100% PASS 2025 AUTHORITATIVE 1Z0-1110-25: ORACLE CLOUD INFRASTRUCTURE 2025 DATA SCIENCE PROFESSIONAL EXAM TUTORIALS

100% Pass 2025 Authoritative 1z0-1110-25: Oracle Cloud Infrastructure 2025 Data Science Professional Exam Tutorials

100% Pass 2025 Authoritative 1z0-1110-25: Oracle Cloud Infrastructure 2025 Data Science Professional Exam Tutorials

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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
  • 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
  • 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
  • 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 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 (Q90-Q95):

NEW QUESTION # 90
Which type of file system does File Storage use?

  • A. NFSv3
  • B. Paravirtualized
  • C. iSCSI
  • D. NVMe SSD

Answer: A

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify the file system type for OCI File Storage.
* Understand File Storage: Network-attached storage in OCI.
* Evaluate Options:
* A: NFSv3-Network File System, used by File Storage-correct.
* B: iSCSI-Block storage protocol, not File Storage.
* C: Paravirtualized-Virtualization mode, not file system.
* D: NVMe SSD-Hardware, not file system.
* Reasoning: NFSv3 is OCI File Storage's protocol.
* Conclusion: A is correct.
OCI documentation states: "File Storage uses NFSv3 (A) as its file system protocol, providing shared storage across instances." B, C, and D are unrelated-only A aligns with OCI's File Storage design.
Oracle Cloud Infrastructure File Storage Documentation, "File System Protocol".


NEW QUESTION # 91
You are asked to prepare data for a custom-built model that requires transcribing Spanish video recordings into a readable text format with profane words identified. Which Oracle Cloud Service would you use?

  • A. OCI Language
  • B. OCI Anomaly Detection
  • C. OCI Speech
  • D. OCI Translation

Answer: C

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Transcribe Spanish video audio and identify profanity.
* Evaluate Options:
* A: Anomaly Detection-Not for transcription or text analysis.
* B: Speech-Converts audio to text (e.g., Spanish), base for further analysis-correct.
* C: Translation-Translates text, not transcription.
* D: Language-Analyzes text (e.g., profanity), but needs transcribed input.
* Reasoning: Speech (B) transcribes video audio; Language could follow for profanity.
* Conclusion: B is correct for transcription.
OCI Speech "transcribes audio from video or audio files into text, supporting languages like Spanish." Post- transcription, OCI Language could detect profanity, but B is the starting point-Anomaly (A) and Translation (C) don't fit.
Oracle Cloud Infrastructure Speech Documentation, "Transcription Features".


NEW QUESTION # 92
You have a dataset with fewer than 1000 observations, and you are using Oracle AutoML to build a classifier.
While visualizing the results of each stage of the Oracle AutoML pipeline, you notice that no visualization has been generated for one of the stages. Which stage is not visualized?

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

Answer: C

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify the non-visualized AutoML stage with small data.
* Understand AutoML Pipeline: Includes sampling, feature/algorithm selection, tuning.
* Evaluate Options:
* A: Feature selection-Visualized (e.g., feature importance).
* B: Algorithm selection-Visualized (e.g., algorithm scores).
* C: Adaptive sampling-Skipped/visualization absent for <1000 rows.
* D: Hyperparameter tuning-Visualized (e.g., trial plots).
* Reasoning: Adaptive sampling optimizes large datasets; small data skips it, omitting visuals.
* Conclusion: C is correct.
OCI AutoML documentation notes: "Adaptive sampling is applied to large datasets (>1000 rows) to reduce size; for smaller datasets, it's skipped, and no visualization is generated." Other stages (A, B,D) produce visuals-only C is absent here.
Oracle Cloud Infrastructure AutoML Documentation, "Pipeline Stages".


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. 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 # 94
How can you collaborate with team members in OCI Data Science Workspace?

  • A. By using version control systems integrated with the workspace
  • B. By sharing the workspace instance with other users
  • C. By enabling chat and video conferencing within the workspace
  • D. By granting access to specific notebooks and files

Answer: A

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Determine collaboration method in OCI Data Science (Notebook Sessions).
* Evaluate Options:
* A: Access control-Possible but not primary collaboration.
* B: Version control (e.g., Git)-Standard for code sharing-correct.
* C: Shared instance-Not supported; sessions are single-user.
* D: Chat/video-Not a feature of OCI Data Science.
* Reasoning: B leverages Git for team collaboration-OCI's recommended method.
* Conclusion: B is correct.
OCI documentation states: "Collaborate in Data Science by integrating version control systems like Git (B) with notebook sessions to share code and notebooks." A is limited, C isn't feasible, and D isn't available- only B matches OCI's collaboration approach.
Oracle Cloud Infrastructure Data Science Documentation, "Collaboration with Git".


NEW QUESTION # 95
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