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Oracle 1z0-1110-25 Exam Syllabus Topics:

Topic	Details
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	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	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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Oracle Cloud Infrastructure 2025 Data Science Professional Sample Questions (Q150-Q155):

NEW QUESTION # 150
You want to create a user group for a team of external data science consultants. The consultants should only have the ability to see Data Science resource details but not have the ability to create, delete, or update Data Science resources. What verb should you write in the policy?

A. Manage
B. Use
C. Inspect
D. Read

Answer: C

Explanation:
Detailed Answer in Step-by-Step Solution:
* Understand OCI IAM Policies: Policies control access using verbs like "inspect," "read," "use," and
"manage."
* Define Requirement: Consultants need view-only access without modification rights.
* Evaluate Verbs:
* A. Use: Allows interaction (e.g., running jobs)-too permissive.
* B. Inspect: Grants view-only access to resource details-matches requirement.
* C. Manage: Full control (create, update, delete)-too permissive.
* D. Read: Includes viewing content (e.g., data), not just metadata-slightly broader than needed.
* Conclusion: "Inspect" (B) is the precise verb for view-only access to resource details.
In OCI Identity and Access Management (IAM), the "inspect" verb allows listing and viewing resource metadata without granting modification or data access rights, ideal for this scenario. This is confirmed in the IAM policy reference. (Reference: Oracle Cloud Infrastructure Documentation, "IAM Policy Verbs").

NEW QUESTION # 151
You are a data scientist using Oracle AutoML to produce a model and you are evaluating the score metric for the model. Which TWO of the following prevailing metrics would you use for evaluating a multiclass classification model?

A. Explained variance score
B. R-squared
C. Recall
D. Mean squared error
E. F1-score

Answer: C,E

Explanation:
Detailed Answer in Step-by-Step Solution:
* Understand Multiclass Classification: Metrics evaluate how well the model predicts multiple classes.
* Evaluate Metrics:
* A. Mean squared error: Used for regression, not classification.
* B. Explained variance score: Regression metric, not suitable.
* C. Recall: Measures true positive rate per class-key for classification.
* D. F1-score: Balances precision and recall-widely used in multiclass.
* E. R-squared: Regression metric, not applicable.
* Select Two: Recall (C) and F1-score (D) are standard for multiclass classification.
Oracle AutoML supports metrics like recall and F1-score for multiclass classification, as they assess per-class performance and overall precision-recall balance, respectively. Regression metrics (A, B,E) are irrelevant here. (Reference: Oracle Cloud Infrastructure Data Science Documentation, "AutoML Metrics").

NEW QUESTION # 152
Which TWO statements about Oracle Cloud Infrastructure (OCI) Open Data service are true?

A. Each dataset in Open Data consists of code and tooling usage examples for consumption and reproducibility.
B. Open Data is a dataset repository made for the people that create, use, and manipulate datasets.
C. Subscribers can pay and log into Open Data to view curated datasets that are otherwise not available to the public.
D. A primary goal of Open Data is for users to contribute to the data repositories to expand the content offered.
E. Open Data includes text and image data repositories for AI and ML.
F. Audio and video formats are not available.

Answer: B,E

Explanation:
Detailed Answer in Step-by-Step Solution:
* Analyze OCI Open Data: OCI Open Data is a free service providing access to public datasets for AI
/ML use cases.
* Evaluate Statements:
* A: True-Open Data includes text and image datasets (e.g., geospatial images).
* B: False-Video and other formats may be available depending on the dataset; no strict exclusion exists.
* C: False-Datasets may include metadata, but code/tooling examples aren't guaranteed.
* D: True-It's designed for data scientists and analysts who work with datasets.
* E: False-It's not a user-contributed repository; it's curated by Oracle.
* F: False-Open Data is free and public, not subscription-based.
* Select Two: A and D align with the service's purpose and offerings.
OCI Open Data provides access to datasets like text and images (A) for AI/ML, aimed at data professionals (D). It's a free, curated service, not user-contributed (E) or paid (F), and while it focuses on certain formats, it doesn't explicitly exclude audio/video (B). (Reference: Oracle Cloud Infrastructure Open Data Documentation, "Overview of Open Data").

NEW QUESTION # 153
Which OCI service provides a managed Kubernetes service for deploying, scaling, and managing containerized applications?

A. Oracle Cloud Infrastructure Streaming
B. Oracle Cloud Infrastructure Container Registry
C. Oracle Cloud Infrastructure Load Balancing
D. Oracle Cloud Infrastructure Container Engine for Kubernetes

Answer: D

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify OCI's managed Kubernetes service.
* Evaluate Options:
* A: Container Registry-Stores images, not Kubernetes.
* B: Load Balancing-Network service, not Kubernetes.
* C: Container Engine (OKE)-Managed Kubernetes-correct.
* D: Streaming-Data streaming, not containers.
* Reasoning: C is OCI's Kubernetes offering-OKE.
* Conclusion: C is correct.
OCI documentation states: "OCI Container Engine for Kubernetes (OKE) (C) provides a managed service to deploy, scale, and manage containerized applications using Kubernetes." A, B, and D serve other purposes- only C matches per OCI's container services.
Oracle Cloud Infrastructure OKE Documentation, "Overview".

NEW QUESTION # 154
Which function's objective is to represent the difference between the predictive value and the target value?

A. Cost function
B. Optimizer function
C. Update function
D. Fit function

Answer: A

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 # 155
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1Z0-1110-25 EXAM VCE FREE, NEW 1Z0-1110-25 TEST TUTORIAL

1z0-1110-25 Exam Vce Free, New 1z0-1110-25 Test Tutorial