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[General] 1z0-1110-25 Free Practice & 1z0-1110-25 New Dumps Free

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【General】 1z0-1110-25 Free Practice & 1z0-1110-25 New Dumps Free

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Oracle 1z0-1110-25 Exam Syllabus Topics:
TopicDetails
Topic 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.
Topic 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.
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
  • 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 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 Sample Questions (Q111-Q116):NEW QUESTION # 111
What is the correct definition of Git?
  • A. Git is a distributed version control system that protects teams from simultaneous repo contributions and merge requests.
  • B. Git is a centralized version control system that allows data scientists and developers to track copious amounts of data.
  • C. Git is a distributed version control system that allows you to track changes made to a set of files.
  • D. Git is a centralized version control system that allows you to revert to previous versions of files as needed.
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
* Define Git: Git is a version control system-centralized vs. distributed is key.
* Evaluate Options:
* A: Incorrect-Git is distributed, not centralized (e.g., SVN is centralized).
* B: Correct-Distributed, tracks file changes across local and remote repos.
* C: Incorrect-Git allows simultaneous contributions; it manages, not prevents, merges.
* D: Incorrect-Centralized is wrong, and "copious data" is vague.
* Reasoning: Git's distributed nature (each user has a full repo copy) and change-tracking are core traits.
* Conclusion: B is accurate.
OCI documentation aligns with Git's official definition: "Git is a distributed version control system that tracks changes to files, enabling collaboration and version history management." A and D misclassify it as centralized, while C misrepresents merge handling-B captures Git's essence as used in OCI Data Science.
Oracle Cloud Infrastructure Code Repository Documentation, "Git Overview".

NEW QUESTION # 112
When preparing your model artifact to save it to the Oracle Cloud Infrastructure (OCI) DataScience model catalog, you create a score.py file. What is the purpose of the score.py file?
  • A. Execute the inference logic code
  • B. Define the inference server dependencies
  • C. Configure the deployment infrastructure
  • D. Define the compute scaling strategy
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Define the role of score.py in OCI model artifacts.
* Understand Artifacts: score.py is key for deployment runtime.
* Evaluate Options:
* A: Infra config-Handled by OCI settings, not score.py.
* B: Inference logic-Correct; runs load_model(), predict().
* C: Scaling-Set in deployment, not score.py.
* D: Dependencies-In runtime.yaml, not score.py.
* Reasoning: B aligns with score.py's execution role.
* Conclusion: B is correct.
OCI documentation states: "score.py (B) contains the inference logic, including functions to load the model and predict outputs, executed by the deployment endpoint." A, C, and D are managed elsewhere-only B matches OCI's design.
Oracle Cloud Infrastructure Data Science Documentation, "Model Artifact - score.py".

NEW QUESTION # 113
Which Oracle Cloud Service provides restricted access to target resources?
  • A. Bastion
  • B. Internet Gateway
  • C. Load Balancer
  • D. SSL Certificate
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify the OCI service for restricted resource access.
* Evaluate Options:
* A: Bastion-Secure, temporary access to resources-correct.
* B: Internet Gateway-Public access, not restricted.
* C: Load Balancer-Distributes traffic, not access control.
* D: SSL Certificate-Secures comms, not access.
* Reasoning: Bastion limits access (e.g., SSH) to specific targets.
* Conclusion: A is correct.
OCI documentation states: "OCI Bastion (A) provides restricted, audited access to target resources like instances, typically via SSH." B, C, and D don't restrict-only A fits per OCI's security services.
Oracle Cloud Infrastructure Bastion Documentation, "Overview".

NEW QUESTION # 114
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. The last generated hyperparameter configuration
  • B. A random hyperparameter configuration
  • C. A hyperparameter configuration with a minimum learning rate
  • D. The current best-known hyperparameter configuration
Answer: D
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 # 115
You are a data scientist with a set of text and image files that need annotation, and you want to use Oracle Cloud Infrastructure (OCI) Data Labeling. Which of the following THREE annotation classes are supported by the tool?
  • A. Object detection
  • B. Named entity extraction
  • C. Polygonal segmentation
  • D. Semantic segmentation
  • E. Key-point and landmark
  • F. Classification (single/multi-label)
Answer: A,D,F
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify supported annotation classes in OCI Data Labeling.
* Understand Tool: Supports image/text annotations for ML.
* Evaluate Options:
* A: Object detection-Yes (bounding boxes).
* B: Named entity-Text-specific, not primary for images.
* C: Classification-Yes (labels for images/text).
* D: Key-point-Not listed in OCI docs.
* E: Polygonal-Not explicitly supported.
* F: Semantic segmentation-Yes (pixel-level).
* Reasoning: A, C, F match OCI's image/text focus.
* Conclusion: A, C, F are correct.
OCI Data Labeling supports "object detection (A), classification (C), and semantic segmentation (F) for images and text," per documentation. B is text-specific, D and E aren't highlighted-only A, C, F are core classes.
Oracle Cloud Infrastructure Data Labeling Documentation, "Annotation Types".

NEW QUESTION # 116
......
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