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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 |
- 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 3 |
- 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 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 |
- 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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Oracle Cloud Infrastructure 2025 Data Science Professional Sample Questions (Q17-Q22):
NEW QUESTION # 17
During a job run, you receive an error message that no space is left on your disk device. To solve the problem, you must increase the size of the job storage. What would be the most efficient way to do this with Data Science Jobs?
- A. Create a new job with increased storage size and then run the job
- B. Your code using too much disk space. Refactor the code to identify the problem
- C. Edit the job, change the size of the storage of your job, and start a new job run
- D. On the job run, set the environment variable that helps increase the size of the storage
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Efficiently increase storage for an OCI Job.
* Understand Jobs: Storage (block volume) is set at job creation, not dynamically adjustable.
* Evaluate Options:
* A: False-Jobs can't edit storage post-creation; it's fixed.
* B: False-No environment variable adjusts storage size.
* C: True-Create a new job with larger storage (e.g., 200 GB) and run it.
* D: False-Refactoring code is inefficient compared to increasing storage.
* Reasoning: C is the standard OCI process for adjusting resources.
* Conclusion: C is correct.
OCI documentation states: "Storage size for a Data Science Job is specified during job creation (e.g., block volume size). To increase it, create a new job with a larger storage configuration and initiate a new run." Editing (A) isn't supported, variables (B) don't apply, and refactoring (D) avoids the issue-only C is efficient.
Oracle Cloud Infrastructure Data Science Documentation, "Jobs - Storage Configuration".
NEW QUESTION # 18
You have been given a collection of digital files required for a business audit. They consist of several different formats that you would like to annotate using Oracle Cloud Infrastructure (OCI) Data Labeling.
Which THREE types of files could this tool annotate?
- A. Video footage of a conversation in a conference room
- B. A typewritten document that details an annual budget
- C. Images of computer server racks
- D. A collection of purchase orders for office supplies
- E. An audio recording of a phone conversation
Answer: A,B,C
Explanation:
Detailed Answer in Step-by-Step Solution:
* Understand OCI Data Labeling Capabilities: OCI Data Labeling is designed to annotate data for machine learning, supporting specific file types like images, text documents, and videos.
* Evaluate Options:
* A. Video footage: Supported for tasks like object detection or action recognition.
* B. Images: Supported for image classification, object detection, etc.
* C. Typewritten document: Supported as text data for tasks like entity extraction or classification.
* D. Purchase orders: While potentially text-based, this is ambiguous without format clarification (e.g., PDF, image). OCI supports text annotation, but "purchase orders" isn't a specific file type- it's assumed as text here.
* E. Audio recording: Not supported, as OCI Data Labeling focuses on visual and textual data, not audio.
* Select Three: A (video), B (images), and C (text documents) are explicitly supported file types.
OCI Data Labeling supports annotating datasets of images, text, and videos, as per the official documentation.
Video footage (A) can be annotated for tasks like object tracking, images (B) for classification or detection, and typewritten documents (C) for text-based annotations (e.g., named entity recognition). Audio files (E) are not supported, and while purchase orders (D) could be text, the question specifies "typewritten document" as a clearer match. (Reference: Oracle Cloud Infrastructure Data Labeling Service Documentation, "Supported Data Types").
NEW QUESTION # 19
You want to evaluate the relationship between feature values and target variables. You have a large number of observations having a near uniform distribution and the features are highly correlated. Which model explanation technique should you choose?
- A. Accumulated Local Effects
- B. Local Interpretable Model-Agnostic Explanations
- C. Feature Permutation Importance Explanations
- D. Feature Dependence Explanations
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Select an explanation technique for feature-target relationships with correlated features.
* Evaluate Options:
* A: Permutation-Breaks with high correlation.
* B: LIME-Local, not global relationships.
* C: Dependence-Not a standard term; vague.
* D: ALE-Handles correlation, shows feature effects-correct.
* Reasoning: ALE is robust to correlated features, ideal here.
* Conclusion: D is correct.
OCI documentation states: "Accumulated Local Effects (ALE) (D) evaluates feature-target relationships, accounting for correlations, unlike permutation importance (A) which falters with high correlation." B is local, C isn't defined-only D fits per OCI's explanation tools.
Oracle Cloud Infrastructure Data Science Documentation, "Model Explanation Techniques".
NEW QUESTION # 20
You are a data scientist using Oracle AutoML to produce a model and you are evaluating the score metric for the model. Which of the following TWO prevailing metrics would you use for evaluating a multiclass classification model?
- A. Mean squared error
- B. Explained variance score
- C. Recall
- D. R-Squared
- E. F1 Score
Answer: C,E
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Select two metrics for multiclass classification in AutoML.
* Understand Multiclass Metrics: Focus on class-specific performance-classification, not regression.
* Evaluate Options:
* A. Recall: Measures true positives per class-key for multiclass-correct.
* B. Mean squared error: Regression metric-incorrect.
* C. F1 Score: Balances precision and recall-standard for multiclass-correct.
* D. R-Squared: Regression fit-incorrect.
* E. Explained variance: Regression metric-incorrect.
* Reasoning: A and C assess classification accuracy across multiple classes-fit AutoML's evaluation.
* Conclusion: A and C are correct.
OCI AutoML documentation states: "For multiclass classification, common evaluation metrics include recall (A) for per-class sensitivity and F1 Score (C) for balanced performance." B, D, and E are regression- focused-only A and C are supported and relevant per OCI's AutoML metrics suite.
Oracle Cloud Infrastructure AutoML Documentation, "Evaluation Metrics for Classification".
NEW QUESTION # 21
True or false? Data scientists typically need a combination of technical skills, nontechnical ones, and suitable personality traits to be successful.
Answer: B
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Assess required skills for data scientists.
* Analyze Skills:
* Technical: Coding, stats, ML.
* Nontechnical: Communication, business acumen.
* Traits: Curiosity, problem-solving.
* Reasoning: Success requires this mix-e.g., explaining models to stakeholders.
* Conclusion: A (True) is correct.
OCI documentation states: "Effective data scientists combine technical skills (e.g., Python), nontechnical skills (e.g., storytelling), and traits like analytical thinking." This holistic requirement is true (A), not false (B).
Oracle Cloud Infrastructure Data Science Documentation, "Data Scientist Skills".
NEW QUESTION # 22
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