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1z0-1110-25独学書籍、1z0-1110-25認証pdf資料

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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

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 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

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 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

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.

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Oracle Cloud Infrastructure 2025 Data Science Professional 認定 1z0-1110-25 試験問題 (Q64-Q69):

質問 # 64
Which encryption is used for Oracle Data Science?

A. 256-bit Advanced Encryption Standard (AES-256)
B. Data Encryption Standard (DES)
C. Twofish
D. Rivest Shamir Adleman (RSA)
E. Triple DES (TDES)

正解：A

解説：
Detailed Answer in Step-by-Step Solution:
* Objective: Identify encryption standard for OCI Data Science.
* Understand OCI Encryption: Applies to data at rest and in transit.
* Evaluate Options:
* A: AES-256-Industry-standard, OCI default-correct.
* B: DES-Outdated, weak-incorrect.
* C: TDES-Older, less secure-incorrect.
* D: Twofish-Not OCI standard-incorrect.
* E: RSA-Asymmetric, not primary for data at rest-incorrect.
* Reasoning: AES-256 is OCI's go-to for Data Science resources.
* Conclusion: A is correct.
OCI documentation states: "Data Science services encrypt data at rest using AES-256 (A), ensuring high security for notebooks, jobs, and models." B, C, D, and E are either outdated or not used-only A matches OCI's encryption policy.
Oracle Cloud Infrastructure Data Science Documentation, "Data Encryption".

質問 # 65
Which cache rules criterion matches if the concatenation of the requested URL path and query are identical to the contents of the value field?

A. URL_PART_CONTAINS
B. URL_STARTS_WITH
C. URL_PART_ENDS_WITH
D. URL_IS

正解：D

解説：
Detailed Answer in Step-by-Step Solution:
* Objective: Match a cache rule criterion for exact URL path and query.
* Understand Cache Rules: Used in OCI (e.g., WAF, CDN) to cache content.
* Evaluate Options:
* A: Contains-Partial match, not exact.
* B: Is-Exact match of full URL (path + query)-correct.
* C: Ends with-Matches end, not full URL.
* D: Starts with-Matches start, not full URL.
* Reasoning: "URL_IS" checks exact equality-fits requirement.
* Conclusion: B is correct.
OCI documentation states: "The URL_IS (B) criterion in cache rules matches when the full URL (path and query) exactly equals the specified value." A, C, and D are partial matches-only B ensures identical concatenation per OCI's caching config.
Oracle Cloud Infrastructure WAF Documentation, "Cache Rules Criteria".

質問 # 66
Six months ago, you created and deployed a model that predicts customer churn for a call centre. Initially, it was yielding quality predictions. However, over the last two months, users are questioning the credibility of the predictions. Which TWO methods would you employ to verify the accuracy of the model?

A. Retrain the model
B. Drift monitoring
C. Validate the model using recent data
D. Operational monitoring
E. Redeploy the model

正解：A、B

解説：
Detailed Answer in Step-by-Step Solution:
* Objective: Address declining prediction accuracy and verify model performance.
* Analyze Problem: Degradation over time suggests data drift or model staleness-common ML issues.
* Evaluate Options:
* A. Retrain the model: Uses new data to update the model-fixes accuracy-correct.
* B. Validate with recent data: Tests performance but doesn't fix-diagnostic only.
* C. Drift monitoring: Detects data distribution shifts-verifies cause-correct.
* D. Redeploy the model: Repeats deployment, doesn't address root cause.
* E. Operational monitoring: Tracks infra (e.g., latency), not prediction accuracy.
* Reasoning: C identifies drift (why accuracy dropped), A corrects it-best pair for verification and improvement.
* Conclusion: A and C are correct.
OCI documentation states: "Drift monitoring (C) detects changes in data distribution that impact accuracy, while retraining (A) with new data restores model performance." Validation (B) checks but doesn't fix, redeployment (D) is redundant, and operational monitoring (E) is infra-focused-only A and C align with OCI's model maintenance strategy.
Oracle Cloud Infrastructure Data Science Documentation, "Model Monitoring and Retraining".

質問 # 67
Which statement about Oracle Cloud Infrastructure Data Science Jobs is true?

A. Jobs comes with a set of standard tasks that cannot be customized
B. You must create and manage your own Jobs infrastructure
C. Jobs provisions the infrastructure to run a process on-demand
D. You must use a single Shell/Bash or Python artifact to run a job

正解：C

解説：
Detailed Answer in Step-by-Step Solution:
* Objective: Identify a true statement about OCI Data Science Jobs.
* Understand OCI Jobs: Jobs automate ML tasks (e.g., training) on managed infrastructure.
* Evaluate Options:
* A: True-Jobs provision OCI compute resources on-demand for task execution.
* B: False-Users define custom tasks (e.g., Python scripts), not limited to standard ones.
* C: False-Infrastructure is fully managed by OCI, not user-managed.
* D: False-Multiple artifacts (e.g., ZIP with dependencies) can be used, not just one file.
* Reasoning: A reflects OCI's managed, on-demand provisioning model for Jobs.
* Conclusion: A is correct.
The OCI Data Science documentation states: "Jobs provision compute infrastructure on-demand to execute user-defined tasks, such as model training or data processing, on fully managed OCI resources." B is incorrect (customization is allowed), C contradicts the managed nature, and D misstates artifact flexibility-only A accurately describes Jobs.
Oracle Cloud Infrastructure Data Science Documentation, "Jobs Overview".

質問 # 68
Which step is a part of the AutoML pipeline?

A. Model saved to Model Catalog
B. Feature Selection
C. Feature Extraction
D. Model Deployment

正解：B

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
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