ORACLE 1Z0-1110-25 RELIABLE DUMPS SHEET | 1Z0-1110-25 RELIABLE GUIDE FILES

Oracle 1z0-1110-25 Reliable Dumps Sheet | 1z0-1110-25 Reliable Guide Files

Oracle 1z0-1110-25 Reliable Dumps Sheet | 1z0-1110-25 Reliable Guide Files

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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
  • 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
  • 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
  • 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 5
  • 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.

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Oracle Cloud Infrastructure 2025 Data Science Professional Sample Questions (Q99-Q104):

NEW QUESTION # 99
What is the minimum active storage duration for logs used by Logging Analytics to be archived?

  • A. 15 days
  • B. 30 days
  • C. 60 days
  • D. 10 days

Answer: B

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Determine minimum log storage duration before archiving in Logging Analytics.
* Understand Logging Analytics: Logs are active before archival.
* Evaluate Options:
* A: 60 days-Too long for minimum.
* B: 10 days-Too short.
* C: 30 days-Standard minimum-correct.
* D: 15 days-Not OCI's default.
* Reasoning: 30 days is OCI's documented minimum active period.
* Conclusion: C is correct.
OCI documentation states: "Logs in Logging Analytics remain active for a minimum of 30 days (C) before archiving, ensuring availability for analysis." B and D are shorter, A is longer-only C matches OCI's policy.
Oracle Cloud Infrastructure Logging Analytics Documentation, "Log Retention".


NEW QUESTION # 100
You are a data scientist trying to load data into your notebook session. You understand that Accelerated Data Science (ADS) SDK supports loading various data formats. Which of the following THREE are ADS- supported data formats?

  • A. DOCX
  • B. Raw Images
  • C. JSON
  • D. XML
  • E. Pandas DataFrame

Answer: C,D,E

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify three data formats supported by ADS SDK for loading data.
* Understand ADS SDK: Facilitates data loading into notebook sessions via DatasetFactory.
* Evaluate Options:
* A. DOCX: Not natively supported-requires conversion (e.g., to text).
* B. Pandas DataFrame: Supported-core format for data manipulation in ADS.
* C. JSON: Supported-common structured data format.
* D. Raw Images: Not directly supported-image data needs preprocessing (e.g., via Vision).
* E. XML: Supported-parseable structured format.
* Reasoning: ADS focuses on tabular/structured data-B, C, E align; A and D require external handling.
* Conclusion: B, C, E are correct.
OCI documentation states: "ADS SDK's DatasetFactory supports loading data from formats like Pandas DataFrames (B), JSON (C), and XML (E), enabling easy integration into notebook sessions." DOCX (A) isn't natively handled, and raw images (D) require preprocessing outside ADS-B, C, E match the supported list.
Oracle Cloud Infrastructure ADS SDK Documentation, "Supported Data Formats".


NEW QUESTION # 101
While working with Git on Oracle Cloud Infrastructure (OCI) Data Science, you notice that two of the operations are taking more time than the others due to your slow internet speed. Which TWO operations would experience the delay?

  • A. Moving the changes into staging area for the next commit
  • B. Making a commit that is taking a snapshot of the local repository for the next push
  • C. Converting an existing local project folder to a Git repository
  • D. Updating the local repo to match the content from a remote repository
  • E. Pushing changes to a remote repository

Answer: D,E

Explanation:
Detailed Answer in Step-by-Step Solution:
* Analyze Git Operations: Identify which depend on internet speed.
* Evaluate Options:
* A. Staging (git add): Local operation-adds files to the index; no network involved.
* B. Updating local repo (git pull): Downloads remote changes-requires internet, slowed by poor connectivity.
* C. Pushing changes (git push): Uploads local commits to remote-network-dependent, delayed by slow speed.
* D. Committing (git commit): Local snapshot-no network needed.
* E. Converting to Git repo (git init): Local initialization-no internet required.
* Reasoning: Only B and C involve network transfers, directly impacted by slow internet.
* Conclusion: B and C are the correct choices.
Git operations like git pull (B) and git push (C) rely on network communication with a remote repository, such as OCI Code Repository, and are documented as "bandwidth-sensitive" in OCI's guides. Local actions like staging (A), committing (D), and initializing (E) occur on the user's machine, unaffected by internet speed. This matches standard Git behavior and OCI's implementation.
Oracle Cloud Infrastructure Data Science Documentation, "Using Git in Notebook Sessions".


NEW QUESTION # 102
A bike sharing platform has collected user commute data for the past 3 years. For increasing profitability and making useful inferences, a machine learning model needs to be built from the accumulated data. Which of the following options has the correct order of the required machine learning tasks for building a model?

  • A. Data Access, Data Exploration, Feature Engineering, Feature Exploration, Modeling
  • B. Data Access, Data Exploration, Feature Exploration, Feature Engineering, Modeling
  • C. Data Access, Feature Exploration, Feature Engineering, Data Exploration, Modeling
  • D. Data Access, Feature Exploration, Data Exploration, Feature Engineering, Modeling

Answer: A

Explanation:
Detailed Answer in Step-by-Step Solution:
* Data Access: The first step in any machine learning workflow is accessing the raw data. This involves retrieving the user commute data collected over the past 3 years from the bike-sharing platform's storage system.
* Data Exploration: Once data is accessed, it's explored to understand its structure, quality, and patterns (e.g., missing values, distributions). This step helps identify what preprocessing is needed.
* Feature Engineering: After understanding the data, features are created or transformed (e.g., commute duration, time of day) to improve model performance. This step precedes feature exploration because you need engineered features to analyze further.
* Feature Exploration: This involves analyzing the engineered features (e.g., correlation analysis, importance ranking) to refine them or select the most relevant ones for modeling.
* Modeling: Finally, the prepared data and features are used to train and evaluate a machine learning model.
Option C (Data Access, Data Exploration, Feature Engineering, Feature Exploration, Modeling) follows this logical sequence, aligning with standard ML workflows.
The correct order reflects the machine learning lifecycle as outlined in Oracle's OCI Data Science documentation. Data Access is the initial step to retrieve data, followed by Data Exploration to assess it (e.g., using OCI Data Science Notebook Sessions with tools like pandas). Feature Engineering transforms raw data into meaningful inputs, followed by Feature Exploration to analyze feature importance (e.g., using ADS SDK' s correlation tools). Modeling is the final step where the model is built and trained. This sequence is consistent with Oracle's recommended practices for building ML models in OCI Data Science (Reference: Oracle Cloud Infrastructure Data Science Service Documentation, "Machine Learning Lifecycle").


NEW QUESTION # 103
You are attempting to save a model from a notebook session to the model catalog by using ADS SDK, with resource principal as the authentication signer, and you get a 404 authentication error. Which TWO should you look for to ensure permissions are set up correctly?

  • A. The model artifact is saved to the block volume of the notebook session
  • B. The networking configuration allows access to the Oracle Cloud Infrastructure services through a service gateway
  • C. The dynamic groups matching rule exists for notebook sessions in the compartment
  • D. The policy for your user group grants manage permissions for the model catalog in this compartment
  • E. The policy for the dynamic group grants manage permissions for the model catalog in this compartment

Answer: C,E

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Troubleshoot a 404 authentication error when saving a model using ADS SDK with resource principal.
* Understand Resource Principal: Allows notebook sessions to act as principals via dynamic groups and policies-no user credentials needed.
* Analyze 404 Error: Indicates an authorization failure-likely missing permissions or misconfigured resource principal.
* Evaluate Options:
* A: True-Dynamic group must include notebook sessions (e.g., resource.type =
'datasciencenotebooksession') to authenticate.
* B: False-Block volume stores artifacts locally, but saving to the catalog is a permission issue, not storage.
* C: True-Policy must grant manage data-science-models to the dynamic group for catalog access.
* D: False-Service gateway ensures network access, but 404 is auth-related, not connectivity.
* E: False-Resource principal uses dynamic group policies, not user group policies.
* Reasoning: A (group inclusion) and C (policy permission) are critical for resource principal auth- others are tangential.
* Conclusion: A and C are correct.
OCI documentation states: "To use resource principal with ADS SDK for model catalog operations, ensure (1) a dynamic group includes the notebook session with a matching rule (e.g., all {resource.type =
'datasciencenotebooksession'}) and (2) a policy grants the dynamic group manage data-science-models permissions in the compartment." B is unrelated (storage location), D is network-focused, and E applies to user auth-not resource principal. A 404 error flags missing auth, fixed by A and C.
Oracle Cloud Infrastructure Data Science Documentation, "Using Resource Principals with ADS SDK".


NEW QUESTION # 104
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The Oracle Cloud Infrastructure 2025 Data Science Professional 1z0-1110-25 certification provides both novices and experts with a fantastic opportunity to show off their knowledge of and proficiency in carrying out a particular task. With the Oracle 1z0-1110-25 exam, you will have the chance to update your knowledge while obtaining dependable evidence of your proficiency. You can also get help from actual Oracle Cloud Infrastructure 2025 Data Science Professional 1z0-1110-25 Exam Questions and pass your dream Oracle Cloud Infrastructure 2025 Data Science Professional 1z0-1110-25 certification exam.

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