Sessions).
* Evaluate Options:
* A: Access control-Possible but not primary collaboration.
* B: Version control (e.g., Git)-Standard for code sharing-correct.
* C: Shared instance-Not supported; sessions are single-user.
* D: Chat/video-Not a feature of OCI Data Science.
* Reasoning: B leverages Git for team collaboration-OCI's recommended method.
* Conclusion: B is correct.
OCI documentation states: "Collaborate in Data Science by integrating version control systems like Git (B) with notebook sessions to share code and notebooks." A is limited, C isn't feasible, and D isn't available- only B matches OCI's collaboration approach.
Oracle Cloud Infrastructure Data Science Documentation, "Collaboration with Git".

質問 # 19
Which statement is true about origin management in Web Application Firewall (WAF)?

A. Only statement B is true
B. Only a single origin can be active for a WAF
C. Both the statements are true
D. Only statement A is true
E. Both the statements are false
F. Multiple origins can be defined

正解：C

解説：
Detailed Answer in Step-by-Step Solution:
* Objective: Determine truth about WAF origin management.
* Understand WAF: Protects apps by routing traffic via origins.
* Evaluate Statements:
* A: Multiple origins-True; WAF supports this.
* B: Single active origin-True; only one is active per policy.
* Evaluate Options:
* C: B only-False; A is true.
* D: Both false-Incorrect.
* E: Both true-Correct per OCI WAF.
* F: A only-False; B is true.
* Conclusion: E is correct.
OCI documentation states: "WAF allows defining multiple origins (A), but only one origin is active per WAF policy at a time (B)-both are true (E)." C, D, and F misalign-E matches OCI's WAF origin management.
Oracle Cloud Infrastructure WAF Documentation, "Origin Management".

質問 # 20
......

数千人の専門家で構成された権威ある制作チームが、1z0-1110-25学習の質問を理解し、質の高い学習体験を楽しんでいます。試験概要と現在のポリシーの最近の変更に応じて、1z0-1110-25テストガイドの内容を随時更新します。また、1z0-1110-25試験の質問は、わかりにくい概念を簡素化して学習方法を最適化し、習熟度を高めるのに役立ちます。

1z0-1110-25日本語参考: https://www.certshiken.com/1z0-1110-25-shiken.html