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[General] Valid 1z0-1110-25 Test Registration & 1z0-1110-25 Reliable Dumps Files

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【General】 Valid 1z0-1110-25 Test Registration & 1z0-1110-25 Reliable Dumps Files

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Oracle 1z0-1110-25 Exam Syllabus Topics:
TopicDetails
Topic 1
  • 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 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
  • 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 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.

Oracle Cloud Infrastructure 2025 Data Science Professional Sample Questions (Q92-Q97):NEW QUESTION # 92
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. Accumulated Local Effects
  • B. Feature Dependence Explanations
  • C. Local Interpretable Model-Agnostic Explanations
  • D. Feature Permutation Importance Explanations
Answer: A
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 # 93
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. OneHotEncoder()
  • B. DataFrameLabelEncode()
  • C. auto_transform()
  • D. show_in_notebook()
Answer: A
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 # 94
You want to use ADSTuner to tune the hyperparameters of a supported model you recently trained. You have just started your search and want to reduce the computational cost as well as assess the quality of the model class that you are using. What is the most appropriate search space strategy to choose?
  • A. Perfunctory
  • B. Detailed
  • C. Pass a dictionary that defines a search space
  • D. ADSTuner doesn't need a search space to tune the hyperparameters
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Select an ADSTuner strategy to minimize cost and assess model quality.
* Understand ADSTuner: Optimizes hyperparameters with configurable search spaces.
* Evaluate Options:
* A: Detailed-Exhaustive, high cost-incorrect.
* B: No search space-False; tuning requires a space.
* C: Perfunctory-Quick, low-cost assessment-correct.
* D: Dictionary-Defines space but not a strategy.
* Reasoning: Perfunctory balances cost and initial quality check.
* Conclusion: C is correct.
OCI documentation states: "ADSTuner's perfunctory strategy (C) performs a quick, low-cost search to assess model quality, ideal for initial tuning." Detailed (A) is costly, B misstates requirements, and D is a method, not a strategy-only C fits the goal.
Oracle Cloud Infrastructure ADS SDK Documentation, "ADSTuner Search Strategies".

NEW QUESTION # 95
You are working as a data scientist for a healthcare company. They decide to analyze the data to find patterns in a large volume of electronic medical records. You are asked to build a PySpark solution to analyze these records in a JupyterLab notebook. What is the order of recommended stepsto develop a PySpark application in Oracle Cloud Infrastructure (OCI) Data Science?
  • A. Configure core-site.xml, install a PySpark conda environment, create a Data Flow application with the Accelerated Data Science (ADS) SDK, develop your PySpark application, launch a notebook session
  • B. Install a Spark conda environment, configure core-site.xml, launch a notebook session, create a Data Flow application with the Accelerated Data Science (ADS) SDK, develop your PySpark application
  • C. Launch a notebook session, configure core-site.xml, install a PySpark conda environment, develop your PySpark application, create a Data Flow application with the Accelerated Data Science (ADS) SDK
  • D. Launch a notebook session, install a PySpark conda environment, configure core-site.xml, develop your PySpark application, create a Data Flow application with the Accelerated Data Science (ADS) SDK
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Sequence steps for PySpark app development.
* Steps:
* Launch notebook: First-sets up environment.
* Install PySpark conda: Second-adds Spark libraries.
* Configure core-site.xml: Third-connects to data.
* Develop app: Fourth-writes code.
* Data Flow: Fifth-scales (optional).
* Evaluate: D (1, 2, 3, 4, 5) matches this logical order.
* Reasoning: Notebook first, then setup and coding.
* Conclusion: D is correct.
OCI documentation states: "1) Launch a notebook session, 2) install a PySpark conda env, 3) configure core- site.xml, 4) develop your PySpark app, 5) optionally use Data Flow (D)." Other orders (A, B, C) misplace notebook launch or config-D is correct.
Oracle Cloud Infrastructure Data Science Documentation, "PySpark Development".

NEW QUESTION # 96
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. Oracle Digital Assistant
  • B. OCI Speech
  • C. OCI Language
  • D. OCI Vision
Answer: D
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 # 97
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