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Oracle 1z0-1110-25 Exam Syllabus Topics:

Topic
Details

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

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 3

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 4

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 5

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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Oracle Cloud Infrastructure 2025 Data Science Professional Sample Questions (Q143-Q148):

NEW QUESTION # 143
On which option do you set Oracle Cloud Infrastructure Budget?

A. Free-form tags
B. Compartments
C. Tenancy
D. Instances

Answer: C

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Determine where OCI budgets are set.
* Understand Budgets: Track spending across OCI resources.
* Evaluate Options:
* A: Compartments-Scoped within tenancy, not budget root.
* B: Instances-Specific resources, not budget scope.
* C: Tags-Filter costs, not budget setting.
* D: Tenancy-Top-level scope for budgets-correct.
* Reasoning: Budgets apply at tenancy, optionally filtered (e.g., by compartment).
* Conclusion: D is correct.
OCI documentation states: "Budgets are set at the tenancy level (D), with optional filters like compartments or tags to monitor spending." A, B, and C are sub-elements-only D is the primary scope per OCI's cost management.
Oracle Cloud Infrastructure Cost Management Documentation, "Setting Budgets".

NEW QUESTION # 144
Which feature of Oracle Cloud Infrastructure Data Science provides an interactive coding environment for building and training machine learning models?

A. Projects
B. Jobs
C. Notebook Sessions
D. Model Catalog

Answer: C

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify the interactive coding environment in OCI Data Science.
* Evaluate Options:
* A: Model Catalog stores models-not for coding.
* B: Jobs run predefined tasks-not interactive.
* C: Notebook Sessions provide JupyterLab for coding and training-interactive.
* D: Projects organize work-not a coding environment.
* Reasoning: Notebook Sessions are OCI's Jupyter-based tool for interactive ML development.
* Conclusion: C is correct.
OCI Data Science Notebook Sessions "provide an interactive JupyterLab environment where data scientists can write code, explore data, and train machine learning models." Model Catalog (A) is for storage, Jobs (B) for automation, and Projects (D) for organization-only C offers interactivity.
Oracle Cloud Infrastructure Data Science Documentation, "Notebook Sessions Overview".

NEW QUESTION # 145
What do you use the score.py file for?

A. Define the required conda environment
B. Configure the deployment infrastructure
C. Define the scaling strategy
D. Execute the inference logic code

Answer: D

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Determine the purpose of score.py in OCI Data Science model deployment.
* Understand Model Deployment: When deploying a model in OCI, artifacts include score.py, runtime.
yaml, etc.
* Evaluate Options:
* A: Infrastructure configuration (e.g., compute shape) is handled by deployment settings, not score.
py.
* B: score.py contains the inference logic (e.g., load_model(), predict())-correct.
* C: Conda environment is defined in runtime.yaml or a requirements file-not score.py.
* D: Scaling (e.g., instance count) is set in deployment configuration-not score.py.
* Reasoning: score.py is the script executed by the deployment endpoint to load the model and make predictions.
* Conclusion: B is the correct purpose.
The OCI Data Science documentation states: "The score.py file is a required artifact for model deployment, containing the inference logic-functions like load_model() to load the model and predict() to generate predictions from input data." Infrastructure (A) and scaling (D) are managed via the OCI Console or SDK, while the environment (C) is specified in runtime.yaml. B is the precise role of score.py in OCI's deployment workflow.
Oracle Cloud Infrastructure Data Science Documentation, "Model Deployment - score.py".

NEW QUESTION # 146
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. Named entity extraction
B. Classification (single/multi-label)
C. Semantic segmentation
D. Key-point and landmark
E. Object detection
F. Polygonal segmentation

Answer: B,C,E

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 # 147
How can you collaborate with team members in OCI Data Science Workspace?

A. By granting access to specific notebooks and files
B. By using version control systems integrated with the workspace
C. By enabling chat and video conferencing within the workspace
D. By sharing the workspace instance with other users

Answer: B

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Determine collaboration method in OCI Data Science (Notebook Sessions).
* Evaluate Options:
* A: Access control-Possible but not primary collaboration.
* B: Version control (e.g., Git)-Standard for code sharing-correct.
* C: Shared instance-Not supported; sessions are single-user.
* D: Chat/video-Not a feature of OCI Data Science.
* Reasoning: B leverages Git for team collaboration-OCI's recommended method.
* Conclusion: B is correct.
OCI documentation states: "Collaborate in Data Science by integrating version control systems like Git (B) with notebook sessions to share code and notebooks." A is limited, C isn't feasible, and D isn't available- only B matches OCI's collaboration approach.
Oracle Cloud Infrastructure Data Science Documentation, "Collaboration with Git".

NEW QUESTION # 148
......

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James Martin James Martin

Biography

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