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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Design and Set Up Data Science Workspace | 15% | - Create and manage projects and notebook sessions - Configure compute shapes, storage, and networking - Manage access control, security, and IAM integration |
| Topic 2: OCI Data Science - Introduction & Configuration | 10% | - Capabilities of the Accelerated Data Science (ADS) SDK - Tenancy and environment configuration for Data Science - Overview and core concepts of OCI Data Science |
| Topic 3: Apply MLOps Practices | 20% | - Governance, auditing, and compliance - ML pipelines, automation, and reproducibility - Model monitoring, drift detection, and performance tracking |
| Topic 4: Integrate Related OCI Services | 10% | - Use OCI AI and data services with Data Science - Integration with OCI Object Storage, Vault, and Networking |
| Topic 5: Implement End-to-End Machine Learning Lifecycle | 45% | - Model saving, cataloging, and versioning - Use AutoML and built-in algorithms - Model development, training, and evaluation - Data preparation, exploration, and transformation - Deploy models and manage endpoints |
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NEW QUESTION # 131
Which is NOT a valid OCI Data Science notebook session approach?
Answer: E
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify an invalid notebook session practice.
Evaluate Options:
A: Valid—Long runs should use Jobs, not notebooks.
B: Valid—Terminal runs with logging are supported.
C: Valid—Multi-user conflicts are a concern.
D: Invalid—Copying from Object Storage to block volume is unnecessary; direct access is best.
E: Valid—Resource principals are secure and recommended.
Reasoning: D contradicts OCI’s direct-access efficiency.
Conclusion: D is incorrect.
OCI documentation advises: “Access data in Object Storage directly from notebook sessions using SDKs or resource principals—avoid local copies (D) unless necessary.” A, B, C, and E are best practices—D is inefficient and not standard.
1: Oracle Cloud Infrastructure Data Science Documentation, "Notebook Session Best Practices".
NEW QUESTION # 132
Which OCI service enables you to build, train, and deploy machine learning models in the cloud?
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify the OCI service for ML model lifecycle.
Evaluate Options:
A: Data Catalog—Metadata management, not ML.
B: Data Integration—ETL, not ML.
C: Data Science—Full ML lifecycle—correct.
D: Data Flow—Spark processing, not full ML.
Reasoning: C supports building, training, deploying models.
Conclusion: C is correct.
OCI documentation states: “OCI Data Science (C) provides tools to build, train, and deploy machine learning models in the cloud, including notebooks and model catalog.” A, B, and D serve other purposes—only C fits the ML lifecycle per OCI’s offerings.
1: Oracle Cloud Infrastructure Data Science Documentation, "Service Overview".
NEW QUESTION # 133
As a data scientist, you are tasked with creating a model training job that is expected to take different hyperparameter values on every run. What is the most efficient way to set those parameters with Oracle Data Science Jobs?
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Efficiently manage varying hyperparameters in OCI Data Science Jobs.
Understand OCI Jobs: Jobs execute predefined tasks with configurable inputs (e.g., env vars, args).
Evaluate Options:
A: New job per run with env vars—Redundant job creation, inefficient.
B: New job per run with args—Similarly inefficient due to repeated setup.
C: Hardcode params, new job per change—Highly inefficient, requires code edits.
D: Single job, flexible params via env vars or args—Efficient, reusable—correct.
Reasoning: D minimizes job creation, allows runtime flexibility via configuration—best practice.
Conclusion: D is correct.
OCI documentation states: “For Jobs with varying hyperparameters, write code to accept environment variables or command-line arguments (D), then configure these per Job Run using the OCI Console or SDK—most efficient approach.” Options A, B, and C involve unnecessary job proliferation or code changes—only D aligns with OCI’s design for parameterized runs.
1: Oracle Cloud Infrastructure Data Science Documentation, "Configuring Job Runs with Parameters".
NEW QUESTION # 134
Which TWO statements about Oracle Cloud Infrastructure (OCI) Open Data service are true?
Answer: A,D
Explanation:
Detailed Answer in Step-by-Step Solution:
Analyze OCI Open Data: OCI Open Data is a free service providing access to public datasets for AI/ML use cases.
Evaluate Statements:
A: True—Open Data includes text and image datasets (e.g., geospatial images).
B: False—Video and other formats may be available depending on the dataset; no strict exclusion exists.
C: False—Datasets may include metadata, but code/tooling examples aren’t guaranteed.
D: True—It’s designed for data scientists and analysts who work with datasets.
E: False—It’s not a user-contributed repository; it’s curated by Oracle.
F: False—Open Data is free and public, not subscription-based.
Select Two: A and D align with the service’s purpose and offerings.
OCI Open Data provides access to datasets like text and images (A) for AI/ML, aimed at data professionals (D). It’s a free, curated service, not user-contributed (E) or paid (F), and while it focuses on certain formats, it doesn’t explicitly exclude audio/video (B). (Oracle Cloud Infrastructure Open Data Documentation, "Overview of Open Data").
NEW QUESTION # 135
A bike sharing platform has collected user commute data for the past 3 years. For increasing profitability and making useful inferences, a machine learning model needs to be built from the accumulated data. Which of the following options has the correct order of the required machine learning tasks for building a model?
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
Data Access: The first step in any machine learning workflow is accessing the raw data. This involves retrieving the user commute data collected over the past 3 years from the bike-sharing platform’s storage system.
Data Exploration: Once data is accessed, it’s explored to understand its structure, quality, and patterns (e.g., missing values, distributions). This step helps identify what preprocessing is needed.
Feature Engineering: After understanding the data, features are created or transformed (e.g., commute duration, time of day) to improve model performance. This step precedes feature exploration because you need engineered features to analyze further.
Feature Exploration: This involves analyzing the engineered features (e.g., correlation analysis, importance ranking) to refine them or select the most relevant ones for modeling.
Modeling: Finally, the prepared data and features are used to train and evaluate a machine learning model.
Option C (Data Access, Data Exploration, Feature Engineering, Feature Exploration, Modeling) follows this logical sequence, aligning with standard ML workflows.
The correct order reflects the machine learning lifecycle as outlined in Oracle’s OCI Data Science documentation. Data Access is the initial step to retrieve data, followed by Data Exploration to assess it (e.g., using OCI Data Science Notebook Sessions with tools like pandas). Feature Engineering transforms raw data into meaningful inputs, followed by Feature Exploration to analyze feature importance (e.g., using ADS SDK’s correlation tools). Modeling is the final step where the model is built and trained. This sequence is consistent with Oracle’s recommended practices for building ML models in OCI Data Science (Oracle Cloud Infrastructure Data Science Service Documentation, "Machine Learning Lifecycle").
NEW QUESTION # 136
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