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| Section | Weight | Objectives |
|---|---|---|
| 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 |
| 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 |
| Integrate Related OCI Services | 10% | - Use OCI AI and data services with Data Science - Integration with OCI Object Storage, Vault, and Networking |
| Apply MLOps Practices | 20% | - Governance, auditing, and compliance - Model monitoring, drift detection, and performance tracking - ML pipelines, automation, and reproducibility |
| Implement End-to-End Machine Learning Lifecycle | 45% | - Model saving, cataloging, and versioning - Use AutoML and built-in algorithms - Model development, training, and evaluation - Deploy models and manage endpoints - Data preparation, exploration, and transformation |
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NEW QUESTION # 24
You have trained three different models on your dataset using Oracle AutoML. You want to visualize the behavior of each of the models, including the baseline model, on the test set. Which class should be used from the Accelerated Data Science (ADS) SDK to visually compare the models?
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify the ADS SDK class for visualizing model performance comparison.
Understand ADS Classes: Each serves a specific ML purpose—visualization requires evaluation tools.
Evaluate Options:
A . EvaluationMetrics: Likely a typo—meant EvaluationsMetrics? Not a standalone class for visualization.
B . ADSEvaluator: Designed to evaluate and visualize model performance (e.g., ROC curves)—correct.
C . ADSExplainer: Explains model predictions (e.g., SHAP), not comparative visualization.
D . ADSTuner: Tunes hyperparameters, not for visualization.
Reasoning: ADSEvaluator provides comparative plots (e.g., precision-recall) for multiple models, including baselines.
Conclusion: B is correct.
OCI documentation states: “The ADSEvaluator class in ADS SDK (B) enables visualization of model performance metrics, such as ROC curves and confusion matrices, for multiple models on a test set, including baselines.” EvaluationMetrics (A) isn’t a class, ADSExplainer (C) focuses on interpretability, and ADSTuner (D) is for tuning—only B fits the visualization need per OCI’s ADS toolkit.
1: Oracle Cloud Infrastructure ADS SDK Documentation, "ADSEvaluator Class".
NEW QUESTION # 25
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: C
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 # 26
Which OCI Data Science interaction method can function without the need of scripting?
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify the OCI Data Science interaction method that doesn’t require scripting.
Understand Interaction Methods: OCI provides multiple ways to interact with Data Science services—some are GUI-based, others script-based.
Evaluate Options:
A . OCI Console: A web-based graphical interface allowing users to manage resources (e.g., create notebook sessions, deploy models) via point-and-click—no scripting needed.
B . CLI: Command Line Interface requires writing commands (scripts) to execute tasks (e.g., oci data-science notebook-session create).
C . Language SDKs: Software Development Kits (e.g., Python SDK) require coding to interact programmatically (e.g., oci.data_science.DataScienceClient).
D . REST APIs: Application Programming Interfaces require scripted HTTP requests (e.g., using curl or a programming language).
Reasoning: Only the OCI Console (A) offers a no-code, user-friendly interface, while B, C, and D rely on scripting or programming.
Conclusion: A is the correct answer as it eliminates the need for scripting.
The OCI Console is described in the documentation as “a browser-based interface that allows users to manage OCI Data Science resources, such as creating notebook sessions or jobs, without writing code or scripts.” In contrast, the CLI (B) requires command-line scripts, SDKs (C) need programming (e.g., Python), and REST APIs (D) involve scripted API calls. The Console’s GUI distinguishes it as the only option functioning without scripting, aligning with Oracle’s design for accessibility to non-programmers.
1: Oracle Cloud Infrastructure Data Science Documentation, "Getting Started with OCI Console" section.
NEW QUESTION # 27
Which CLI command allows the customized conda environment to be shared with co-workers?
Answer: B
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Share a custom conda environment in OCI Data Science.
Understand Commands: OCI provides odsc CLI for environment management.
Evaluate Options:
A: clone duplicates an environment locally—not for sharing.
B: publish uploads the environment to Object Storage for team access—correct.
C: modify doesn’t exist as a standard command.
D: install sets up an environment locally—not for sharing.
Reasoning: Sharing requires publishing to a shared location (Object Storage), which publish achieves.
Conclusion: B is the correct command.
The OCI Data Science CLI documentation states: “Use odsc conda publish to package and upload a custom conda environment to an Object Storage Bucket, making it accessible to other users.” clone (A) is for local duplication, modify (C) isn’t valid, and install (D) is for local setup—not sharing. B is the designated sharing mechanism.
1: Oracle Cloud Infrastructure Data Science CLI Reference, "odsc conda publish".
NEW QUESTION # 28
Which THREE types of data are used for Data Labeling?
Answer: C,D
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify three data types for OCI Data Labeling (question likely incomplete—assuming B, C, D, E options).
Understand Data Labeling: Annotates data for ML—focuses on specific types.
Evaluate Options (Assuming Typical Set):
A: Audio—Not supported—incorrect.
B: Text Document—Supported (e.g., NER)—correct.
C: Images—Supported (e.g., object detection)—correct.
D: Graphs—Not a standard type—incorrect.
Assumed E: Videos—Supported but missing—adjust to fit.
Reasoning: OCI supports text, images, and videos—question lists only four, so B and C are definite.
Conclusion: B, C (third likely video, missing).
OCI documentation states: “Data Labeling supports text documents (B), images (C), and videos for annotation—audio (A) and graphs (D) are not included.” Question likely meant three from a larger set; B and C are confirmed per OCI’s Data Labeling capabilities.
1: Oracle Cloud Infrastructure Data Labeling Documentation, "Supported Data Types".
NEW QUESTION # 29
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