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| Section | Objectives |
|---|---|
| Use Related OCI Services | - Apply OCI services for data ingestion, storage, and processing - Integrate OCI Data and AI services - Design machine learning solutions for business use cases |
| Design and Set Up Data Science Workspace | - Use Accelerated Data Science SDK and open source tools - Create and configure Data Science projects - Manage notebook sessions and compute resources |
| 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 |
| Implement End-to-End Machine Learning Lifecycle | - Save and manage models using Model Catalog - Deploy models and consume model endpoints - Prepare and manage datasets - Automate machine learning workflows and pipelines - Build, train, and evaluate machine learning models |
| Apply MLOps Practices | - Implement model lifecycle management - Monitor and maintain machine learning models - Use best practices for operationalizing ML solutions |
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NEW QUESTION # 34
Which model has an open-source, open model format that allows you to run machine learning models on different platforms?
Answer: B
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 # 35
You have an embarrassingly parallel or distributed batch job with a large amount of data running using Data Science Jobs. What would be the best approach to run the workload?
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Optimize an embarrassingly parallel job in OCI Data Science Jobs.
Define Embarrassingly Parallel: Tasks are independent, ideal for simultaneous runs.
Evaluate Options:
A: Multiple simultaneous runs—Leverages parallelism—correct.
B: One job per run—Misstates capability; unnecessary complexity.
C: Sequential runs—Inefficient, ignores parallelism.
D: False—Jobs support parallelism.
Reasoning: A maximizes efficiency for parallel tasks.
Conclusion: A is correct.
OCI documentation states: “For embarrassingly parallel workloads, create a single Job and launch multiple simultaneous Job Runs to process data in parallel.” B misinterprets limits, C wastes time, and D denies capability—only A fits OCI’s design.
1: Oracle Cloud Infrastructure Data Science Documentation, "Parallel Job Runs".
NEW QUESTION # 36
Which feature of Oracle Cloud Infrastructure Data Science provides an interactive coding environment for building and training machine learning models?
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify the interactive coding environment in OCI Data Science.
Evaluate Options:
A: Model Catalog stores models—not for coding.
B: Jobs run predefined tasks—not interactive.
C: Notebook Sessions provide JupyterLab for coding and training—interactive.
D: Projects organize work—not a coding environment.
Reasoning: Notebook Sessions are OCI’s Jupyter-based tool for interactive ML development.
Conclusion: C is correct.
OCI Data Science Notebook Sessions “provide an interactive JupyterLab environment where data scientists can write code, explore data, and train machine learning models.” Model Catalog (A) is for storage, Jobs (B) for automation, and Projects (D) for organization—only C offers interactivity.
1: Oracle Cloud Infrastructure Data Science Documentation, "Notebook Sessions Overview".
NEW QUESTION # 37
Which Oracle Accelerated Data Science (ADS) classes can be used for easy access to datasets from reference libraries and index websites such as scikit-learn?
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify ADS class for dataset access (e.g., scikit-learn).
Evaluate Options:
A: DataLabeling—Not an ADS class.
B: DatasetBrowser—Not real.
C: SecretKeeper—Credentials, not data.
D: DatasetFactory—Loads datasets (e.g., open())—correct.
Reasoning: DatasetFactory simplifies library dataset access.
Conclusion: D is correct.
OCI documentation states: “DatasetFactory (D) in ADS SDK accesses datasets from libraries like scikit-learn (e.g., DatasetFactory.open('sklearn.datasets:load_iris')).” A, B, and C don’t exist or apply—only D fits.
1: Oracle Cloud Infrastructure ADS SDK Documentation, "DatasetFactory".
NEW QUESTION # 38
Which statement about resource principals is true?
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
Define Resource Principals: They allow OCI resources (e.g., notebook sessions) to authenticate to other OCI services without user credentials.
Evaluate Options:
A: False—Resource principals eliminate manual credential management.
B: False—They’re secure, leveraging IAM policies, not less secure than API keys.
C: False—Data Science supports resource principals for accessing resources (e.g., Object Storage).
D: True—Resource principals are an IAM feature authorizing resources as actors.
Reasoning: D captures the essence of resource principals as an IAM mechanism.
Conclusion: D is correct.
OCI documentation states: “A resource principal is an IAM feature that enables OCI resources, such as compute instances or notebook sessions, to act as principal actors and authenticate to other OCI services using policies.” This refutes A (no credentials needed), B (secure method), and C (supported in Data Science), making D the accurate statement.
1: Oracle Cloud Infrastructure IAM Documentation, "Resource Principals".
NEW QUESTION # 39
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