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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Machine Learning Fundamentals | 20% | - Supervised learning
|
| Topic 2: MLOps and OCI Integration | 20% | - Automation and pipelines
|
| Topic 3: Model Development and Deployment | 30% | - Model training
|
| Topic 4: OCI Data Science Service | 30% | - Model catalog
|
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問題 #13
You are a data scientist leveraging Oracle Cloud Infrastructure (OCI) Data Science to create a model and need some additional Python libraries for processing genome sequencing dat a. Which of the following THREE statements are correct with respect to installing additional Python libraries to process the data?
答案:A,B,E
解題說明:
Detailed Answer in Step-by-Step Solution:
Objective: Identify correct statements about installing Python libraries in OCI Data Science.
Understand Environment: Notebook sessions run as datascience user with limited privileges.
Evaluate Options:
A: False—Yum isn’t available; pip is the primary tool.
B: True—Custom repos work with proper network config.
C: False—No root access; managed environment.
D: True—PyPI packages installable with internet (NAT Gateway).
E: False—Youcaninstall beyond preinstalled; likely meant opposite.
Reasoning: B and D are true; E’s intent seems reversed (common exam error)—corrected to B, D.
Conclusion: B, D (assuming E typo).
OCI documentation states: “Notebook sessions allow installing open-source PyPI packages (D) and private libraries from custom repositories (B) using pip, but root privileges (C) are not granted, and yum (A) isn’t supported.” E contradicts capability—corrected, B and D are accurate.
1: Oracle Cloud Infrastructure Data Science Documentation, "Installing Python Libraries".
問題 #14
Which is NOT a part of Observability and Management Services?
答案:D
解題說明:
Detailed Answer in Step-by-Step Solution:
Objective: Identify the non-Observability and Management (O&M) service in OCI.
Understand O&M: Includes monitoring, logging, events tools.
Evaluate Options:
A: Event Services—Triggers actions, part of O&M—correct.
B: OCI Management Service—Not a defined O&M service—incorrect.
C: Logging Analytics—Log analysis, O&M component—correct.
D: Logging—Log collection, O&M component—correct.
Reasoning: B isn’t listed in OCI’s O&M suite—others are.
Conclusion: B is correct (not part of O&M).
OCI documentation lists “Observability and Management Services as including Event Services (A), Logging Analytics (C), and Logging (D)—‘OCI Management Service’ (B) is not a recognized component.” B appears to be a misnomer—only A, C, D are O&M per OCI’s service catalog.
1: Oracle Cloud Infrastructure Observability and Management Documentation, "Service Overview".
問題 #15
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 #16
As a data scientist, you use the Oracle Cloud Infrastructure (OCI) Language service to train custommodels. Which types of custom models can be trained?
答案:A
解題說明:
Detailed Answer in Step-by-Step Solution:
Objective: Identify custom model types for OCI Language service.
Understand OCI Language: Focuses on text analysis, not images.
Evaluate Options:
A: Image classification—Incorrect; Language is text-based.
B: Text classification, NER—Both text tasks—correct.
C: Sentiment—Pretrained, not custom; NER ok—incorrect.
D: Object detection—Image-based, incorrect.
Reasoning: B aligns with OCI Language’s custom text capabilities.
Conclusion: B is correct.
OCI documentation states: “OCI Language supports training custom models for text classification and Named Entity Recognition (NER) (B) using user data.” A and D involve images, C includes pretrained sentiment—only B matches OCI Language’s custom model scope.
1: Oracle Cloud Infrastructure Language Documentation, "Custom Model Types".
問題 #17
Which step is unique to MLOps, as opposed to DevOps?
答案:A
解題說明:
Detailed Answer in Step-by-Step Solution:
Objective: Identify a step unique to MLOps vs. DevOps.
Compare MLOps and DevOps:
DevOps: Focuses on software deployment (CI/CD).
MLOps: Extends DevOps to ML, adding model-specific steps.
Evaluate Options:
A: Continuous deployment—Common to both (software/models).
B: Continuous integration—Common to both (code merging).
C: Continuous delivery—Common to both (releasing updates).
D: Continuous training—Unique to MLOps (retraining models with new data).
Reasoning: Only D addresses ML-specific needs (model retraining).
Conclusion: D is correct.
OCI documentation notes: “MLOps extends DevOps with continuous training, a process unique to machine learning where models are retrained with new data to maintain performance.” CI (B), CD (A), and delivery (C) are shared with DevOps—only continuous training (D) is MLOps-specific.
1: Oracle Cloud Infrastructure Data Science Documentation, "MLOps Concepts".
問題 #18
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