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| Topic |
Details |
| Topic 1 |
- 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 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 |
- 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 |
- 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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Oracle Cloud Infrastructure 2025 Data Science Professional Sample Questions (Q135-Q140):
NEW QUESTION # 135
How can you convert a fixed load balancer to a flexible load balancer?
- A. There is no way to convert the load balancer
- B. Use Update Shape workflows
- C. Delete the fixed load balancer and create a new one
- D. Using the Edit Listener option
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Convert fixed to flexible load balancer in OCI.
* Understand Load Balancers: Fixed (e.g., 10 Mbps) vs. flexible (dynamic shapes).
* Evaluate Options:
* A: False-Conversion possible via recreation.
* B: Update Shape-For flexible only, not conversion.
* C: Delete and recreate-Standard method-correct.
* D: Edit Listener-Configures rules, not type.
* Reasoning: OCI requires new creation for type change.
* Conclusion: C is correct.
OCI documentation states: "To change from a fixed to a flexible load balancer, delete the existing fixed load balancer and create a new flexible one (C)-direct conversion isn't supported." A is too absolute, B and D don't apply-only C matches OCI's process.
Oracle Cloud Infrastructure Load Balancing Documentation, "Changing Load Balancer Type".
NEW QUESTION # 136
Which Web Application Firewall (WAF) service component must be configured to allow, block, or log network requests when they meet specified criteria?
- A. Bot Management
- B. Web Application Firewall policy
- C. Origin
- D. Protection rules
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify the WAF component that controls request actions based on criteria.
* Understand WAF Components:
* Protection Rules: Define conditions and actions (e.g., allow, block, log).
* Bot Management: Handles bot traffic, not general request rules.
* Origin: Backend server endpoint, not rule-based.
* WAF Policy: Umbrella config, but rules specify actions.
* Evaluate Options:
* A: Protection rules-Set specific criteria and actions-correct.
* B: Bot Management-Bot-specific, not general requests.
* C: Origin-Defines source, not actions.
* D: WAF policy-Broad config, not the granular rules.
* Reasoning: Protection rules directly manage request behavior-fit the requirement.
* Conclusion: A is correct.
OCI documentation states: "Protection rules (A) in WAF define conditions (e.g., IP, URL) and actions (allow, block, log) for incoming requests." Bot Management (B) targets bots, Origin (C) is a target server, and WAF Policy (D) encompasses rules but isn't the action specifier-only A aligns with OCI's WAF configuration.
Oracle Cloud Infrastructure WAF Documentation, "Protection Rules".
NEW QUESTION # 137
You are a computer vision engineer building an image recognition model. You decide to use Oracle Data Labeling to annotate your image data. Which of the following THREE are possible ways to annotate an image in Data Labeling?
- A. Adding labels to an image by drawing a bounding box to an image is not supported by Data Labeling
- B. Adding a single label to an image
- C. Adding multiple labels to an image
- D. Adding labels to an image using semantic segmentation, by drawing multiple bounding boxes to an image
- E. Adding labels to an image using object detection, by drawing bounding boxes to an image
Answer: B,C,E
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify three annotation methods in OCI Data Labeling for images.
* Understand Data Labeling: Supports image annotations for ML.
* Evaluate Options:
* A: Semantic segmentation with boxes-Incorrect; segmentation is pixel-based, not boxes.
* B: Single label (classification)-Supported-correct.
* C: No bounding boxes-False; boxes are supported.
* D: Object detection with boxes-Supported-correct.
* E: Multiple labels (multi-label)-Supported-correct.
* Reasoning: B (classification), D (detection), E (multi-label) match OCI capabilities.
* Conclusion: B, D, E are correct.
OCI documentation states: "Data Labeling supports image annotations via single-label classification (B), object detection with bounding boxes (D), and multi-label classification (E)." A misdefines segmentation, C contradicts support-only B, D, E are valid per OCI's Data Labeling features.
Oracle Cloud Infrastructure Data Labeling Documentation, "Image Annotation Types".
NEW QUESTION # 138
You have an embarrassingly parallel or distributed batch job on a large amount of data that you consider running using Data Science Jobs. What would be the best approach to run the workload?
- A. Create a new job for every job run that you have to run in parallel, because the Data Science Jobs service can have only one job run per job
- B. Reconfigure the job run because Data Science Jobs does not support embarrassingly parallel workloads
- C. Create the job in Data Science Jobs and start a job run. When it is done, start a new job run until you achieve the number of runs required
- D. Create the job in Data Science Jobs and then start the number of simultaneous job runs required for your workload
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Optimize embarrassingly parallel workload in OCI Jobs.
* Evaluate Options:
* A: Sequential runs-Inefficient for parallel tasks.
* B: Simultaneous runs-Maximizes parallelism-correct.
* C: False-Jobs support parallelism.
* D: One job per run-Misstates capability, wasteful.
* Reasoning: B leverages OCI's parallel run support.
* Conclusion: B is correct.
OCI documentation states: "For embarrassingly parallel tasks, create one Job and launch multiple simultaneous Job Runs (B) to process data efficiently." A is slow, C is incorrect, and Dovercomplicates-B is the best approach.
Oracle Cloud Infrastructure Data Science Documentation, "Parallel Job Execution".
NEW QUESTION # 139
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. Feature Permutation Importance Explanations
- B. Accumulated Local Effects
- C. Feature Dependence Explanations
- D. Local Interpretable Model-Agnostic Explanations
Answer: B
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 # 140
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