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Oracle 1z0-1110-25 Prüfungsplan:

Thema	Einzelheiten
Thema 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.
Thema 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.
Thema 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.
Thema 4	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.
Thema 5	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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Oracle Cloud Infrastructure 2025 Data Science Professional 1z0-1110-25 Prüfungsfragen mit Lösungen (Q48-Q53):

48. Frage
How can you convert a fixed load balancer to a flexible load balancer?

A. There is no way to convert the load balancer
B. Delete the fixed load balancer and create a new one
C. Use Update Shape workflows
D. Using the Edit Listener option

Antwort: B

Begründung:
Detailed Answer in Step-by-Step Solution:
* Objective: Convert fixed to flexible load balancer in OCI.
* Understand Load Balancers: Fixed (e.g., 10 Mbps) vs. flexible (dynamic shapes).
* Evaluate Options:
* A: False-Conversion possible via recreation.
* B: Update Shape-For flexible only, not conversion.
* C: Delete and recreate-Standard method-correct.
* D: Edit Listener-Configures rules, not type.
* Reasoning: OCI requires new creation for type change.
* Conclusion: C is correct.
OCI documentation states: "To change from a fixed to a flexible load balancer, delete the existing fixed load balancer and create a new flexible one (C)-direct conversion isn't supported." A is too absolute, B and D don't apply-only C matches OCI's process.
Oracle Cloud Infrastructure Load Balancing Documentation, "Changing Load Balancer Type".

49. Frage
Which statement best describes Oracle Cloud Infrastructure Data Science Jobs?

A. Jobs let you define and run repeatable tasks on fully managed third-party cloud infrastructures.
B. Jobs let you define and run repeatable tasks on fully managed infrastructure.
C. Jobs let you define and run repeatable tasks on customer-managed infrastructure.
D. Jobs let you define and run all Oracle Cloud DevOps workloads.

Antwort: B

Begründung:
Detailed Answer in Step-by-Step Solution:
* Understand OCI Data Science Jobs: This service automates ML tasks (e.g., training, evaluation) with configurable, repeatable executions.
* Key Characteristics: Jobs run on OCI's infrastructure, managed by Oracle, not the customer or third parties, and are specific to Data Science, not general DevOps.
* Evaluate Options:
* A: Correct-Jobs are defined by users (e.g., via scripts) and executed on OCI's fully managed compute resources.
* B: Incorrect-Infrastructure is managed by OCI, not the customer.
* C: Incorrect-No third-party cloud integration; it's OCI-specific.
* D: Incorrect-Jobs are for Data Science tasks (e.g., ML training), not all DevOps workloads (e.
g., CI/CD pipelines).
* Reasoning: "Fully managed" means OCI handles provisioning and scaling, aligning with A.
* Conclusion: A accurately reflects the service's purpose and operation.
OCI Data Science Jobs "allow users to define and execute repeatable machine learning tasks, such as model training or batch processing, on fully managed OCI infrastructure." This eliminates customer management (B), third-party clouds (C), or broad DevOps scope (D). The documentation emphasizes automation and management by OCI, making A the precise description.
Oracle Cloud Infrastructure Data Science Documentation, "Overview of Jobs" section.

50. Frage
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. Local Interpretable Model-Agnostic Explanations
D. Feature Permutation Importance Explanations

Antwort: A

Begründung:
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".

51. Frage
Which step is unique to MLOps, as opposed to DevOps?

A. Continuous delivery
B. Continuous deployment
C. Continuous training
D. Continuous integration

Antwort: C

Begründung:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify a step unique to MLOps vs. DevOps.
* Compare MLOps and DevOps:
* DevOps: Focuses on software deployment (CI/CD).
* MLOps: Extends DevOps to ML, adding model-specific steps.
* Evaluate Options:
* A: Continuous deployment-Common to both (software/models).
* B: Continuous integration-Common to both (code merging).
* C: Continuous delivery-Common to both (releasing updates).
* D: Continuous training-Unique to MLOps (retraining models with new data).
* Reasoning: Only D addresses ML-specific needs (model retraining).
* Conclusion: D is correct.
OCI documentation notes: "MLOps extends DevOps with continuous training, a process unique to machine learning where models are retrained with new data to maintain performance." CI (B), CD (A), and delivery (C) are shared with DevOps-only continuous training (D) is MLOps-specific.
Oracle Cloud Infrastructure Data Science Documentation, "MLOps Concepts".

52. Frage
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. R-Squared
B. F1 Score
C. Mean squared error
D. Recall
E. Explained variance score

Antwort: B,D

Begründung:
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".

53. Frage
......

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