HIGH-QUALITY 1Z0-1110-25 EXAMINATIONS ACTUAL QUESTIONS - 100% PASS-RATE SOURCE OF 1Z0-1110-25 EXAM

High-quality 1z0-1110-25 Examinations Actual Questions - 100% Pass-Rate Source of 1z0-1110-25 Exam

High-quality 1z0-1110-25 Examinations Actual Questions - 100% Pass-Rate Source of 1z0-1110-25 Exam

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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
  • 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 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
  • 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 (Q158-Q163):

NEW QUESTION # 158
On which option do you set Oracle Cloud Infrastructure Budget?

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

Answer: B

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 # 159
Which statement best describes Oracle Cloud Infrastructure Data Science Jobs?

  • A. Jobs let you define and run all Oracle Cloud DevOps workloads.
  • B. Jobs let you define and run repeatable tasks on fully managed third-party cloud infrastructures.
  • C. Jobs let you define and run repeatable tasks on customer-managed infrastructure.
  • D. Jobs let you define and run repeatable tasks on fully managed infrastructure.

Answer: D

Explanation:
Detailed Answer in Step-by-Step Solution:
* Understand OCI Data Science Jobs: This service automates ML tasks (e.g., training, evaluation) with configurable, repeatable executions.
* Key Characteristics: Jobs run on OCI's infrastructure, managed by Oracle, not the customer or third parties, and are specific to Data Science, not general DevOps.
* Evaluate Options:
* A: Correct-Jobs are defined by users (e.g., via scripts) and executed on OCI's fully managed compute resources.
* B: Incorrect-Infrastructure is managed by OCI, not the customer.
* C: Incorrect-No third-party cloud integration; it's OCI-specific.
* D: Incorrect-Jobs are for Data Science tasks (e.g., ML training), not all DevOps workloads (e.
g., CI/CD pipelines).
* Reasoning: "Fully managed" means OCI handles provisioning and scaling, aligning with A.
* Conclusion: A accurately reflects the service's purpose and operation.
OCI Data Science Jobs "allow users to define and execute repeatable machine learning tasks, such as model training or batch processing, on fully managed OCI infrastructure." This eliminates customer management (B), third-party clouds (C), or broad DevOps scope (D). The documentation emphasizes automation and management by OCI, making A the precise description.
Oracle Cloud Infrastructure Data Science Documentation, "Overview of Jobs" section.


NEW QUESTION # 160
You have just received a new dataset from a colleague. You want to quickly find out summary information about the dataset, such as the types of features, the total number of observations, and distributions of the data.
Which Accelerated Data Science (ADS) SDK method from the ADSDataset class would you use?

  • A. show_corr()
  • B. compute()
  • C. to_xgb()
  • D. show_in_notebook()

Answer: D

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Get summary info from an ADSDataset object.
* Evaluate Options:
* A: Correlation matrix-Specific, not full summary.
* B: Converts to XGBoost-Not for summary.
* C: Executes computation-Not summary-focused.
* D: Displays summary (types, counts, dist)-correct.
* Reasoning: show_in_notebook() provides a comprehensive overview.
* Conclusion: D is correct.
OCI documentation states: "show_in_notebook() (D) from ADSDataset displays a summary of the dataset, including feature types, observation count, and distributions, in a notebook." A is partial, B and C are unrelated-only D meets the need per ADS SDK.
Oracle Cloud Infrastructure ADS SDK Documentation, "ADSDataset Methods".


NEW QUESTION # 161
Which cache rules criterion matches if the concatenation of the requested URL path and query are identical to the contents of the value field?

  • A. URL_PART_CONTAINS
  • B. URL_PART_ENDS_WITH
  • C. URL_IS
  • D. URL_STARTS_WITH

Answer: C

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Match a cache rule criterion for exact URL path and query.
* Understand Cache Rules: Used in OCI (e.g., WAF, CDN) to cache content.
* Evaluate Options:
* A: Contains-Partial match, not exact.
* B: Is-Exact match of full URL (path + query)-correct.
* C: Ends with-Matches end, not full URL.
* D: Starts with-Matches start, not full URL.
* Reasoning: "URL_IS" checks exact equality-fits requirement.
* Conclusion: B is correct.
OCI documentation states: "The URL_IS (B) criterion in cache rules matches when the full URL (path and query) exactly equals the specified value." A, C, and D are partial matches-only B ensures identical concatenation per OCI's caching config.
Oracle Cloud Infrastructure WAF Documentation, "Cache Rules Criteria".


NEW QUESTION # 162
You are a data scientist working for a utilities company. You have developed an algorithm that detects anomalies from a utility reader in the grid. The size of the model artifact is about 2 GB, and you are trying to store it in the model catalog. Which THREE interfaces could you use to save the model artifact into the model catalog?

  • A. OCI Python SDK
  • B. ODSC CLI
  • C. Git CLI
  • D. Oracle Cloud Infrastructure (OCI) Command Line Interface (CLI)
  • E. Accelerated Data Science (ADS) Software Development Kit (SDK)
  • F. Console

Answer: A,E,F

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify interfaces to save a 2 GB model to the Model Catalog.
* Evaluate Options:
* A: OCI CLI-Supports Data Science tasks-possible but not primary.
* B: ADS SDK-Designed for model catalog ops-correct.
* C: ODSC CLI-Not standard; likely typo for OCI CLI.
* D: Console-GUI for catalog uploads-correct.
* E: OCI Python SDK-Programmatic catalog access-correct.
* F: Git CLI-Version control, not catalog-related.
* Reasoning: B, D, E are OCI's primary interfaces; A is valid but less emphasized.
* Conclusion: B, D, E are correct (A plausible but not top-tier).
OCI documentation lists "ADS SDK (B), OCI Console (D), and OCI Python SDK (E) as primary methods to save models to the Model Catalog." OCI CLI (A) works but isn't highlighted, C isn't real, and F is unrelated- B, D, E are the standard trio.
Oracle Cloud Infrastructure Data Science Documentation, "Model Catalog Interfaces".


NEW QUESTION # 163
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