100% PASS QUIZ 2025 ORACLE LATEST 1Z0-1110-25 RELIABLE EXAM SIMULATOR

100% Pass Quiz 2025 Oracle Latest 1z0-1110-25 Reliable Exam Simulator

100% Pass Quiz 2025 Oracle Latest 1z0-1110-25 Reliable Exam Simulator

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Oracle 1z0-1110-25 Exam Syllabus Topics:

TopicDetails
Topic 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.
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.
Topic 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.
Topic 4
  • 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.
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.

Oracle Cloud Infrastructure 2025 Data Science Professional Sample Questions (Q110-Q115):

NEW QUESTION # 110
What happens when a notebook session is deactivated?

  • A. The underlying compute instance stops
  • B. Compute cost increases due to frequent deactivation
  • C. The data on boot volume is preserved
  • D. The block volume attached to the notebook is permanently deleted

Answer: A

Explanation:
Detailed Answer in Step-by-Step Solution:
* Understand Notebook Sessions: These are OCI compute instances running JupyterLab.
* Deactivation Impact: Deactivating stops the session to save costs.
* Evaluate Options:
* A: False-Costs decrease as compute stops.
* B: False-Boot volume data isn't preserved; block volume data is.
* C: True-The compute instance shuts down, halting billing.
* D: False-Block volume persists unless explicitly deleted.
* Reasoning: Deactivation stops the instance (C), preserving block volume data separately.
* Conclusion: C is correct.
The OCI documentation states: "When a notebook session is deactivated, the underlying compute instance stops, and billing for compute resources ceases. Data on the attached block volume is preserved, but the boot volume is not." A is backwards, B misattributes preservation, and D overstates deletion-only C aligns with the process.
Oracle Cloud Infrastructure Data Science Documentation, "Notebook Session Lifecycle".


NEW QUESTION # 111
You realize that your model deployment is about to reach its utilization limit. What would you do to avoid the issue before requests start to fail? Pick THREE.

  • A. Update the deployment to add more instances
  • B. Update the deployment to use a larger virtual machine (more CPUs/memory)
  • C. Delete the deployment
  • D. Update the deployment to use fewer instances
  • E. Reduce the load balancer bandwidth limit so that fewer requests come in

Answer: A,B,E

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Prevent deployment failure due to high utilization.
* Evaluate Options:
* A: More instances-Scales capacity-correct.
* B: Delete-Stops service, not a solution.
* C: Fewer instances-Worsens utilization.
* D: Larger VM-Increases resource capacity-correct.
* E: Reduce bandwidth-Limits load-correct.
* Reasoning: A and D boost capacity, E controls demand-proactive fixes.
* Conclusion: A, D, E are correct.
OCI documentation advises: "To handle high utilization, increase instances (A), use a larger compute shape (D), or adjust load balancer bandwidth (E) to manage request volume." B stops service, C reduces capacity- only A, D, E prevent failure per OCI's scaling options.
Oracle Cloud Infrastructure Data Science Documentation, "Model Deployment Scaling".


NEW QUESTION # 112
True or false? Bias is a common problem in data science applications.

  • A. True
  • B. False

Answer: A

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Assess if bias is a common issue in data science.
* Define Bias: Systematic errors in data/models (e.g., skewed training data).
* Evaluate Statement:
* Bias arises from unrepresentative data, poor feature selection, or algorithmic flaws-widely recognized in ML.
* Examples: Gender bias in hiring models, racial bias in facial recognition.
* Reasoning: Literature and practice (e.g., fairness in AI) confirm bias as prevalent.
* Conclusion: A (True) is correct.
OCI documentation notes: "Bias is a common challenge in data science, stemming from imbalanced datasets or flawed assumptions, requiring techniques like re-weighting or fairness checks." This aligns with industry standards-bias is a well-documented issue, making A true.
Oracle Cloud Infrastructure Data Science Documentation, "Addressing Bias in Models".


NEW QUESTION # 113
Using Oracle AutoML, you are tuning hyperparameters on a supported model class and have specified a time budget. AutoML terminates computation once the time budget is exhausted. What would you expect AutoML to return in case the time budget is exhausted before hyperparameter tuning is completed?

  • A. A random hyperparameter configuration
  • B. The current best-known hyperparameter configuration
  • C. The last generated hyperparameter configuration
  • D. A hyperparameter configuration with a minimum learning rate

Answer: B

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Predict AutoML's output when time runs out during tuning.
* Understand AutoML Tuning: Iteratively tests hyperparameters, tracks best results.
* Evaluate Options:
* A: Best-known config-Logical, reflects optimization goal-correct.
* B: Last config-Ignores prior better results-incorrect.
* C: Minimum learning rate-Arbitrary, not performance-based.
* D: Random-Defeats tuning purpose.
* Reasoning: AutoML prioritizes the best config found within the budget.
* Conclusion: A is correct.
OCI AutoML documentation states: "If the time budget expires, AutoML returns the best hyperparameter configuration (A) identified during tuning based on performance metrics." Last (B), minimum (C), or random (D) configs aren't selected-only A aligns with OCI's optimization strategy.
Oracle Cloud Infrastructure AutoML Documentation, "Hyperparameter Tuning - Time Budget".


NEW QUESTION # 114
True or false? Data scientists typically need a combination of technical skills, nontechnical ones, and suitable personality traits to be successful.

  • A. True
  • B. False

Answer: A

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Assess required skills for data scientists.
* Analyze Skills:
* Technical: Coding, stats, ML.
* Nontechnical: Communication, business acumen.
* Traits: Curiosity, problem-solving.
* Reasoning: Success requires this mix-e.g., explaining models to stakeholders.
* Conclusion: A (True) is correct.
OCI documentation states: "Effective data scientists combine technical skills (e.g., Python), nontechnical skills (e.g., storytelling), and traits like analytical thinking." This holistic requirement is true (A), not false (B).
Oracle Cloud Infrastructure Data Science Documentation, "Data Scientist Skills".


NEW QUESTION # 115
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