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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Machine Learning Fundamentals | 20% | - Supervised learning
|
| Topic 2: OCI Data Science Service | 30% | - Projects and notebooks
|
| Topic 3: MLOps and OCI Integration | 20% | - Automation and pipelines
|
| Topic 4: Model Development and Deployment | 30% | - Model training
|
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NEW QUESTION # 57
Which of these options allow the sharing and loading back of ML models into a notebook session?
Answer: B
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 # 58
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: A,B,D
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 # 59
You are preparing a configuration object necessary to create a Data Flow application. Which THREE parameter values should you provide?
Answer: A,B,D
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify three required params for an OCI Data Flow app config.
Understand Data Flow: Runs Spark apps; needs compartment, storage, and identity.
Evaluate Options:
A: Archive path—Optional if script is in Object Storage—incorrect.
B: Local script path—Not needed; script is uploaded—incorrect.
C: Compartment—Required for resource scope—correct.
D: Bucket—Required for script storage/access—correct.
E: Display name—Required for app identification—correct.
Reasoning: C, D, E are mandatory metadata for Data Flow creation—script location is specified via bucket.
Conclusion: C, D, E are correct.
OCI documentation states: “To create a Data Flow application, configure the compartment OCID (C), Object Storage bucket for the PySpark script (D), and a display name (E) in the application object.” Local paths (B) or archives (A) are optional or handled separately—only C, D, E are required per OCI’s Data Flow API spec.
1: Oracle Cloud Infrastructure Data Flow Documentation, "Creating Applications".
NEW QUESTION # 60
Which of these is a unique feature of the published conda environment?
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
Understand Published Conda Environments: In OCI Data Science, these are custom conda environments shared across users via Object Storage.
Evaluate Options:
A: Vague—All conda environments can address use cases; not unique to “published.” B: Incorrect—Availability on reactivation applies to session persistence, not publishing.
C: Correct—Publishing saves the environment to Object Storage for sharing/reuse.
D: Incorrect—Block volumes store session data, not published environments.
Reasoning: The unique aspect of “published” environments is their storage in Object Storage (via odsc conda publish), enabling team access.
Conclusion: C is the distinctive feature.
The OCI Data Science documentation highlights that “published conda environments are saved to an OCI Object Storage Bucket, allowing them to be shared across notebook sessions and users.” This distinguishes C from A (generic), B (session-related), and D (block volume is for session state, not publishing). Publishing to Object Storage is the defining trait per Oracle’s design.
1: Oracle Cloud Infrastructure Data Science Documentation, "Managing Conda Environments - Publishing" section.
NEW QUESTION # 61
Which statement accurately describes an aspect of machine learning models?
Answer: B
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Find a true statement about ML models.
Evaluate Options:
A: True—Data drift (changes in data distribution) degrades performance over time.
B: False—Static predictions don’t improve without retraining.
C: False—Models need updates as data changes, unlike static software.
D: False—Even high-quality models require retraining with new data.
Reasoning: A reflects the reality of data drift, a common ML challenge.
Conclusion: A is correct.
OCI documentation notes: “Model performance can degrade over time due to data drift, where the underlying data distribution changes, necessitating monitoring and retraining.” B, C, and D contradict this—static predictions don’t improve (B), models aren’t static (C), and retraining is needed (D). A is the accurate aspect.
1: Oracle Cloud Infrastructure Data Science Documentation, "Model Monitoring and Drift".
NEW QUESTION # 62
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