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1z0-1110-25 Valid Test Objectives, Valid 1z0-1110-25 Cram Materials
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Oracle 1z0-1110-25 Exam Syllabus Topics:
Topic
Details
Topic 1
- 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 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
- 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 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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Pass Guaranteed Quiz 2025 1z0-1110-25: Unparalleled Oracle Cloud Infrastructure 2025 Data Science Professional Valid Test Objectives
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Oracle Cloud Infrastructure 2025 Data Science Professional Sample Questions (Q11-Q16):
NEW QUESTION # 11
Which statement about resource principals is true?
- A. When you authenticate using a resource principal, you need to create and manage credentials to access OCI resources.
- B. A resource principal is not a secure way to authenticate to resources, compared to the OCI configuration and API key approach.
- C. A resource principal is a feature of IAM that enables resources to be authorized principal actors.
- D. The Data Science service does not provide authentication via a notebook session's or job run's resource principal to access other OCI resources.
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
* Define Resource Principals: They allow OCI resources (e.g., notebook sessions) to authenticate to other OCI services without user credentials.
* Evaluate Options:
* A: False-Resource principals eliminate manual credential management.
* B: False-They're secure, leveraging IAM policies, not less secure than API keys.
* C: False-Data Science supports resource principals for accessing resources (e.g., Object Storage).
* D: True-Resource principals are an IAM feature authorizing resources as actors.
* Reasoning: D captures the essence of resource principals as an IAM mechanism.
* Conclusion: D is correct.
OCI documentation states: "A resource principal is an IAM feature that enables OCI resources, such as compute instances or notebook sessions, to act as principal actors and authenticate to other OCI services using policies." This refutes A (no credentials needed), B (secure method), and C (supported in Data Science), making D the accurate statement.
Oracle Cloud Infrastructure IAM Documentation, "Resource Principals".
NEW QUESTION # 12
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. OCI Vision
- B. OCI Language
- C. OCI Speech
- D. Oracle Digital Assistant
Answer: A
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 # 13
Which function's objective is to represent the difference between the predictive value and the target value?
- A. Update function
- B. Fit function
- C. Optimizer function
- D. Cost function
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify the function that measures the difference between predicted and actual values in machine learning.
* Understand ML Functions:
* Optimizer function: Adjusts model parameters to minimize error (e.g., gradient descent)-it uses the cost, not defines it.
* Fit function: Trains the model by fitting it to data-process-oriented, not a measure.
* Update function: Typically updates weights during training-not a standard term for error measurement.
* Cost function: Quantifies prediction error (e.g., MSE, cross-entropy)-directly represents the difference.
* Evaluate Options:
* A: Optimizer minimizes the cost, not the cost itself-incorrect.
* B: Fit executes training, not error definition-incorrect.
* C: Update is vague and not a standard ML term for this-incorrect.
* D: Cost function (e.g., loss) measures prediction vs. target-correct.
* Reasoning: The cost function (or loss function) is the mathematical representation of error, guiding optimization.
* Conclusion: D is the correct answer.
In OCI Data Science, the documentation explains: "The cost function (or loss function) measures the difference between the model's predicted values and the actual target values, such as mean squared error for regression or cross-entropy for classification." Optimizers (A) use this to adjust weights, fit (B) is a training step, and update (C) isn't a defined function here-only the cost function (D) fits the description. This aligns with standard ML terminology and OCI's AutoML processes.
Oracle Cloud Infrastructure Data Science Documentation, "Machine Learning Concepts - Cost Functions".
NEW QUESTION # 14
You have created a model and want to use Accelerated Data Science (ADS) SDK to deploy the model. Where are the artifacts to deploy this model with ADS?
- A. OCI Vault
- B. Model Depository
- C. Model Catalog
- D. Data Science Artifactory
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Locate artifacts for ADS model deployment.
* Understand ADS Deployment: Requires model artifacts (e.g., score.py) stored in OCI.
* Evaluate Options:
* A: Vault-Stores secrets, not models.
* B: Depository-Not an OCI term.
* C: Model Catalog-Stores models/artifacts for deployment-correct.
* D: Artifactory-Not an OCI service.
* Reasoning: Model Catalog is OCI's model repository for ADS.
* Conclusion: C is correct.
OCI documentation states: "ADS SDK deploys models from the Model Catalog, where trainedmodels and artifacts (e.g., score.py) are stored." Vault (A) is for secrets, B and D aren't real-only C supports ADS deployment.
Oracle Cloud Infrastructure Data Science Documentation, "ADS Model Deployment".
NEW QUESTION # 15
How can you collaborate with team members in OCI Data Science Workspace?
- A. By sharing the workspace instance with other users
- B. By using version control systems integrated with the workspace
- C. By granting access to specific notebooks and files
- D. By enabling chat and video conferencing within the workspace
Answer: B
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 # 16
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