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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 |
- 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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| 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.
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| Topic 4 |
- 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 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 (Q30-Q35):
NEW QUESTION # 30
You are a researcher who requires access to large datasets. Which OCI service would you use?
- A. Oracle Open Data
- B. OCI Data Science
- C. ADW (Autonomous Data Warehouse)
- D. Oracle Databases
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Find the OCI service for accessing large public datasets.
* Evaluate Options:
* A: Oracle Databases-General-purpose, not dataset-focused.
* B: ADW-Analytics warehouse, not a dataset repository.
* C: OCI Data Science-ML platform, not a dataset provider.
* D: Oracle Open Data-Free, public datasets (e.g., geospatial).
* Reasoning: Open Data provides pre-existing large datasets for research.
* Conclusion: D is correct.
OCI documentation states: "Oracle Open Data provides free access to large, curated datasets, such as geospatial data, ideal for researchers." Databases (A) and ADW (B) are for storage/analytics, Data Science (C) is for ML-not datasets-only Open Data (D) fits.
Oracle Cloud Infrastructure Open Data Documentation, "Overview".
NEW QUESTION # 31
You are a data scientist working for a utilities company. You have developed an algorithm that detects anomalies from a utility reader in the grid. The size of the model artifact is about 2 GB, and you are trying to store it in the model catalog. Which THREE interfaces could you use to save the model artifact into the model catalog?
- A. Console
- B. Git CLI
- C. Oracle Cloud Infrastructure (OCI) Command Line Interface (CLI)
- D. Accelerated Data Science (ADS) Software Development Kit (SDK)
- E. ODSC CLI
- F. OCI Python SDK
Answer: A,D,F
NEW QUESTION # 32
How can you collaborate with team members in OCI Data Science Workspace?
- A. By sharing the workspace instance with other users
- B. By granting access to specific notebooks and files
- C. By enabling chat and video conferencing within the workspace
- D. By using version control systems integrated with the workspace
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Determine collaboration method in OCI Data Science (Notebook Sessions).
* Evaluate Options:
* A: Access control-Possible but not primary collaboration.
* B: Version control (e.g., Git)-Standard for code sharing-correct.
* C: Shared instance-Not supported; sessions are single-user.
* D: Chat/video-Not a feature of OCI Data Science.
* Reasoning: B leverages Git for team collaboration-OCI's recommended method.
* Conclusion: B is correct.
OCI documentation states: "Collaborate in Data Science by integrating version control systems like Git (B) with notebook sessions to share code and notebooks." A is limited, C isn't feasible, and D isn't available- only B matches OCI's collaboration approach.
Oracle Cloud Infrastructure Data Science Documentation, "Collaboration with Git".
NEW QUESTION # 33
You have just received a new dataset from a colleague. You want to quickly find out summary information about the dataset, such as the types of features, the total number of observations, and distributions of the data.
Which Accelerated Data Science (ADS) SDK method from the ADSDataset class would you use?
- A. show_in_notebook()
- B. show_corr()
- C. to_xgb()
- D. compute()
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Get summary info from an ADSDataset object.
* Evaluate Options:
* A: Correlation matrix-Specific, not full summary.
* B: Converts to XGBoost-Not for summary.
* C: Executes computation-Not summary-focused.
* D: Displays summary (types, counts, dist)-correct.
* Reasoning: show_in_notebook() provides a comprehensive overview.
* Conclusion: D is correct.
OCI documentation states: "show_in_notebook() (D) from ADSDataset displays a summary of the dataset, including feature types, observation count, and distributions, in a notebook." A is partial, B and C are unrelated-only D meets the need per ADS SDK.
Oracle Cloud Infrastructure ADS SDK Documentation, "ADSDataset Methods".
NEW QUESTION # 34
You are a data scientist designing an air traffic control model, and you choose to leverage Oracle AutoML.
You understand that the Oracle AutoML pipeline consists of multiple stages and automatically operates in a certain sequence. What is the correct sequence for the Oracle AutoML pipeline?
- A. Algorithm selection, Adaptive sampling, Feature selection, Hyperparameter tuning
- B. Algorithm selection, Feature selection, Adaptive sampling, Hyperparameter tuning
- C. Adaptive sampling, Feature selection, Algorithm selection, Hyperparameter tuning
- D. Adaptive sampling, Algorithm selection, Feature selection, Hyperparameter tuning
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Sequence OCI AutoML pipeline stages.
* Stages:
* Adaptive sampling: Reduces data size if large.
* Feature selection: Picks relevant features.
* Algorithm selection: Chooses best model type.
* Hyperparameter tuning: Optimizes model params.
* Evaluate: C (sampling, features, algorithms, tuning) matches logical flow-data first, then model.
* Reasoning: Sampling precedes feature work-standard in OCI.
* Conclusion: C is correct.
OCI documentation states: "AutoML pipeline runs 1) adaptive sampling, 2) feature selection, 3) algorithm selection, 4) hyperparameter tuning (C)." Sampling reduces data first, then features and models are optimized-other orders (A, B, D) misalign with OCI's sequence.
Oracle Cloud Infrastructure AutoML Documentation, "Pipeline Sequence".
NEW QUESTION # 35
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