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| 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.
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| Topic 2 |
- 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.
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| 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.
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| 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.
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| 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 (Q12-Q17):
NEW QUESTION # 12
You are a data scientist working inside a notebook session and you attempt to pip install a package from a public repository that is not included in your conda environment. After running this command, you get a network timeout error. What might be missing from your networking configuration?
- A. FastConnect to an on-premises network
- B. Primary Virtual Network Interface Card (VNIC)
- C. Service Gateway with private subnet access
- D. NAT Gateway with public internet access
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Fix network timeout for pip install in a notebook.
* Evaluate Options:
* A: FastConnect-On-premises link, not public internet.
* B: VNIC-Default, not the issue.
* C: NAT Gateway-Grants internet access-correct.
* D: Service Gateway-OCI services, not PyPI.
* Reasoning: C enables outbound traffic to public repos.
* Conclusion: C is correct.
OCI documentation states: "A NAT Gateway (C) is required for notebook sessions in private subnets to access public internet repositories like PyPI." A, B, and D don't provide this-only C resolves the timeout.
Oracle Cloud Infrastructure Data Science Documentation, "Notebook Networking".
NEW QUESTION # 13
Which is NOT a compliance document?
- A. Bridge letter
- B. Penetration test report
- C. Attestation
- D. Certificate
Answer: B
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify a non-compliance document in OCI context.
* Understand Compliance Docs: Formal attestations of adherence (e.g., SOC, ISO).
* Evaluate Options:
* A: Certificate-Proof of compliance (e.g., ISO)-compliance doc.
* B: Pen test report-Security test result, not formal compliance-correct.
* C: Attestation-Statement of compliance-compliance doc.
* D: Bridge letter-Links audit periods-compliance doc.
* Reasoning: B is operational, not a compliance artifact.
* Conclusion: B is correct.
OCI documentation lists "compliance documents like certificates (A), attestations (C), and bridge letters (D) for standards like SOC or ISO; penetration test reports (B) are security assessments, not formal compliance docs." Only B stands apart per OCI's compliance terminology.
Oracle Cloud Infrastructure Compliance Documentation, "Compliance Artifacts".
NEW QUESTION # 14
Which of these protects customer data at rest and in transit in a way that allows customers to meet their security and compliance requirements for cryptographic algorithms and key management?
- A. Customer isolation
- B. Security controls
- C. Identity Federation
- D. Data encryption
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify protection for data at rest/transit with customer control.
* Evaluate Options:
* A: Controls-Broad, not specific to encryption.
* B: Isolation-Separates tenants, not crypto-focused.
* C: Encryption-Secures data, allows key management-correct.
* D: Federation-Auth sharing, not data protection.
* Reasoning: C provides crypto control (e.g., Vault keys).
* Conclusion: C is correct.
OCI documentation states: "Data encryption (C) protects data at rest and in transit, with customer-managed keys in OCI Vault meeting compliance needs." A and B are broader, D is unrelated-only C fits per OCI's security model.
Oracle Cloud Infrastructure Security Documentation, "Data Encryption".
NEW QUESTION # 15
You have a complex Python code project that could benefit from using Data Science Jobs as it is a repeatable machine learning model training task. The project contains many sub-folders and classes. What is the best way to run this project as a Job?
- A. ZIP the entire code project folder and upload it as a Job artifact on job creation. Jobs identifies the main executable file automatically
- B. Rewrite your code so that it is a single executable Python or Bash/Shell script file
- C. ZIP the entire code project folder and upload it as a Job artifact. Jobs automatically identifies the main top-level where the code is run
- D. ZIP the entire code project folder, upload it as a Job artifact on job creation, and set JOB_RUN_ENTRYPOINT to point to the main executable file
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Run a complex Python project as an OCI Job.
* Evaluate Options:
* A: Auto-identification-False; entrypoint must be set.
* B: Rewrite-Unnecessary, inefficient.
* C: Auto-executable-False; needs explicit entrypoint.
* D: ZIP with entrypoint-Correct, flexible approach.
* Reasoning: D preserves structure, specifies execution.
* Conclusion: D is correct.
OCI documentation states: "For complex projects, ZIP the folder and upload as a Job artifact, then set JOB_RUN_ENTRYPOINT (D) to the main executable (e.g., main.py)." Auto-detection (A, C) isn't supported, and B discards structure-D is best.
Oracle Cloud Infrastructure Data Science Documentation, "Job Artifacts".
NEW QUESTION # 16
You have a dataset with fewer than 1000 observations, and you are using Oracle AutoML to build a classifier.
While visualizing the results of each stage of the Oracle AutoML pipeline, you notice that no visualization has been generated for one of the stages. Which stage is not visualized?
- A. Feature selection
- B. Adaptive sampling
- C. Algorithm selection
- D. Hyperparameter tuning
Answer: B
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify the non-visualized AutoML stage with small data.
* Understand AutoML Pipeline: Includes sampling, feature/algorithm selection, tuning.
* Evaluate Options:
* A: Feature selection-Visualized (e.g., feature importance).
* B: Algorithm selection-Visualized (e.g., algorithm scores).
* C: Adaptive sampling-Skipped/visualization absent for <1000 rows.
* D: Hyperparameter tuning-Visualized (e.g., trial plots).
* Reasoning: Adaptive sampling optimizes large datasets; small data skips it, omitting visuals.
* Conclusion: C is correct.
OCI AutoML documentation notes: "Adaptive sampling is applied to large datasets (>1000 rows) to reduce size; for smaller datasets, it's skipped, and no visualization is generated." Other stages (A, B,D) produce visuals-only C is absent here.
Oracle Cloud Infrastructure AutoML Documentation, "Pipeline Stages".
NEW QUESTION # 17
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