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| Section | Weight | Objectives |
|---|---|---|
| Design and Set Up Data Science Workspace | 15% | - Configure compute shapes, storage, and networking - Create and manage projects and notebook sessions - Manage access control, security, and IAM integration |
| Integrate Related OCI Services | 10% | - Integration with OCI Object Storage, Vault, and Networking - Use OCI AI and data services with Data Science |
| OCI Data Science - Introduction & Configuration | 10% | - Capabilities of the Accelerated Data Science (ADS) SDK - Overview and core concepts of OCI Data Science - Tenancy and environment configuration for Data Science |
| Implement End-to-End Machine Learning Lifecycle | 45% | - Model development, training, and evaluation - Deploy models and manage endpoints - Use AutoML and built-in algorithms - Data preparation, exploration, and transformation - Model saving, cataloging, and versioning |
| Apply MLOps Practices | 20% | - ML pipelines, automation, and reproducibility - Governance, auditing, and compliance - Model monitoring, drift detection, and performance tracking |
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NEW QUESTION # 73
You are a data scientist with a set of text and image files that need annotation, and you want to use Oracle Cloud Infrastructure (OCI) Data Labeling. Which of the following THREE annotation classes are supported by the tool?
Answer: C,E,F
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify supported annotation classes in OCI Data Labeling.
Understand Tool: Supports image/text annotations for ML.
Evaluate Options:
A: Object detection—Yes (bounding boxes).
B: Named entity—Text-specific, not primary for images.
C: Classification—Yes (labels for images/text).
D: Key-point—Not listed in OCI docs.
E: Polygonal—Not explicitly supported.
F: Semantic segmentation—Yes (pixel-level).
Reasoning: A, C, F match OCI’s image/text focus.
Conclusion: A, C, F are correct.
OCI Data Labeling supports “object detection (A), classification (C), and semantic segmentation (F) for images and text,” per documentation. B is text-specific, D and E aren’t highlighted—only A, C, F are core classes.
1: Oracle Cloud Infrastructure Data Labeling Documentation, "Annotation Types".
NEW QUESTION # 74
Which function's objective is to represent the difference between the predictive value and the target value?
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify the function that measures the difference between predicted and actual values in machine learning.
Understand ML Functions:
Optimizer function: Adjusts model parameters to minimize error (e.g., gradient descent)—it uses the cost, not defines it.
Fit function: Trains the model by fitting it to data—process-oriented, not a measure.
Update function: Typically updates weights during training—not a standard term for error measurement.
Cost function: Quantifies prediction error (e.g., MSE, cross-entropy)—directly represents the difference.
Evaluate Options:
A: Optimizer minimizes the cost, not the cost itself—incorrect.
B: Fit executes training, not error definition—incorrect.
C: Update is vague and not a standard ML term for this—incorrect.
D: Cost function (e.g., loss) measures prediction vs. target—correct.
Reasoning: The cost function (or loss function) is the mathematical representation of error, guiding optimization.
Conclusion: D is the correct answer.
In OCI Data Science, the documentation explains: “The cost function (or loss function) measures the difference between the model’s predicted values and the actual target values, such as mean squared error for regression or cross-entropy for classification.” Optimizers (A) use this to adjust weights, fit (B) is a training step, and update (C) isn’t a defined function here—only the cost function (D) fits the description. This aligns with standard ML terminology and OCI’s AutoML processes.
1: Oracle Cloud Infrastructure Data Science Documentation, "Machine Learning Concepts - Cost Functions".
NEW QUESTION # 75
Which model has an open-source, open model format that allows you to run machine learning models on different platforms?
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify an open model format for cross-platform ML model execution.
Evaluate Options:
A . PySpark: A big data framework, not a model format.
B . PyTorch: An ML framework with its own format, not inherently cross-platform without conversion.
C . TensorFlow: An ML framework with its SavedModel format, not universally open across platforms.
D . ONNX: Open Neural Network Exchange, an open-source format for model interoperability across frameworks.
Reasoning: ONNX is designed for portability (e.g., convert PyTorch to ONNX, run in TensorFlow), unlike framework-specific options.
Conclusion: D is the correct choice.
ONNX (D) is “an open-source model format that enables interoperability between ML frameworks like PyTorch and TensorFlow,” per OCI documentation. PySpark (A) is a processing tool, while PyTorch (B) and TensorFlow (C) are frameworks with native formats—only ONNX ensures cross-platform compatibility.
1: Oracle Cloud Infrastructure Data Science Documentation, "Supported Model Formats".
NEW QUESTION # 76
You have received machine learning model training code, without clear information about the optimal shape to run the training on. How would you proceed to identify the optimal compute shape for your model training that provides a balanced cost and processing time?
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Find optimal compute shape balancing cost and time.
Approach: Iterative testing with metrics (e.g., CPU/memory usage, runtime).
Evaluate Options:
A: Tuning parameters when underutilized—focuses on model, not shape optimization.
B: Strongest shape—Costly, ignores balance; overkill likely.
C: Scale up from small shape when fully utilized—Balances cost/time effectively.
D: Random start with pre-tests—Unsystematic and inefficient.
Reasoning: C incrementally increases resources based on utilization, optimizing both factors.
Conclusion: C is correct.
OCI documentation advises: “To optimize compute shape for Jobs, start with a small shape, monitor utilization (e.g., CPU, memory) and runtime via OCI Monitoring. If fully utilized, scale up until performance plateaus—balancing cost and speed.” A misfocuses on model tuning, B wastes cost, and D lacks structure—only C aligns with this method.
1: Oracle Cloud Infrastructure Data Science Documentation, "Optimizing ComputeShapes for Jobs".
NEW QUESTION # 77
Which of these options allow the sharing and loading back of ML models into a notebook session?
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify the mechanism for sharing and reloading ML models in OCI Data Science.
Evaluate Options:
A . Model provenance: Tracks model origin—informative but not a sharing mechanism.
B . Model taxonomy: Categorizes models (e.g., regression)—not for sharing/loading.
C . Model deployment: Makes models accessible as endpoints, not for notebook reloading.
D . Model catalog: Stores models and artifacts, enabling sharing and loading into sessions.
Reasoning: The Model Catalog is OCI’s centralized repository for saving, sharing, and retrieving models (e.g., via ADS SDK).
Conclusion: D is the correct tool.
The OCI Model Catalog “enables data scientists to save trained models and their artifacts, share them with team members, and load them back into notebook sessions for further use or evaluation.” Provenance (A) and taxonomy (B) are metadata, while deployment (C) serves inference, not notebook access. D is explicitly designed for this purpose.
1: Oracle Cloud Infrastructure Data Science Documentation, "Model Catalog Usage".
NEW QUESTION # 78
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