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| Section | Objectives |
|---|---|
| Implement End-to-End Machine Learning Lifecycle | - Save and manage models using Model Catalog - Deploy models and consume model endpoints - Automate machine learning workflows and pipelines - Prepare and manage datasets - Build, train, and evaluate machine learning models |
| Use Related OCI Services | - Apply OCI services for data ingestion, storage, and processing - Design machine learning solutions for business use cases - Integrate OCI Data and AI services |
| OCI Data Science - Introduction and Configuration | - Understand OCI Data Science service concepts and architecture - Configure and manage Data Science resources - Use OCI Data Science notebooks and sessions |
| Design and Set Up Data Science Workspace | - Manage notebook sessions and compute resources - Create and configure Data Science projects - Use Accelerated Data Science SDK and open source tools |
| Apply MLOps Practices | - Monitor and maintain machine learning models - Use best practices for operationalizing ML solutions - Implement model lifecycle management |
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NEW QUESTION # 64
Which TWO statements about Oracle Cloud Infrastructure (OCI) Open Data service are true?
Answer: E,F
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 # 65
Which of these options allow the sharing and loading back of ML models into a notebook session?
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify the mechanism for sharing and reloading ML models in OCI Data Science.
Evaluate Options:
A . Model provenance: Tracks model origin—informative but not a sharing mechanism.
B . Model taxonomy: Categorizes models (e.g., regression)—not for sharing/loading.
C . Model deployment: Makes models accessible as endpoints, not for notebook reloading.
D . Model catalog: Stores models and artifacts, enabling sharing and loading into sessions.
Reasoning: The Model Catalog is OCI’s centralized repository for saving, sharing, and retrieving models (e.g., via ADS SDK).
Conclusion: D is the correct tool.
The OCI Model Catalog “enables data scientists to save trained models and their artifacts, share them with team members, and load them back into notebook sessions for further use or evaluation.” Provenance (A) and taxonomy (B) are metadata, while deployment (C) serves inference, not notebook access. D is explicitly designed for this purpose.
1: Oracle Cloud Infrastructure Data Science Documentation, "Model Catalog Usage".
NEW QUESTION # 66
You are a data scientist trying to load data into your notebook session. You understand that Accelerated Data Science (ADS) SDK supports loading various data formats. Which of the following THREE are ADS-supported data formats?
Answer: B,D,E
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify three data formats supported by ADS SDK for loading data.
Understand ADS SDK: Facilitates data loading into notebook sessions via DatasetFactory.
Evaluate Options:
A . DOCX: Not natively supported—requires conversion (e.g., to text).
B . Pandas DataFrame: Supported—core format for data manipulation in ADS.
C . JSON: Supported—common structured data format.
D . Raw Images: Not directly supported—image data needs preprocessing (e.g., via Vision).
E . XML: Supported—parseable structured format.
Reasoning: ADS focuses on tabular/structured data—B, C, E align; A and D require external handling.
Conclusion: B, C, E are correct.
OCI documentation states: “ADS SDK’s DatasetFactory supports loading data from formats like Pandas DataFrames (B), JSON (C), and XML (E), enabling easy integration into notebook sessions.” DOCX (A) isn’t natively handled, and raw images (D) require preprocessing outside ADS—B, C, E match the supported list.
1: Oracle Cloud Infrastructure ADS SDK Documentation, "Supported Data Formats".
NEW QUESTION # 67
As a data scientist, you are working on a global health dataset that has data from more than 50 countries. You want to encode three features, such as 'countries', 'race', and 'body organ' as categories. Which option would you use to encode the categorical feature?
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Encode categorical features in a Data Science context (likely ADS SDK).
Understand Encoding: Converts categories (e.g., countries) to numerical forms.
Evaluate Options:
A: Not a standard ADS method—incorrect.
B: General transformation, not specific encoding—incorrect.
C: OneHotEncoder—Standard for categorical encoding—correct.
D: Visualization, not encoding—incorrect.
Reasoning: One-hot encoding creates binary columns—ideal for multiple categories.
Conclusion: C is correct.
OCI documentation states: “In ADS SDK, use OneHotEncoder (C) from sklearn (or similar) to encode categorical features like ‘countries’ into binary vectors for modeling.” A isn’t real, B is too broad, D is unrelated—only C fits OCI’s encoding practice.
1: Oracle Cloud Infrastructure Data Science Documentation, "Feature Encoding with ADS".
NEW QUESTION # 68
For your next data science project, you need access to public geospatial images. Which Oracle Cloud service provides free access to those images?
Answer: B
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Find the OCI service for free geospatial images.
Evaluate Options:
A: Big Data Service—Spark processing, not datasets.
B: Analytics Cloud—Visualization, not data source.
C: Data Science—ML platform, not dataset provider.
D: Open Data—Free public datasets, including geospatial—correct.
Reasoning: Open Data is OCI’s public dataset hub.
Conclusion: D is correct.
OCI documentation states: “Oracle Open Data provides free access to curated datasets, including geospatial images, for public use.” A, B, and C serve other purposes—only D delivers free geospatial data.
1: Oracle Cloud Infrastructure Open Data Documentation, "Dataset Offerings".
NEW QUESTION # 69
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