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Quiz 1z0-1110-25 - Oracle Cloud Infrastructure 2025 Data Science Professional–Professional Latest Exam Test
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Oracle 1z0-1110-25 Exam Syllabus Topics:
Topic
Details
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
- 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 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.
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
- 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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Oracle Cloud Infrastructure 2025 Data Science Professional Sample Questions (Q158-Q163):
NEW QUESTION # 158
What is a common maxim about data scientists?
- A. They spend 80% of their time on failed analytics projects and 20% doing useful work.
- B. They spend 80% of their time analyzing data and 20% finding and preparing it.
- C. They spend 80% of their time finding and preparing data and 20% analyzing it.
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify a widely accepted maxim about data scientists' time allocation.
* Understand Data Science Workflow: Involves data collection, preparation, and analysis-time distribution is key.
* Evaluate Options:
* A: 80% on finding/preparing, 20% analyzing-Reflects the data wrangling challenge.
* B: 80% analyzing, 20% finding/preparing-Inverts the common perception.
* C: 80% on failed projects, 20% useful-Pessimistic, not a standard maxim.
* Reasoning: Industry consensus (e.g., "80/20 rule") emphasizes data prep as the bulk of effort due to messy real-world data.
* Conclusion: A is correct.
OCI Data Science documentation aligns with industry norms: "Data scientists typically spend 80% of their time finding, cleaning, and preparing data, and 20% on analysis and modeling, due to the complexity of raw data." B reverses this, and C isn't supported-only A reflects this widely cited maxim from sources like Forbes and OCI's practical guidance.
Oracle Cloud Infrastructure Data Science Documentation, "Data Science WorkflowOverview".
NEW QUESTION # 159
Which architecture is based on the principle of "never trust, always verify"?
- A. Federated identity
- B. Zero trust
- C. Fluid perimeter
- D. Defense in depth
Answer: B
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify the architecture with "never trust, always verify."
* Evaluate Options:
* A: Federated identity-Shares auth, not verification-focused.
* B: Zero trust-Explicitly "never trust, always verify"-correct.
* C: Fluid perimeter-Adaptive, not the core principle.
* D: Defense in depth-Layered, not verification-centric.
* Reasoning: Zero trust matches the stated principle exactly.
* Conclusion: B is correct.
OCI documentation states: "Zero trust (B) architecture operates on 'never trust, always verify,' requiring continuous authentication and authorization." A, C, and D have different focuses-only B aligns with OCI's security philosophy.
Oracle Cloud Infrastructure Security Documentation, "Zero Trust Architecture".
NEW QUESTION # 160
You want to write a program that performs document analysis tasks such as extracting text and tables from a document. Which Oracle AI service would you use?
- A. OCI Vision
- B. OCI Language
- C. Oracle Digital Assistant
- D. OCI Speech
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Select an OCI AI service for text and table extraction from documents.
* Evaluate Options:
* A: Language-Text analysis, not extraction-incorrect.
* B: Digital Assistant-Chatbots, not document tasks-incorrect.
* C: Speech-Audio transcription, not documents-incorrect.
* D: Vision-OCR for text/tables-correct.
* Reasoning: Vision's OCR extracts text and tables from document images.
* Conclusion: D is correct.
OCI documentation states: "OCI Vision (D) uses OCR to extract text and tables from documents, supporting document analysis tasks." A analyzes text post-extraction, B and C are unrelated-only D fits per OCI's AI services.
Oracle Cloud Infrastructure Vision Documentation, "Document Analysis Features".
NEW QUESTION # 161
Which feature of Oracle Cloud Infrastructure Data Science provides an interactive coding environment for building and training machine learning models?
- A. Model Catalog
- B. Jobs
- C. Projects
- D. Notebook Sessions
Answer: D
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 # 162
What does the Data Science Service template in Oracle Resource Manager (ORM) NOTautomatically create?
- A. Required user groups
- B. Policies for a basic use case
- C. Individual Data Science users
- D. Dynamic groups
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
* Understand ORM Template: It automates OCI Data Science setup with predefined configurations.
* Evaluate Components:
* A: User groups are created for role-based access-automated.
* B: Dynamic groups (e.g., for notebook sessions) are included-automated.
* C: Individual users require manual creation via IAM-not automated.
* D: Basic policies (e.g., access to Data Science resources) are included-automated.
* Reasoning: ORM focuses on infrastructure and permissions, not user accounts.
* Conclusion: C is the exception.
The OCI Resource Manager template for Data Science "automatically provisions user groups, dynamic groups, and policies for basic use cases," but "individual users must be created separately in IAM and assigned to groups." C is the only item not handled by the template, per the documentation.
Oracle Cloud Infrastructure Resource Manager Documentation, "Data Science Template".
NEW QUESTION # 163
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