STUDY YOUR ORACLE 1Z0-1110-25 EXAM WITH THE BEST ORACLE ACTUAL 1Z0-1110-25 TEST PDF EASILY

Study Your Oracle 1z0-1110-25 Exam with The Best Oracle Actual 1z0-1110-25 Test Pdf Easily

Study Your Oracle 1z0-1110-25 Exam with The Best Oracle Actual 1z0-1110-25 Test Pdf Easily

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Exam Oracle 1z0-1110-25 Questions Pdf - 1z0-1110-25 Latest Exam Notes

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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
  • 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
  • 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.

Oracle Cloud Infrastructure 2025 Data Science Professional Sample Questions (Q21-Q26):

NEW QUESTION # 21
True or false? Data scientists typically need a combination of technical skills, nontechnical ones, and suitable personality traits to be successful.

  • A. True
  • B. False

Answer: A

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Assess required skills for data scientists.
* Analyze Skills:
* Technical: Coding, stats, ML.
* Nontechnical: Communication, business acumen.
* Traits: Curiosity, problem-solving.
* Reasoning: Success requires this mix-e.g., explaining models to stakeholders.
* Conclusion: A (True) is correct.
OCI documentation states: "Effective data scientists combine technical skills (e.g., Python), nontechnical skills (e.g., storytelling), and traits like analytical thinking." This holistic requirement is true (A), not false (B).
Oracle Cloud Infrastructure Data Science Documentation, "Data Scientist Skills".


NEW QUESTION # 22
You have built a machine model to predict whether a bank customer is going to default on a loan. You want to use Local Interpretable Model-Agnostic Explanations (LIME) to understand a specific prediction. What is the key idea behind LIME?

  • A. Global behaviour of a machine learning model may be complex, while the local behaviour may be approximated with a simpler surrogate model
  • B. Model-agnostic techniques are more interpretable than techniques that are dependent on the types of models
  • C. Global and local behaviours of machine learning models are similar
  • D. Local explanation techniques are model-agnostic, while global explanation techniques are not

Answer: A

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Define LIME's core concept.
* Understand LIME: Explains individual predictions with local surrogate models.
* Evaluate Options:
* A: Complex global, simple local-Correct LIME principle.
* B: Agnosticism-True but not the key idea.
* C: Global/local similarity-False.
* D: Local vs. global agnosticism-Incorrect distinction.
* Reasoning: A captures LIME's local approximation focus.
* Conclusion: A is correct.
OCI documentation notes: "LIME (A) explains predictions by approximating complex global models with simpler local surrogate models around specific instances." B, C, and D misalign-only A reflects LIME's foundational idea per OCI's interpretability tools.
Oracle Cloud Infrastructure Data Science Documentation, "Model Interpretability - LIME".


NEW QUESTION # 23
You want to make your model more frugal to reduce the cost of collecting and processing data. You plan to do this by removing features that are highly correlated. You would like to create a heatmap that displays the correlation so that you can identify candidate features to remove. Which Accelerated Data Science (ADS) SDK method is appropriate to display the comparability between Continuous and Categorical features?

  • A. correlation_ratio_plot()
  • B. cramersv_plot()
  • C. pearson_plot()
  • D. corr()

Answer: A

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Visualize correlation between continuous and categorical features.
* Evaluate Options:
* A: Pearson-Continuous vs. continuous-incorrect.
* B: Cramer's V-Categorical vs. categorical-incorrect.
* C: Correlation ratio-Continuous vs. categorical-correct.
* D: General correlation-Not specific to mixed types.
* Reasoning: Correlation ratio handles mixed feature types for heatmaps.
* Conclusion: C is correct.
OCI documentation states: "correlation_ratio_plot() (C) in ADS SDK visualizes correlations between continuous and categorical features, ideal for mixed-type heatmaps." Pearson (A) and Cramer's (B) are type- specific, corr() (D) is broad-only C fits per ADS capabilities.
Oracle Cloud Infrastructure ADS SDK Documentation, "Correlation Visualization".


NEW QUESTION # 24
You are attempting to save a model from a notebook session to the model catalog by using the Accelerated Data Science (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 networking configuration allows access to Oracle Cloud Infrastructure services through a Service Gateway
  • B. The model artifact is saved to the block volume of the notebook session
  • C. A dynamic group has rules that match the notebook sessions in its compartment
  • D. The policy for a dynamic group grants manage permissions for the model catalog in this compartment
  • E. The policy for your user group grants manage permissions for the model catalog in this compartment

Answer: C,D

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Troubleshoot a 404 auth error when saving a model with resource principal.
* Understand Resource Principal: Allows notebook sessions to act as principals via dynamic groups and policies-no user credentials.
* Analyze 404 Error: Indicates permission failure-likely IAM misconfiguration.
* Evaluate Options:
* A: Block volume storage-Irrelevant to auth; it's about saving locally-incorrect.
* B: Dynamic group matching-Ensures notebook is recognized-correct.
* C: User group policy-Not used with resource principal-incorrect.
* D: Dynamic group policy-Grants catalog access-correct.
* E: Service Gateway-Network-related, not auth-specific-incorrect.
* Reasoning: Resource principal needs B (group inclusion) and D (policy perms)-404 points to these.
* Conclusion: B and D are correct.
OCI documentation states: "For ADS SDK to save to the Model Catalog using resource principal, ensure (1) a dynamic group includes notebook sessions with matching rules (e.g., resource.type
='datasciencenotebooksession') (B), and (2) a policy grants manage data-science-models to that dynamic group (D)." A is storage, C is user-based, E is network-only B and D fix the auth issue per OCI's IAM setup.
Oracle Cloud Infrastructure Data Science Documentation, "Resource Principal with Model Catalog".


NEW QUESTION # 25
Which OCI service enables you to build, train, and deploy machine learning models in the cloud?

  • A. Oracle Cloud Infrastructure Data Catalog
  • B. Oracle Cloud Infrastructure Data Integration
  • C. Oracle Cloud Infrastructure Data Flow
  • D. Oracle Cloud Infrastructure Data Science

Answer: D

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify the OCI service for ML model lifecycle.
* Evaluate Options:
* A: Data Catalog-Metadata management, not ML.
* B: Data Integration-ETL, not ML.
* C: Data Science-Full ML lifecycle-correct.
* D: Data Flow-Spark processing, not full ML.
* Reasoning: C supports building, training, deploying models.
* Conclusion: C is correct.
OCI documentation states: "OCI Data Science (C) provides tools to build, train, and deploy machine learning models in the cloud, including notebooks and model catalog." A, B, and D serve other purposes-only C fits the ML lifecycle per OCI's offerings.
Oracle Cloud Infrastructure Data Science Documentation, "Service Overview".


NEW QUESTION # 26
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