PASS4SURE 1Z0-1110-25 EXAM PREP | 1Z0-1110-25 LAB QUESTIONS

Pass4sure 1z0-1110-25 Exam Prep | 1z0-1110-25 Lab Questions

Pass4sure 1z0-1110-25 Exam Prep | 1z0-1110-25 Lab Questions

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Oracle 1z0-1110-25 Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 2
  • 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 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
  • 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 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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Oracle Cloud Infrastructure 2025 Data Science Professional Sample Questions (Q140-Q145):

NEW QUESTION # 140
You are a data scientist using Oracle AutoML to produce a model and you are evaluating the score metric for the model. Which of the following TWO prevailing metrics would you use for evaluating a multiclass classification model?

  • A. Recall
  • B. Explained variance score
  • C. F1 Score
  • D. Mean squared error
  • E. R-Squared

Answer: A,C

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Select two metrics for multiclass classification in AutoML.
* Understand Multiclass Metrics: Focus on class-specific performance-classification, not regression.
* Evaluate Options:
* A. Recall: Measures true positives per class-key for multiclass-correct.
* B. Mean squared error: Regression metric-incorrect.
* C. F1 Score: Balances precision and recall-standard for multiclass-correct.
* D. R-Squared: Regression fit-incorrect.
* E. Explained variance: Regression metric-incorrect.
* Reasoning: A and C assess classification accuracy across multiple classes-fit AutoML's evaluation.
* Conclusion: A and C are correct.
OCI AutoML documentation states: "For multiclass classification, common evaluation metrics include recall (A) for per-class sensitivity and F1 Score (C) for balanced performance." B, D, and E are regression- focused-only A and C are supported and relevant per OCI's AutoML metrics suite.
Oracle Cloud Infrastructure AutoML Documentation, "Evaluation Metrics for Classification".


NEW QUESTION # 141
How are datasets exported in the OCI Data Labeling service?

  • A. As an XML file
  • B. As a binary file
  • C. As a line-delimited JSON file
  • D. As a CSV file

Answer: C

Explanation:
Detailed Answer in Step-by-Step Solution:
* Understand OCI Data Labeling Export: After annotation, datasets are exported for ML use.
* Check Supported Formats: OCI Data Labeling exports annotations in a structured, machine-readable format.
* Evaluate Options:
* A: Binary isn't a standard export format for annotations.
* B: XML isn't used; JSON is preferred for flexibility.
* C: Line-delimited JSON is the correct format, aligning with ML workflows.
* D: CSV is common but not the default for OCI Data Labeling.
* Conclusion: C matches the official export format.
OCI Data Labeling exports annotated datasets as line-delimited JSON files, which store each annotation as a separate JSON object per line, suitable for ML pipelines. This is explicitly stated in the documentation.
(Reference: Oracle Cloud Infrastructure Data Labeling Service Documentation, "Exporting Datasets").


NEW QUESTION # 142
Which architecture is based on the principle of "never trust, always verify"?

  • A. Zero trust
  • B. Defense in depth
  • C. Federated identity
  • D. Fluid perimeter

Answer: A

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 # 143
What is the name of the machine learning library used in Apache Spark?

  • A. HadoopML
  • B. Structured Streaming
  • C. MLib
  • D. GraphX

Answer: C

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify Apache Spark's ML library.
* Understand Spark: A big data framework with specialized libraries.
* Evaluate Options:
* A: MLib (correctly MLlib)-Spark's machine learning library.
* B: GraphX-Graph processing, not ML.
* C: Structured Streaming-Streaming data, not ML.
* D: HadoopML-Not a Spark library (Hadoop-related).
* Reasoning: MLlib is Spark's official ML toolkit (e.g., regression, clustering).
* Conclusion: A is correct (noting "MLib" should be "MLlib").
OCI Data Science supports Spark via Data Flow, where "MLlib (Machine Learning library) provides scalable ML algorithms." GraphX (B) and Structured Streaming (C) serve other purposes, and HadoopML (D) isn't real-MLlib (A) is the standard, despite the typo.
Oracle Cloud Infrastructure Data Flow Documentation, "Apache Spark MLlib".


NEW QUESTION # 144
Which type of file system does File Storage use?

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

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 # 145
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