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1z0-1110-25最新題庫|準備通過Oracle Cloud Infrastructure 2025 Data Science Professional快人一步
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Oracle 1z0-1110-25 考試大綱：
| 主題 |
簡介 |
| 主題 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.
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| 主題 2 |
- 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.
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| 主題 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.
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| 主題 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.
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| 主題 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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1z0-1110-25考題資訊 - 1z0-1110-25測試引擎
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最新的 Oracle Cloud 1z0-1110-25 免費考試真題 (Q69-Q74):
問題 #69
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_PART_ENDS_WITH
- C. URL_IS
- D. URL_STARTS_WITH
答案：C
解題說明：
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".
問題 #70
You are using Oracle Cloud Infrastructure (OCI) Anomaly Detection to train a model to detect anomalies in pump sensor data. How does the required False Alarm Probability setting affect an anomaly detection model?
- A. It adds a score to each signal indicating the probability that it's a false alarm
- B. It determines how many false alarms occur before an error message is generated
- C. It changes the sensitivity of the model to detecting anomalies
- D. It is used to disable the reporting of false alarms
答案：C
解題說明：
Detailed Answer in Step-by-Step Solution:
* Objective: Understand the effect of False Alarm Probability (FAP) in OCI Anomaly Detection.
* Understand FAP: Controls false positive rate-threshold for anomaly flagging.
* Evaluate Options:
* A: Disable reporting-Incorrect; FAP sets sensitivity, not on/off.
* B: Changes sensitivity-Correct; lower FAP = fewer false positives-correct.
* C: Count-based error-Incorrect; not a counter.
* D: Score per signal-Incorrect; FAP is a global setting.
* Reasoning: FAP adjusts detection threshold-direct impact on sensitivity.
* Conclusion: B is correct.
OCI documentation states: "False Alarm Probability (FAP) (B) adjusts the model's sensitivity in Anomaly Detection-lower values increase specificity, reducing false positives." A, C, and D misinterpret FAP's role- only B aligns with OCI's anomaly detection tuning.
Oracle Cloud Infrastructure Anomaly Detection Documentation, "FAP Settings".
問題 #71
You are a data scientist using Oracle AutoML to produce a model and you are evaluating the score metric for the model. Which of the following TWO prevailing metrics would you use for evaluating a multiclass classification model?
- A. Mean squared error
- B. Explained variance score
- C. Recall
- D. R-Squared
- E. F1 Score
答案：C,E
解題說明：
Detailed Answer in Step-by-Step Solution:
* Objective: Select two metrics for multiclass classification in AutoML.
* Understand Multiclass Metrics: Focus on class-specific performance-classification, not regression.
* Evaluate Options:
* A. Recall: Measures true positives per class-key for multiclass-correct.
* B. Mean squared error: Regression metric-incorrect.
* C. F1 Score: Balances precision and recall-standard for multiclass-correct.
* D. R-Squared: Regression fit-incorrect.
* E. Explained variance: Regression metric-incorrect.
* Reasoning: A and C assess classification accuracy across multiple classes-fit AutoML's evaluation.
* Conclusion: A and C are correct.
OCI AutoML documentation states: "For multiclass classification, common evaluation metrics include recall (A) for per-class sensitivity and F1 Score (C) for balanced performance." B, D, and E are regression- focused-only A and C are supported and relevant per OCI's AutoML metrics suite.
Oracle Cloud Infrastructure AutoML Documentation, "Evaluation Metrics for Classification".
問題 #72
You are a researcher who requires access to large datasets. Which OCI service would you use?
- A. Oracle Open Data
- B. ADW (Autonomous Data Warehouse)
- C. Oracle Databases
- D. OCI Data Science
答案：A
解題說明：
Detailed Answer in Step-by-Step Solution:
* Objective: Find the OCI service for accessing large public datasets.
* Evaluate Options:
* A: Oracle Databases-General-purpose, not dataset-focused.
* B: ADW-Analytics warehouse, not a dataset repository.
* C: OCI Data Science-ML platform, not a dataset provider.
* D: Oracle Open Data-Free, public datasets (e.g., geospatial).
* Reasoning: Open Data provides pre-existing large datasets for research.
* Conclusion: D is correct.
OCI documentation states: "Oracle Open Data provides free access to large, curated datasets, such as geospatial data, ideal for researchers." Databases (A) and ADW (B) are for storage/analytics, Data Science (C) is for ML-not datasets-only Open Data (D) fits.
Oracle Cloud Infrastructure Open Data Documentation, "Overview".
問題 #73
You want to make your model more frugal to reduce the cost of collecting and processing data. You plan to do this by removing features that are highly correlated. You would like to create a heatmap that displays the correlation so that you can identify candidate features to remove. Which Accelerated Data Science (ADS) SDK method is appropriate to display the comparability between Continuous and Categorical features?
- A. pearson_plot()
- B. corr()
- C. cramersv_plot()
- D. correlation_ratio_plot()
答案：D
解題說明：
Detailed Answer in Step-by-Step Solution:
* Objective: Visualize correlation between continuous and categorical features.
* Evaluate Options:
* A: Pearson-Continuous vs. continuous-incorrect.
* B: Cramer's V-Categorical vs. categorical-incorrect.
* C: Correlation ratio-Continuous vs. categorical-correct.
* D: General correlation-Not specific to mixed types.
* Reasoning: Correlation ratio handles mixed feature types for heatmaps.
* Conclusion: C is correct.
OCI documentation states: "correlation_ratio_plot() (C) in ADS SDK visualizes correlations between continuous and categorical features, ideal for mixed-type heatmaps." Pearson (A) and Cramer's (B) are type- specific, corr() (D) is broad-only C fits per ADS capabilities.
Oracle Cloud Infrastructure ADS SDK Documentation, "Correlation Visualization".
問題 #74
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