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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement End-to-End Machine Learning Lifecycle | 45% | - Use AutoML and built-in algorithms - Data preparation, exploration, and transformation - Model saving, cataloging, and versioning - Deploy models and manage endpoints - Model development, training, and evaluation |
| Topic 2: Integrate Related OCI Services | 10% | - Use OCI AI and data services with Data Science - Integration with OCI Object Storage, Vault, and Networking |
| Topic 3: Design and Set Up Data Science Workspace | 15% | - Manage access control, security, and IAM integration - Configure compute shapes, storage, and networking - Create and manage projects and notebook sessions |
| Topic 4: Apply MLOps Practices | 20% | - Model monitoring, drift detection, and performance tracking - ML pipelines, automation, and reproducibility - Governance, auditing, and compliance |
| Topic 5: OCI Data Science - Introduction & Configuration | 10% | - Tenancy and environment configuration for Data Science - Capabilities of the Accelerated Data Science (ADS) SDK - Overview and core concepts of OCI Data Science |
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NEW QUESTION # 87
Select two reasons why it is important to rotate encryption keys when using Oracle Cloud Infrastructure (OCI) Vault to store credentials or other secrets.
Answer: B,D
NEW QUESTION # 88
You have just started as a data scientist at a healthcare company. You have been asked to analyze and improve a deep neural network model, which was built based on the electrocardiogram records of patients. There are no details about the model framework that was built. What would be the best way to find more details about the machine learning models inside the model catalog?
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
Context Analysis: You need to investigate an existing deep neural network model in the OCI Model Catalog with no prior information.
Understand Model Catalog: The Model Catalog stores trained models along with metadata, hyperparameters, and provenance (origin and history) details.
Evaluate Options:
A . Refer to the code inside the model: The model artifact (e.g., a serialized file like .pkl) doesn’t typically include readable source code; it’s a trained object, not the training script.
B . Check for model taxonomy details: Taxonomy (e.g., classification vs. regression) provides high-level categorization but lacks specifics like framework or architecture.
C . Check for metadata tags: Metadata includes name, description, and tags, offering some context but not detailed framework info (e.g., TensorFlow vs. PyTorch).
D . Check for provenance details: Provenance tracks the model’s creation process, including the framework, training environment, and data sources, providing the most comprehensive insight.
Reasoning: Provenance details are designed to document the “how” and “what” of model creation, making them ideal for uncovering the framework (e.g., Keras, PyTorch) and other specifics absent from initial handover.
Conclusion: D is the best approach for detailed investigation.
In OCI Data Science, the Model Catalog stores provenance information, which includes “details about the model’s origin, such as the framework used (e.g., TensorFlow, PyTorch), the training environment, and dataset references.” This is more informative than metadata tags (C), which are user-defined and less structured, or taxonomy (B), which is broad. The model artifact (A) is a binary file (e.g., pickle), not a readable codebase. Provenance (D) offers a detailed audit trail, critical for analyzing an undocumented deep neural network model like this one.
1: Oracle Cloud Infrastructure Data Science Documentation, "Model Catalog - Provenance Details" section.
NEW QUESTION # 89
True or false? Bias is a common problem in data science applications.
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Assess if bias is a common issue in data science.
Define Bias: Systematic errors in data/models (e.g., skewed training data).
Evaluate Statement:
Bias arises from unrepresentative data, poor feature selection, or algorithmic flaws—widely recognized in ML.
Examples: Gender bias in hiring models, racial bias in facial recognition.
Reasoning: Literature and practice (e.g., fairness in AI) confirm bias as prevalent.
Conclusion: A (True) is correct.
OCI documentation notes: “Bias is a common challenge in data science, stemming from imbalanced datasets or flawed assumptions, requiring techniques like re-weighting or fairness checks.” This aligns with industry standards—bias is a well-documented issue, making A true.
1: Oracle Cloud Infrastructure Data Science Documentation, "Addressing Bias in Models".
NEW QUESTION # 90
You have received machine learning model training code, without clear information about the optimal shape to run the training. 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: Optimize compute shape for cost and time.
Evaluate Options:
A: Tuning params—Focuses on model, not shape.
B: Strongest shape—Costly, unbalanced.
C: Scale up when utilized—Balances cost/time—correct.
D: Random start—Unsystematic.
Reasoning: C iteratively optimizes based on utilization.
Conclusion: C is correct.
OCI documentation advises: “Start with a small shape, monitor utilization and time (C); scale up if fully utilized until performance stabilizes—optimizes cost and speed.” A misfocuses, B overspends, D lacks method—only C aligns.
1: Oracle Cloud Infrastructure Data Science Documentation, "Compute Shape Optimization".
NEW QUESTION # 91
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?
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.
1: Oracle Cloud Infrastructure Data Science Documentation, "Job Artifacts".
NEW QUESTION # 92
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