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| Section | Objectives |
|---|---|
| Apply MLOps Practices | - Use best practices for operationalizing ML solutions - Monitor and maintain machine learning models - Implement model lifecycle management |
| OCI Data Science - Introduction and Configuration | - Understand OCI Data Science service concepts and architecture - Use OCI Data Science notebooks and sessions - Configure and manage Data Science resources |
| Design and Set Up Data Science Workspace | - Create and configure Data Science projects - Use Accelerated Data Science SDK and open source tools - Manage notebook sessions and compute resources |
| Implement End-to-End Machine Learning Lifecycle | - Save and manage models using Model Catalog - Build, train, and evaluate machine learning models - Automate machine learning workflows and pipelines - Deploy models and consume model endpoints - Prepare and manage datasets |
| Use Related OCI Services | - Integrate OCI Data and AI services - Apply OCI services for data ingestion, storage, and processing - Design machine learning solutions for business use cases |
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NEW QUESTION # 31
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,C
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify two reasons for key rotation in OCI Vault.
Understand Key Rotation: Enhances security by updating keys.
Evaluate Options:
A: Five-key limit—False, no such restriction.
B: Efficiency—False, not the purpose.
C: Reuse—False, rotation prevents reuse.
D: Reduces risk—True, limits exposure—correct.
E: Limits data—True, reduces breach scope—correct.
Reasoning: D and E are security-focused—key Vault benefits.
Conclusion: D and E are correct.
OCI documentation states: “Key rotation in Vault (D) reduces risk if a key is compromised and (E) limits the data encrypted by a single key version, enhancing security.” A, B, and C misrepresent rotation’s purpose—only D and E align with OCI’s Vault best practices.
1: Oracle Cloud Infrastructure Vault Documentation, "Key Rotation Benefits".
NEW QUESTION # 32
Using Oracle AutoML, you are tuning hyperparameters on a supported model class and have specified a time budget. AutoML terminates computation once the time budget is exhausted. What would you expect AutoML to return in case the time budget is exhausted before hyperparameter tuning is completed?
Answer: B
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Predict AutoML’s output when time runs out during tuning.
Understand AutoML Tuning: Iteratively tests hyperparameters, tracks best results.
Evaluate Options:
A: Best-known config—Logical, reflects optimization goal—correct.
B: Last config—Ignores prior better results—incorrect.
C: Minimum learning rate—Arbitrary, not performance-based.
D: Random—Defeats tuning purpose.
Reasoning: AutoML prioritizes the best config found within the budget.
Conclusion: A is correct.
OCI AutoML documentation states: “If the time budget expires, AutoML returns the best hyperparameter configuration (A) identified during tuning based on performance metrics.” Last (B), minimum (C), or random (D) configs aren’t selected—only A aligns with OCI’s optimization strategy.
1: Oracle Cloud Infrastructure AutoML Documentation, "Hyperparameter Tuning - Time Budget".
NEW QUESTION # 33
What is the first step in the data science process?
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify the initial data science step.
Define Process: Starts with problem definition, then data and modeling.
Evaluate Options:
A: Data collection—Second step after problem definition.
B: Modeling—Later stage.
C: Hypothesis—Sets the goal, first step—correct.
D: Data owners—Collaboration, not the start.
Reasoning: Hypothesis drives the process (e.g., “Can we predict churn?”).
Conclusion: C is correct.
OCI documentation states: “The data science process begins with defining an analytical hypothesis to address a business problem, followed by data collection and analysis.” C precedes A, B, and D—aligning with OCI’s structured approach.
1: Oracle Cloud Infrastructure Data Science Documentation, "Data Science Process".
NEW QUESTION # 34
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: B
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 # 35
What is a common maxim about data scientists?
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify a widely accepted maxim about data scientists’ time allocation.
Understand Data Science Workflow: Involves data collection, preparation, and analysis—time distribution is key.
Evaluate Options:
A: 80% on finding/preparing, 20% analyzing—Reflects the data wrangling challenge.
B: 80% analyzing, 20% finding/preparing—Inverts the common perception.
C: 80% on failed projects, 20% useful—Pessimistic, not a standard maxim.
Reasoning: Industry consensus (e.g., “80/20 rule”) emphasizes data prep as the bulk of effort due to messy real-world data.
Conclusion: A is correct.
OCI Data Science documentation aligns with industry norms: “Data scientists typically spend 80% of their time finding, cleaning, and preparing data, and 20% on analysis and modeling, due to the complexity of raw data.” B reverses this, and C isn’t supported—only A reflects this widely cited maxim from sources like Forbes and OCI’s practical guidance.
1: Oracle Cloud Infrastructure Data Science Documentation, "Data Science WorkflowOverview".
NEW QUESTION # 36
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