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Oracle 1z0-1110-25 Exam Syllabus Topics:

Topic	Details
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	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 3	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 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	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.

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Oracle Cloud Infrastructure 2025 Data Science Professional Sample Questions (Q127-Q132):

NEW QUESTION # 127
You have just started as a data scientist at a healthcare company. You have been asked to analyze and improve a deep neural network model, which was built based on the electrocardiogram records of patients.
There are no details about the model framework that was built. What would be the best way to find more details about the machine learning models inside the model catalog?

A. Refer to the code inside the model
B. Check for metadata tags
C. Check for model taxonomy details
D. Check for provenance details

Answer: D

Explanation:
Detailed Answer in Step-by-Step Solution:
* Context Analysis: You need to investigate an existing deep neural network model in the OCI Model Catalog with no prior information.
* Understand Model Catalog: The Model Catalog stores trained models along with metadata, hyperparameters, and provenance (origin and history) details.
* Evaluate Options:
* A. Refer to the code inside the model: The model artifact (e.g., a serialized file like .pkl) doesn't typically include readable source code; it's a trained object, not the training script.
* B. Check for model taxonomy details: Taxonomy (e.g., classification vs. regression) provides high-level categorization but lacks specifics like framework or architecture.
* C. Check for metadata tags: Metadata includes name, description, and tags, offering some context but not detailed framework info (e.g., TensorFlow vs. PyTorch).
* D. Check for provenance details: Provenance tracks the model's creation process, including the framework, training environment, and data sources, providing the most comprehensive insight.
* Reasoning: Provenance details are designed to document the "how" and "what" of model creation, making them ideal for uncovering the framework (e.g., Keras, PyTorch) and other specifics absent from initial handover.
* Conclusion: D is the best approach for detailed investigation.
In OCI Data Science, the Model Catalog stores provenance information, which includes "details about the model's origin, such as the framework used (e.g., TensorFlow, PyTorch), the training environment, and dataset references." This is more informative than metadata tags (C), which are user-defined and less structured, or taxonomy (B), which is broad. The model artifact (A) is a binary file (e.g., pickle), not a readable codebase. Provenance (D) offers a detailed audit trail, critical for analyzing an undocumented deep neural network model like this one.
Oracle Cloud Infrastructure Data Science Documentation, "Model Catalog - Provenance Details" section.

NEW QUESTION # 128
You have just completed analyzing a set of images by using Oracle Cloud Infrastructure (OCI) Data Labeling, and you want to export the annotated data. Which TWO formats are supported?

A. Data Labeling Service Proprietary JSON
B. COCO
C. Spacy
D. CONLL V2003

Answer: A,B

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify export formats for OCI Data Labeling.
* Understand Export: Annotated data is exported for ML use.
* Evaluate Options:
* A: CONLL V2003-Text-specific, not OCI-supported.
* B: COCO-Standard for image annotations-correct.
* C: Proprietary JSON-OCI's default format-correct.
* D: Spacy-NLP format, not supported.
* Reasoning: B and C are image-compatible per OCI docs.
* Conclusion: B and C are correct.
OCI documentation states: "Data Labeling exports annotations in COCO format (B) for image tasks and a proprietary JSON format (C) specific to the service." CONLL (A) and Spacy (D) are text/NLP-focused-not supported for OCI's image annotations.
Oracle Cloud Infrastructure Data Labeling Documentation, "Export Formats".

NEW QUESTION # 129
Which statement about resource principals is true?

A. A resource principal is a feature of IAM that enables resources to be authorized principal actors.
B. The Data Science service does not provide authentication via a notebook session's or job run's resource principal to access other OCI resources.
C. A resource principal is not a secure way to authenticate to resources, compared to the OCI configuration and API key approach.
D. When you authenticate using a resource principal, you need to create and manage credentials to access OCI resources.

Answer: A

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 # 130
Which step is unique to MLOps, as opposed to DevOps?

A. Continuous delivery
B. Continuous integration
C. Continuous training
D. Continuous deployment

Answer: C

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify a step unique to MLOps vs. DevOps.
* Compare MLOps and DevOps:
* DevOps: Focuses on software deployment (CI/CD).
* MLOps: Extends DevOps to ML, adding model-specific steps.
* Evaluate Options:
* A: Continuous deployment-Common to both (software/models).
* B: Continuous integration-Common to both (code merging).
* C: Continuous delivery-Common to both (releasing updates).
* D: Continuous training-Unique to MLOps (retraining models with new data).
* Reasoning: Only D addresses ML-specific needs (model retraining).
* Conclusion: D is correct.
OCI documentation notes: "MLOps extends DevOps with continuous training, a process unique to machine learning where models are retrained with new data to maintain performance." CI (B), CD (A), and delivery (C) are shared with DevOps-only continuous training (D) is MLOps-specific.
Oracle Cloud Infrastructure Data Science Documentation, "MLOps Concepts".

NEW QUESTION # 131
You want to make API calls against other OCI services from your instance without configuring user credentials. How would you achieve this?

A. Create a dynamic group and add your instance
B. No configuration is required for making API calls
C. Create a dynamic group and add a policy
D. Create a group and add a policy

Answer: C

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Enable credential-less API calls from an instance.
* Understand Resource Principal: Allows instances to authenticate via IAM without user creds.
* Evaluate Options:
* A: Dynamic group + policy-Correct; groups instance, grants access.
* B: Dynamic group only-Incomplete; needs policy.
* C: User group-Irrelevant for instances.
* D: No config-False; setup required.
* Reasoning: A sets up resource principal fully-group and perms.
* Conclusion: A is correct.
OCI documentation states: "To make API calls without credentials, create a dynamic group including the instance and add a policy (A) granting access to OCI services-enables resource principal." B lacks policy, C is user-based, D is false-only A completes the process per OCI's IAM setup.
Oracle Cloud Infrastructure IAM Documentation, "Resource Principal Configuration".

NEW QUESTION # 132
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