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Oracle 1z0-1110-25 Exam Syllabus Topics:

Topic	Details
Topic 1	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 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	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	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	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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Oracle Cloud Infrastructure 2025 Data Science Professional Sample Questions (Q24-Q29):

NEW QUESTION # 24
Which step is a part of the AutoML pipeline?

A. Feature Selection
B. Feature Extraction
C. Model Deployment
D. Model saved to Model Catalog

Answer: A

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify a step in OCI's AutoML pipeline.
* Understand AutoML: Automates model building-includes preprocessing, selection, and tuning.
* Evaluate Options:
* A: Feature Extraction (e.g., PCA) isn't explicitly part of OCI AutoML-too specific.
* B: Saving to Model Catalog is post-AutoML, not a pipeline step.
* C: Deployment is a separate action after AutoML-incorrect.
* D: Feature Selection (e.g., choosing relevant features) is a core AutoML step-correct.
* Reasoning: OCI AutoML automates feature selection, algorithm choice, and tuning-D fits.
* Conclusion: D is correct.
OCI AutoML's pipeline includes "feature selection, algorithm selection, adaptive sampling, and hyperparameter tuning," per the documentation. Extraction (A) isn't highlighted, while saving (B) and deployment (C) are post-process actions-only Feature Selection (D) is an integral automated step.
Oracle Cloud Infrastructure Data Science Documentation, "AutoML Pipeline".

NEW QUESTION # 25
Which THREE types of data are used for Data Labeling?

A. Images
B. Text Document
C. Audio
D. Graphs

Answer: A,B

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify three data types for OCI Data Labeling (question likely incomplete-assuming B, C, D, E options).
* Understand Data Labeling: Annotates data for ML-focuses on specific types.
* Evaluate Options (Assuming Typical Set):
* A: Audio-Not supported-incorrect.
* B: Text Document-Supported (e.g., NER)-correct.
* C: Images-Supported (e.g., object detection)-correct.
* D: Graphs-Not a standard type-incorrect.
* Assumed E: Videos-Supported but missing-adjust to fit.
* Reasoning: OCI supports text, images, and videos-question lists only four, so B and C are definite.
* Conclusion: B, C (third likely video, missing).
OCI documentation states: "Data Labeling supports text documents (B), images (C), and videos for annotation-audio (A) and graphs (D) are not included." Question likely meant three from a larger set; B and C are confirmed per OCI's Data Labeling capabilities.
Oracle Cloud Infrastructure Data Labeling Documentation, "Supported Data Types".

NEW QUESTION # 26
Which statement is true about standards?

A. They may be audited
B. They are the foundation of corporate governance
C. They are methods and instructions on how to maintain or accomplish the directives of the policy
D. They are the result of a regulation or contractual requirement or an industry requirement

Answer: A

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify a true statement about standards in an OCI context (likely governance/security).
* Understand Standards: Rules or benchmarks, often compliance-related.
* Evaluate Options:
* A: Auditable-True; standards are checked for adherence.
* B: Result of requirements-Partially true, but not always.
* C: Methods/instructions-More procedural, not defining standards.
* D: Foundation of governance-Broad, not specific to standards.
* Reasoning: A is universally true-standards face audits (e.g., SOC, ISO).
* Conclusion: A is correct.
OCI documentation notes: "Standards (e.g., security standards) may be audited (A) to ensure compliance with OCI policies or external regulations." B is a source, C describes procedures, D is too vague-only A is consistently true per OCI's compliance framework.
Oracle Cloud Infrastructure Security Documentation, "Compliance and Standards".

NEW QUESTION # 27
As a data scientist, you are working on a global health dataset that has data from more than 50 countries. You want to encode three features, such as 'countries', 'race', and 'body organ' as categories. Which option would you use to encode the categorical feature?

A. auto_transform()
B. DataFrameLabelEncode()
C. show_in_notebook()
D. OneHotEncoder()

Answer: D

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Encode categorical features in a Data Science context (likely ADS SDK).
* Understand Encoding: Converts categories (e.g., countries) to numerical forms.
* Evaluate Options:
* A: Not a standard ADS method-incorrect.
* B: General transformation, not specific encoding-incorrect.
* C: OneHotEncoder-Standard for categorical encoding-correct.
* D: Visualization, not encoding-incorrect.
* Reasoning: One-hot encoding creates binary columns-ideal for multiple categories.
* Conclusion: C is correct.
OCI documentation states: "In ADS SDK, use OneHotEncoder (C) from sklearn (or similar) to encode categorical features like 'countries' into binary vectors for modeling." A isn't real, B is too broad, D is unrelated-only C fits OCI's encoding practice.
Oracle Cloud Infrastructure Data Science Documentation, "Feature Encoding with ADS".

NEW QUESTION # 28
Which feature of Oracle Cloud Infrastructure Data Science provides an interactive coding environment for building and training machine learning models?

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

Answer: B

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 # 29
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