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1z0-1110-25試験の準備方法|完璧な1z0-1110-25試験勉強攻略試験|正確的なOracle Cloud Infrastructure 2025 Data Science Professional的中合格問題集
Oracleの1z0-1110-25試験準備は、テストヒット率が高いため、98%〜100%の合格率です。 したがって、当社の1z0-1110-25学習教材は効果的であるだけでなく、有用でもあります。 誰もが知っているように、時間は誰にとっても非常に重要です。 一部の候補者は、自分の仕事や家族で非常に忙しいです。 1z0-1110-25試験の審査に時間をかけることは非常に困難です。 ただし、1z0-1110-25試験の教材を使用する場合、学習する時間はほとんどなく、Oracle Cloud Infrastructure 2025 Data Science Professional合格率は高くなります。 1z0-1110-25学習教材はあなたの信頼に値します。
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
- 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 2
- 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.
トピック 3
- 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 4
- 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 5
- 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.
有難い1z0-1110-25試験勉強攻略試験-試験の準備方法-効率的な1z0-1110-25的中合格問題集
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Oracle Cloud Infrastructure 2025 Data Science Professional 認定 1z0-1110-25 試験問題 (Q67-Q72):
質問 # 67
Which statement about resource principals is true?
- A. The Data Science service does not provide authentication via a notebook session's or job run's resource principal to access other OCI resources.
- B. When you authenticate using a resource principal, you need to create and manage credentials to access OCI resources.
- C. A resource principal is a feature of IAM that enables resources to be authorized principal actors.
- D. A resource principal is not a secure way to authenticate to resources, compared to the OCI configuration and API key approach.
正解:C
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 # 68
Which step is a part of the AutoML pipeline?
- A. Feature Selection
- B. Model Deployment
- C. Feature Extraction
- D. Model saved to Model Catalog
正解:A
解説:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify a step in OCI's AutoML pipeline.
* Understand AutoML: Automates model building-includes preprocessing, selection, and tuning.
* Evaluate Options:
* A: Feature Extraction (e.g., PCA) isn't explicitly part of OCI AutoML-too specific.
* B: Saving to Model Catalog is post-AutoML, not a pipeline step.
* C: Deployment is a separate action after AutoML-incorrect.
* D: Feature Selection (e.g., choosing relevant features) is a core AutoML step-correct.
* Reasoning: OCI AutoML automates feature selection, algorithm choice, and tuning-D fits.
* Conclusion: D is correct.
OCI AutoML's pipeline includes "feature selection, algorithm selection, adaptive sampling, and hyperparameter tuning," per the documentation. Extraction (A) isn't highlighted, while saving (B) and deployment (C) are post-process actions-only Feature Selection (D) is an integral automated step.
Oracle Cloud Infrastructure Data Science Documentation, "AutoML Pipeline".
質問 # 69
The feature type TechJob has the following registered validators:
* TechJob.validator.register(name='is_tech_job', handler=is_tech_job_default_handler)
* TechJob.validator.register(name='is_tech_job', handler=is_tech_job_open_handler, condition= ('job_family',))
* TechJob.validator.register(name='is_tech_job', handler=is_tech_job_closed_handler, condition= ('job_family': 'IT'))When you run is_tech_job(job_family='Engineering'), what does the feature type validator system do?
- A. Execute the is_tech_job_default_handler handler
- B. Execute the is_tech_job_closed_handler handler
- C. Execute the is_tech_job_open_handler handler
- D. Throw an error because the system cannot determine which handler to run
正解:C
解説:
Detailed Answer in Step-by-Step Solution:
* Objective: Determine which validator handler runs for is_tech_job(job_family='Engineering').
* Understand Validator System: Likely ADS SDK-executes handlers based on conditions.
* Analyze Validators:
* Default: is_tech_job_default_handler-No condition, fallback.
* Open: is_tech_job_open_handler-Condition ('job_family',)-requires job_family arg.
* Closed: is_tech_job_closed_handler-Condition ('job_family': 'IT')-requires job_family='IT'.
* Evaluate Call: job_family='Engineering'-Matches job_family presence, not IT.
* Reasoning:
* Open handler applies (tuple condition means arg exists).
* Closed fails (Engineering # IT).
* Default is overridden by specific matches.
* Conclusion: D is correct.
OCI ADS documentation states: "Validators execute the most specific handler matching the condition; for is_tech_job(job_family='Engineering'), is_tech_job_open_handler (D) runs as it matches job_family presence, while is_tech_job_closed_handler (C) requires IT-default (A) is bypassed, no error (B)." Only D fits per ADS validator logic.
Oracle Cloud Infrastructure ADS SDK Documentation, "Feature Type Validators".
質問 # 70
Which statement about Oracle Cloud Infrastructure Multi-Factor Authentication (MFA) is NOT valid?
- A. An administrator can disable MFA for another user
- B. A user can register only one device to use for MFA
- C. Users must install a supported authenticator app on the mobile device they intend to register for MFA
- D. Users cannot disable MFA for themselves
正解:B
解説:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify the invalid MFA statement.
* Evaluate Options:
* A: True-Users can't disable MFA; admin-controlled.
* B: False-Multiple devices can be registered-invalid.
* C: True-Authenticator app is required.
* D: True-Admins can disable MFA.
* Reasoning: B contradicts OCI's multi-device support.
* Conclusion: B is incorrect.
OCI documentation states: "Users can register multiple devices for MFA (B is false), must use an authenticator app (C), and cannot disable MFA themselves (A)-admins can (D)." Only B is not valid per OCI's IAM MFA policy.
Oracle Cloud Infrastructure IAM Documentation, "Multi-Factor Authentication".
質問 # 71
You want to evaluate the relationship between feature values and target variables. You have a large number of observations having a near uniform distribution and the features are highly correlated. Which model explanation technique should you choose?
- A. Accumulated Local Effects
- B. Feature Dependence Explanations
- C. Feature Permutation Importance Explanations
- D. Local Interpretable Model-Agnostic Explanations
正解:A
解説:
Detailed Answer in Step-by-Step Solution:
* Objective: Select an explanation technique for feature-target relationships with correlated features.
* Evaluate Options:
* A: Permutation-Breaks with high correlation.
* B: LIME-Local, not global relationships.
* C: Dependence-Not a standard term; vague.
* D: ALE-Handles correlation, shows feature effects-correct.
* Reasoning: ALE is robust to correlated features, ideal here.
* Conclusion: D is correct.
OCI documentation states: "Accumulated Local Effects (ALE) (D) evaluates feature-target relationships, accounting for correlations, unlike permutation importance (A) which falters with high correlation." B is local, C isn't defined-only D fits per OCI's explanation tools.
Oracle Cloud Infrastructure Data Science Documentation, "Model Explanation Techniques".
質問 # 72
......
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