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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	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	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	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	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.

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Oracle Cloud Infrastructure 2025 Data Science Professional Sample Questions (Q97-Q102):

NEW QUESTION # 97
On which option do you set Oracle Cloud Infrastructure Budget?

A. Compartments
B. Instances
C. Free-form tags
D. Tenancy

Answer: D

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Determine where OCI budgets are set.
* Understand Budgets: Track spending across OCI resources.
* Evaluate Options:
* A: Compartments-Scoped within tenancy, not budget root.
* B: Instances-Specific resources, not budget scope.
* C: Tags-Filter costs, not budget setting.
* D: Tenancy-Top-level scope for budgets-correct.
* Reasoning: Budgets apply at tenancy, optionally filtered (e.g., by compartment).
* Conclusion: D is correct.
OCI documentation states: "Budgets are set at the tenancy level (D), with optional filters like compartments or tags to monitor spending." A, B, and C are sub-elements-only D is the primary scope per OCI's cost management.
Oracle Cloud Infrastructure Cost Management Documentation, "Setting Budgets".

NEW QUESTION # 98
You want to build a multistep machine learning workflow by using the Oracle Cloud Infrastructure (OCI) Data Science Pipeline feature. How would you configure the conda environment to run a pipeline step?

A. Configure a block volume
B. Use environmental variables
C. Use command-line variables
D. Configure a compute shape

Answer: B

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Configure conda env for a pipeline step.
* Evaluate Options:
* A: Shape-Infra, not env config.
* B: Volume-Storage, not env.
* C: Command-line-Step args, not env.
* D: Env variables-Sets conda path-correct.
* Reasoning: D specifies runtime env (e.g., CONDA_ENV_SLUG).
* Conclusion: D is correct.
OCI documentation states: "Configure a pipeline step's conda environment using environment variables (D), such as CONDA_ENV_SLUG, in the step definition." A, B, and C address other aspects-only D fits env config.
Oracle Cloud Infrastructure Data Science Documentation, "Pipeline Step Configuration".

NEW QUESTION # 99
Select two reasons why it is important to rotate encryption keys when using Oracle Cloud Infrastructure (OCI) Vault to store credentials or other secrets.

A. Periodically rotating keys limits the amount of data encrypted by one key version
B. Key rotation improves encryption efficiency
C. Periodically rotating keys makes it easier to reuse keys
D. Key rotation reduces risk if a key is ever compromised
E. Key rotation allows you to encrypt no more than five keys at a time

Answer: A,D

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify two reasons for key rotation in OCI Vault.
* Understand Key Rotation: Enhances security by updating keys.
* Evaluate Options:
* A: Five-key limit-False, no such restriction.
* B: Efficiency-False, not the purpose.
* C: Reuse-False, rotation prevents reuse.
* D: Reduces risk-True, limits exposure-correct.
* E: Limits data-True, reduces breach scope-correct.
* Reasoning: D and E are security-focused-key Vault benefits.
* Conclusion: D and E are correct.
OCI documentation states: "Key rotation in Vault (D) reduces risk if a key is compromised and (E) limits the data encrypted by a single key version, enhancing security." A, B, and C misrepresent rotation's purpose- only D and E align with OCI's Vault best practices.
Oracle Cloud Infrastructure Vault Documentation, "Key Rotation Benefits".

NEW QUESTION # 100
You are creating an Oracle Cloud Infrastructure (OCI) Data Science job that will run on a recurring basis in a production environment. This job will pick up sensitive data from an Object Storage Bucket, train a model, and save it to the model catalog. How would you design the authentication mechanism for the job?

A. Store your personal OCI config file and keys in the Vault, and access the Vault through the job run resource principal
B. Create a pre-authenticated request (PAR) for the Object Storage bucket and use that in the job code
C. Package your personal OCI config file and keys in the job artifact
D. Use the resource principal of the job run as the signer in the job code, ensuring there is a dynamic group for this job run with appropriate access to Object Storage and the model catalog

Answer: D

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Securely authenticate a recurring OCI Job.
* Evaluate Options:
* A: PAR-Limited duration, insecure for recurring jobs.
* B: Resource principal-Secure, managed auth for Jobs-correct.
* C: Personal config-Unscalable, security risk.
* D: Vault with personal keys-Complex, still uses user creds.
* Reasoning: B uses OCI's native, secure resource principal mechanism.
* Conclusion: B is correct.
OCI documentation states: "For Jobs accessing sensitive data, use resource principals with a dynamic group (e.
g., resource.type = 'datasciencejobrun') and policies granting access to Object Storage and Model Catalog- secure and scalable." A is temporary, C and D risk credential exposure-B is best practice.
Oracle Cloud Infrastructure Data Science Documentation, "Job Authentication".

NEW QUESTION # 101
You are a data scientist trying to load data into your notebook session. You understand that Accelerated Data Science (ADS) SDK supports loading various data formats. Which of the following THREE are ADS- supported data formats?

A. XML
B. Pandas DataFrame
C. JSON
D. DOCX
E. Raw Images

Answer: A,B,C

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify three data formats supported by ADS SDK for loading data.
* Understand ADS SDK: Facilitates data loading into notebook sessions via DatasetFactory.
* Evaluate Options:
* A. DOCX: Not natively supported-requires conversion (e.g., to text).
* B. Pandas DataFrame: Supported-core format for data manipulation in ADS.
* C. JSON: Supported-common structured data format.
* D. Raw Images: Not directly supported-image data needs preprocessing (e.g., via Vision).
* E. XML: Supported-parseable structured format.
* Reasoning: ADS focuses on tabular/structured data-B, C, E align; A and D require external handling.
* Conclusion: B, C, E are correct.
OCI documentation states: "ADS SDK's DatasetFactory supports loading data from formats like Pandas DataFrames (B), JSON (C), and XML (E), enabling easy integration into notebook sessions." DOCX (A) isn't natively handled, and raw images (D) require preprocessing outside ADS-B, C, E match the supported list.
Oracle Cloud Infrastructure ADS SDK Documentation, "Supported Data Formats".

NEW QUESTION # 102
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