1Z0-1110-25 HOT SPOT QUESTIONS & CERTIFICATION SUCCESS GUARANTEED, EASY WAY OF TRAINING & ORACLE ORACLE CLOUD INFRASTRUCTURE 2025 DATA SCIENCE PROFESSIONAL

1z0-1110-25 Hot Spot Questions & Certification Success Guaranteed, Easy Way of Training & Oracle Oracle Cloud Infrastructure 2025 Data Science Professional

1z0-1110-25 Hot Spot Questions & Certification Success Guaranteed, Easy Way of Training & Oracle Oracle Cloud Infrastructure 2025 Data Science Professional

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Oracle 1z0-1110-25 Exam Syllabus Topics:

TopicDetails
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
  • 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 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
  • 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 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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Quiz Oracle - 1z0-1110-25 - Reliable Oracle Cloud Infrastructure 2025 Data Science Professional Hot Spot Questions

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Oracle Cloud Infrastructure 2025 Data Science Professional Sample Questions (Q109-Q114):

NEW QUESTION # 109
You want to evaluate the relationship between feature values and target variables. You have a large number of observations having a near uniform distribution and the features are highly correlated. Which model explanation technique should you choose?

  • A. Feature Permutation Importance Explanations
  • B. Accumulated Local Effects
  • C. Feature Dependence Explanations
  • D. Local Interpretable Model-Agnostic Explanations

Answer: B

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Select an explanation technique for feature-target relationships with correlated features.
* Evaluate Options:
* A: Permutation-Breaks with high correlation.
* B: LIME-Local, not global relationships.
* C: Dependence-Not a standard term; vague.
* D: ALE-Handles correlation, shows feature effects-correct.
* Reasoning: ALE is robust to correlated features, ideal here.
* Conclusion: D is correct.
OCI documentation states: "Accumulated Local Effects (ALE) (D) evaluates feature-target relationships, accounting for correlations, unlike permutation importance (A) which falters with high correlation." B is local, C isn't defined-only D fits per OCI's explanation tools.
Oracle Cloud Infrastructure Data Science Documentation, "Model Explanation Techniques".


NEW QUESTION # 110
You are a data scientist designing an air traffic control model, and you choose to leverage Oracle AutoML.
You understand that the Oracle AutoML pipeline consists of multiple stages and automatically operates in a certain sequence. What is the correct sequence for the Oracle AutoML pipeline?

  • A. Algorithm selection, Adaptive sampling, Feature selection, Hyperparameter tuning
  • B. Adaptive sampling, Algorithm selection, Feature selection, Hyperparameter tuning
  • C. Adaptive sampling, Feature selection, Algorithm selection, Hyperparameter tuning
  • D. Algorithm selection, Feature selection, Adaptive sampling, Hyperparameter tuning

Answer: C

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Sequence OCI AutoML pipeline stages.
* Stages:
* Adaptive sampling: Reduces data size if large.
* Feature selection: Picks relevant features.
* Algorithm selection: Chooses best model type.
* Hyperparameter tuning: Optimizes model params.
* Evaluate: C (sampling, features, algorithms, tuning) matches logical flow-data first, then model.
* Reasoning: Sampling precedes feature work-standard in OCI.
* Conclusion: C is correct.
OCI documentation states: "AutoML pipeline runs 1) adaptive sampling, 2) feature selection, 3) algorithm selection, 4) hyperparameter tuning (C)." Sampling reduces data first, then features and models are optimized-other orders (A, B, D) misalign with OCI's sequence.
Oracle Cloud Infrastructure AutoML Documentation, "Pipeline Sequence".


NEW QUESTION # 111
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. Key-point and landmark
  • B. Semantic segmentation
  • C. Classification (single/multi-label)
  • D. Object detection
  • E. Polygonal segmentation
  • F. Named entity extraction

Answer: B,C,D

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 # 112
What is a conda environment?

  • A. A collection of kernels
  • B. An environment deployment system on Oracle AI
  • C. An open-source environment management system
  • D. A system that manages package dependencies

Answer: C

Explanation:
Detailed Answer in Step-by-Step Solution:
* Define Conda: Conda is a widely used tool for managing packages and environments in data science.
* Evaluate Options:
* A: Partially true-Conda manages dependencies, but it's broader (an environment system).
* B: Incorrect-Kernels (e.g., Jupyter) are separate; Conda manages environments.
* C: Correct-Conda is an open-source tool for creating isolated environments with specific packages.
* D: Incorrect-Not specific to Oracle AI; it's a general tool.
* Reasoning: C captures Conda's full scope as an open-source system, beyond just dependency management (A).
* Conclusion: C is the most accurate.
OCI documentation describes Conda as "an open-source package and environment management system that allows data scientists to create isolated environments with specific versions of Python and libraries." A is too narrow, B misaligns with kernel concepts, and D ties it incorrectly to Oracle AI. C aligns with Conda's official definition and OCI's usage.
Oracle Cloud Infrastructure Data Science Documentation, "Conda Environments Overview".


NEW QUESTION # 113
Which of the following programming languages are most widely used by data scientists?

  • A. Java and JavaScript
  • B. C and C++
  • C. Python, R, and SQL

Answer: C

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify top languages for data science.
* Evaluate Options:
* A: C/C++-Low-level, less common for data tasks.
* B: Python (ML, libraries), R (stats), SQL (data)-Industry standards.
* C: Java (enterprise), JavaScript (web)-Not data-focused.
* Reasoning: B aligns with data science tools (e.g., pandas, ggplot).
* Conclusion: B is correct.
OCI documentation highlights "Python, R, and SQL as the most widely used languages in Data Science for modeling, analysis, and data querying." C/C++ (A) and Java/JS (C) are less prevalent-B matches OCI's notebook support and industry trends.
Oracle Cloud Infrastructure Data Science Documentation, "Supported Languages".


NEW QUESTION # 114
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