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VCETorrent Oracle Cloud Infrastructure 2025 Data Science Professional (1z0-1110-25) Questions have numerous benefits, including the ability to demonstrate to employers and clients that you have the necessary knowledge and skills to succeed in the actual 1z0-1110-25 exam. Certified professionals are often more sought after than their non-certified counterparts and are more likely to earn higher salaries and promotions. Moreover, cracking the Oracle Cloud Infrastructure 2025 Data Science Professional (1z0-1110-25) exam helps to ensure that you stay up to date with the latest trends and developments in the industry, making you more valuable assets to your organization.
| Topic |
Details |
| Topic 1 |
- 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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| Topic 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.
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| Topic 3 |
- 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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| Topic 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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| Topic 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.
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1z0-1110-25 Exam Objectives - Related 1z0-1110-25 Exams
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Oracle Cloud Infrastructure 2025 Data Science Professional Sample Questions (Q96-Q101):
NEW QUESTION # 96
You want to make API calls against other OCI services from your instance without configuring user credentials. How would you achieve this?
- A. Create a group and add a policy
- B. Create a dynamic group and add a policy
- C. Create a dynamic group and add your instance
- D. No configuration is required for making API calls
Answer: B
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Enable credential-less API calls from an instance.
* Understand Resource Principal: Allows instances to authenticate via IAM without user creds.
* Evaluate Options:
* A: Dynamic group + policy-Correct; groups instance, grants access.
* B: Dynamic group only-Incomplete; needs policy.
* C: User group-Irrelevant for instances.
* D: No config-False; setup required.
* Reasoning: A sets up resource principal fully-group and perms.
* Conclusion: A is correct.
OCI documentation states: "To make API calls without credentials, create a dynamic group including the instance and add a policy (A) granting access to OCI services-enables resource principal." B lacks policy, C is user-based, D is false-only A completes the process per OCI's IAM setup.
Oracle Cloud Infrastructure IAM Documentation, "Resource Principal Configuration".
NEW QUESTION # 97
Which is NOT a part of Observability and Management Services?
- A. Logging Analytics
- B. Event Services
- C. OCI Management Service
- D. Logging
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify the non-Observability and Management (O&M) service in OCI.
* Understand O&M: Includes monitoring, logging, events tools.
* Evaluate Options:
* A: Event Services-Triggers actions, part of O&M-correct.
* B: OCI Management Service-Not a defined O&M service-incorrect.
* C: Logging Analytics-Log analysis, O&M component-correct.
* D: Logging-Log collection, O&M component-correct.
* Reasoning: B isn't listed in OCI's O&M suite-others are.
* Conclusion: B is correct (not part of O&M).
OCI documentation lists "Observability and Management Services as including Event Services (A), Logging Analytics (C), and Logging (D)-'OCI Management Service' (B) is not a recognized component." B appears to be a misnomer-only A, C, D are O&M per OCI's service catalog.
Oracle Cloud Infrastructure Observability and Management Documentation, "Service Overview".
NEW QUESTION # 98
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. Validate the model using recent data
- C. Redeploy the model
- D. Drift monitoring
- E. Operational monitoring
Answer: A,D
Explanation:
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".
NEW QUESTION # 99
You loaded data into Oracle Cloud Infrastructure (OCI) Data Science. To transform the data, you want to use the Accelerated Data Science (ADS) SDK. When you applied the get_recommendations() tool to the ADSDataset object, it showed you user-detected issues with all the recommended changes to apply to the dataset. Which option should you use to apply all the recommended transformations at once?
- A. get_transformed_dataset()
- B. visualize_transforms()
- C. fit_transform()
- D. auto_transform()
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Apply all recommended transformations from get_recommendations() in ADS.
* Understand ADS Tools: get_recommendations() suggests fixes (e.g., missing values).
* Evaluate Options:
* A: Returns transformed data-Not for applying-incorrect.
* B: Sklearn-style, not ADS-specific-incorrect.
* C: auto_transform()-Applies all recommendations-correct.
* D: Visualizes, doesn't apply-incorrect.
* Reasoning: auto_transform() executes the fixes suggested by get_recommendations().
* Conclusion: C is correct.
OCI documentation states: "After get_recommendations() identifies issues, use auto_transform() (C) on the ADSDataset to apply all recommended transformations at once." A retrieves, B is external, D visualizes- only C aligns with OCI's ADS transformation workflow.
Oracle Cloud Infrastructure ADS SDK Documentation, "Data Transformation Methods".
NEW QUESTION # 100
Which Oracle Accelerated Data Science (ADS) classes can be used for easy access to datasets fromreference libraries and index websites, such as scikit-learn?
- A. DatasetBrowser
- B. ADSTuner
- C. DatasetFactory
- D. SecretKeeper
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify ADS class for accessing datasets (e.g., scikit-learn).
* Evaluate Options:
* A: DatasetBrowser-Not an ADS class.
* B: DatasetFactory-Loads datasets from sources like scikit-learn-correct.
* C: ADSTuner-Hyperparameter tuning, not data access.
* D: SecretKeeper-Manages credentials, not datasets.
* Reasoning: DatasetFactory simplifies dataset loading (e.g., DatasetFactory.open()).
* Conclusion: B is correct.
OCI documentation states: "DatasetFactory in ADS SDK provides methods to easily load datasets from libraries like scikit-learn or other sources (e.g., DatasetFactory.open('sklearn.datasets:load_iris'))." A isn't real, C tunes models, and D handles secrets-only B fits.
Oracle Cloud Infrastructure ADS SDK Documentation, "DatasetFactory".
NEW QUESTION # 101
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