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Oracle 1z0-1110-25 Exam Syllabus Topics:

Topic	Details
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	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 3	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 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	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.

Oracle Cloud Infrastructure 2025 Data Science Professional Sample Questions (Q76-Q81):

NEW QUESTION # 76
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 policy for the dynamic group grants manage permissions for the model catalog in this compartment
B. The policy for your user group grants manage permissions for the model catalog in this compartment
C. The model artifact is saved to the block volume of the notebook session
D. The networking configuration allows access to the Oracle Cloud Infrastructure services through a service gateway
E. The dynamic groups matching rule exists for notebook sessions in the compartment

Answer: A,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 # 77
The feature type TechJob has the following registered validators:
* TechJob.validator.register(name='is_tech_job', handler=is_tech_job_default_handler)
* TechJob.validator.register(name='is_tech_job', handler=is_tech_job_open_handler, condition= ('job_family',))
* TechJob.validator.register(name='is_tech_job', handler=is_tech_job_closed_handler, condition= ('job_family': 'IT'))When you run is_tech_job(job_family='Engineering'), what does the feature type validator system do?

A. Execute the is_tech_job_closed_handler handler
B. Execute the is_tech_job_open_handler handler
C. Throw an error because the system cannot determine which handler to run
D. Execute the is_tech_job_default_handler handler

Answer: B

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Determine which validator handler runs for is_tech_job(job_family='Engineering').
* Understand Validator System: Likely ADS SDK-executes handlers based on conditions.
* Analyze Validators:
* Default: is_tech_job_default_handler-No condition, fallback.
* Open: is_tech_job_open_handler-Condition ('job_family',)-requires job_family arg.
* Closed: is_tech_job_closed_handler-Condition ('job_family': 'IT')-requires job_family='IT'.
* Evaluate Call: job_family='Engineering'-Matches job_family presence, not IT.
* Reasoning:
* Open handler applies (tuple condition means arg exists).
* Closed fails (Engineering # IT).
* Default is overridden by specific matches.
* Conclusion: D is correct.
OCI ADS documentation states: "Validators execute the most specific handler matching the condition; for is_tech_job(job_family='Engineering'), is_tech_job_open_handler (D) runs as it matches job_family presence, while is_tech_job_closed_handler (C) requires IT-default (A) is bypassed, no error (B)." Only D fits per ADS validator logic.
Oracle Cloud Infrastructure ADS SDK Documentation, "Feature Type Validators".

NEW QUESTION # 78
How are datasets exported in the OCI Data Labeling service?

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

Answer: B

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 # 79
Triggering a PagerDuty notification as part of Monitoring is an example of what in the OCI Console?

A. Event
B. Rule
C. Function
D. Action

Answer: D

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Classify a PagerDuty notification in OCI Monitoring.
* Understand OCI Monitoring: Involves events, rules, and actions for notifications.
* Evaluate Options:
* A: Action-executes a response (e.g., notify PagerDuty) when triggered-correct.
* B: Rule-defines conditions for triggering actions-precedes the action.
* C: Function-serverless code, not directly tied to notifications.
* D: Event-state change triggering a rule, not the notification itself.
* Reasoning: The notification is the action taken after an event/rule-fits A.
* Conclusion: A is correct.
OCI documentation states: "Actions in the Monitoring service execute responses, such as sending notifications to PagerDuty, when a rule's condition is met based on an event." Rules (B) set conditions, Functions (C) are unrelated, and Events (D) are triggers-only Action (A) describes the notification step.
Oracle Cloud Infrastructure Monitoring Documentation, "Actions Overview".

NEW QUESTION # 80
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 and local behaviours of machine learning models are similar
B. Local explanation techniques are model-agnostic, while global explanation techniques are not
C. Global behaviour of a machine learning model may be complex, while the local behaviour may be approximated with a simpler surrogate model
D. Model-agnostic techniques are more interpretable than techniques that are dependent on the types of models

Answer: C

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 # 81
......

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