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| Section | Weight | Objectives |
|---|---|---|
| Implement End-to-End Machine Learning Lifecycle | 45% | - Use AutoML and built-in algorithms - Data preparation, exploration, and transformation - Model saving, cataloging, and versioning - Deploy models and manage endpoints - Model development, training, and evaluation |
| OCI Data Science - Introduction & Configuration | 10% | - Capabilities of the Accelerated Data Science (ADS) SDK - Overview and core concepts of OCI Data Science - Tenancy and environment configuration for Data Science |
| Integrate Related OCI Services | 10% | - Integration with OCI Object Storage, Vault, and Networking - Use OCI AI and data services with Data Science |
| Design and Set Up Data Science Workspace | 15% | - Configure compute shapes, storage, and networking - Manage access control, security, and IAM integration - Create and manage projects and notebook sessions |
| Apply MLOps Practices | 20% | - Governance, auditing, and compliance - Model monitoring, drift detection, and performance tracking - ML pipelines, automation, and reproducibility |
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問題 #60
The feature type TechJob has the following registered validators:
TechJob.validator.register(name=’is_tech_job’, handler=is_tech_job_default_handler) TechJob.validator.register(name=’is_tech_job’, handler=is_tech_job_open_handler, condition=('job_family',)) TechJob.validator.register(name=’is_tech_job’, handler=is_tech_job_closed_handler, condition=('job_family': 'IT'))When you run is_tech_job(job_family='Engineering'), what does the feature type validator system do?
答案:B
解題說明:
Detailed Answer in Step-by-Step Solution:
Objective: Determine which validator handler runs for is_tech_job(job_family='Engineering').
Understand Validator System: Likely ADS SDK—executes handlers based on conditions.
Analyze Validators:
Default: is_tech_job_default_handler—No condition, fallback.
Open: is_tech_job_open_handler—Condition ('job_family',)—requires job_family arg.
Closed: is_tech_job_closed_handler—Condition ('job_family': 'IT')—requires job_family='IT'.
Evaluate Call: job_family='Engineering'—Matches job_family presence, not IT.
Reasoning:
Open handler applies (tuple condition means arg exists).
Closed fails (Engineering ≠ IT).
Default is overridden by specific matches.
Conclusion: D is correct.
OCI ADS documentation states: “Validators execute the most specific handler matching the condition; for is_tech_job(job_family='Engineering'), is_tech_job_open_handler (D) runs as it matches job_family presence, while is_tech_job_closed_handler (C) requires IT—default (A) is bypassed, no error (B).” Only D fits per ADS validator logic.
1: Oracle Cloud Infrastructure ADS SDK Documentation, "Feature Type Validators".
問題 #61
Select two reasons why it is important to rotate encryption keys when using Oracle Cloud Infrastructure (OCI) Vault to store credentials or other secrets.
答案:B,D
問題 #62
Which two statements are true about published conda environments?
答案:C,E
解題說明:
Detailed Answer in Step-by-Step Solution:
Objective: Identify true statements about published conda environments in OCI.
Understand Published Conda: Custom envs shared via Object Storage.
Evaluate Options:
A: False—Users create them, not OCI-curated.
B: True—odsc conda init sets up access to published envs.
C: False—Object Storage, not notebook, is the source.
D: False—Can create from scratch, not just modifications.
E: True—Conda env vars extend job configs.
Reasoning: B configures access, E enhances flexibility—match OCI behavior.
Conclusion: B and E are correct.
OCI documentation states: “odsc conda init (B) configures the bucket for published conda environments, and Jobs can use conda environment variables (E) alongside service vars.” A misattributes curation, C misplaces source, and D limits creation—only B and E are true per OCI’s conda management.
1: Oracle Cloud Infrastructure Data Science Documentation, "Published Conda Environments".
問題 #63
You are given the task of writing a program that sorts document images by language. Which Oracle service would you use?
答案:A
解題說明:
Detailed Answer in Step-by-Step Solution:
Objective: Identify the Oracle service to sort document images by language.
Task Breakdown: Requires extracting text from images (OCR) and detecting language—two potential services involved.
Evaluate Options:
A . Oracle Digital Assistant: Builds chatbots—irrelevant to image or language processing.
B . OCI Language: Detects and classifies languages in text—ideal for sorting after text extraction.
C . OCI Speech: Transcribes audio to text—not applicable to images.
D . OCI Vision: Performs OCR to extract text from images—necessary but not sufficient for language sorting.
Reasoning: The task emphasizes “sorting by language.” OCI Vision extracts text, but OCI Language identifies the language (e.g., English, Spanish). Since the question asks for one service and focuses on sorting, OCI Language (B) is the best fit, assuming text extraction is a precursor step.
Conclusion: B is correct.
OCI Language “provides language detection and classification capabilities, enabling identification of languages in text extracted from documents,” per the documentation. OCI Vision handles OCR, but the sorting task aligns with OCI Language (B). Digital Assistant (A) and Speech (C) don’t apply, and while Vision (D) is a prerequisite, B is the primary service for language sorting as per OCI’s AI service design.
1: Oracle Cloud Infrastructure Language Documentation, "Language Detection Features".
問題 #64
Which step is a part of the AutoML pipeline?
答案:D
解題說明:
Detailed Answer in Step-by-Step Solution:
Objective: Identify a step in OCI’s AutoML pipeline.
Understand AutoML: Automates model building—includes preprocessing, selection, and tuning.
Evaluate Options:
A: Feature Extraction (e.g., PCA) isn’t explicitly part of OCI AutoML—too specific.
B: Saving to Model Catalog is post-AutoML, not a pipeline step.
C: Deployment is a separate action after AutoML—incorrect.
D: Feature Selection (e.g., choosing relevant features) is a core AutoML step—correct.
Reasoning: OCI AutoML automates feature selection, algorithm choice, and tuning—D fits.
Conclusion: D is correct.
OCI AutoML’s pipeline includes “feature selection, algorithm selection, adaptive sampling, and hyperparameter tuning,” per the documentation. Extraction (A) isn’t highlighted, while saving (B) and deployment (C) are post-process actions—only Feature Selection (D) is an integral automated step.
1: Oracle Cloud Infrastructure Data Science Documentation, "AutoML Pipeline".
問題 #65
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