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| Section | Objectives |
|---|---|
| Implement End-to-End Machine Learning Lifecycle | - Save and manage models using Model Catalog - Prepare and manage datasets - Automate machine learning workflows and pipelines - Deploy models and consume model endpoints - Build, train, and evaluate machine learning models |
| OCI Data Science - Introduction and Configuration | - Use OCI Data Science notebooks and sessions - Configure and manage Data Science resources - Understand OCI Data Science service concepts and architecture |
| Use Related OCI Services | - Integrate OCI Data and AI services - Apply OCI services for data ingestion, storage, and processing - Design machine learning solutions for business use cases |
| Apply MLOps Practices | - Implement model lifecycle management - Monitor and maintain machine learning models - Use best practices for operationalizing ML solutions |
| Design and Set Up Data Science Workspace | - Use Accelerated Data Science SDK and open source tools - Manage notebook sessions and compute resources - Create and configure Data Science projects |
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問題 #118
Which cache rules criterion matches if the concatenation of the requested URL path and query are identical to the contents of the value field?
答案：B
解題說明：
Detailed Answer in Step-by-Step Solution:
Objective: Match a cache rule criterion for exact URL path and query.
Understand Cache Rules: Used in OCI (e.g., WAF, CDN) to cache content.
Evaluate Options:
A: Contains—Partial match, not exact.
B: Is—Exact match of full URL (path + query)—correct.
C: Ends with—Matches end, not full URL.
D: Starts with—Matches start, not full URL.
Reasoning: “URL_IS” checks exact equality—fits requirement.
Conclusion: B is correct.
OCI documentation states: “The URL_IS (B) criterion in cache rules matches when the full URL (path and query) exactly equals the specified value.” A, C, and D are partial matches—only B ensures identical concatenation per OCI’s caching config.
1: Oracle Cloud Infrastructure WAF Documentation, "Cache Rules Criteria".
問題 #119
You have created a model and want to use Accelerated Data Science (ADS) SDK to deploy the model. Where are the artifacts to deploy this model with ADS?
答案：B
解題說明：
Detailed Answer in Step-by-Step Solution:
Objective: Locate artifacts for ADS model deployment.
Understand ADS Deployment: Requires model artifacts (e.g., score.py) stored in OCI.
Evaluate Options:
A: Vault—Stores secrets, not models.
B: Depository—Not an OCI term.
C: Model Catalog—Stores models/artifacts for deployment—correct.
D: Artifactory—Not an OCI service.
Reasoning: Model Catalog is OCI’s model repository for ADS.
Conclusion: C is correct.
OCI documentation states: “ADS SDK deploys models from the Model Catalog, where trainedmodels and artifacts (e.g., score.py) are stored.” Vault (A) is for secrets, B and D aren’t real—only C supports ADS deployment.
1: Oracle Cloud Infrastructure Data Science Documentation, "ADS Model Deployment".
問題 #120
As a data scientist, you are tasked with creating a model training job that is expected to take different hyperparameter values on every run. What is the most efficient way to set those parameters with Oracle Data Science Jobs?
答案：C
解題說明：
Detailed Answer in Step-by-Step Solution:
Objective: Efficiently manage varying hyperparameters in OCI Data Science Jobs.
Understand OCI Jobs: Jobs execute predefined tasks with configurable inputs (e.g., env vars, args).
Evaluate Options:
A: New job per run with env vars—Redundant job creation, inefficient.
B: New job per run with args—Similarly inefficient due to repeated setup.
C: Hardcode params, new job per change—Highly inefficient, requires code edits.
D: Single job, flexible params via env vars or args—Efficient, reusable—correct.
Reasoning: D minimizes job creation, allows runtime flexibility via configuration—best practice.
Conclusion: D is correct.
OCI documentation states: “For Jobs with varying hyperparameters, write code to accept environment variables or command-line arguments (D), then configure these per Job Run using the OCI Console or SDK—most efficient approach.” Options A, B, and C involve unnecessary job proliferation or code changes—only D aligns with OCI’s design for parameterized runs.
1: Oracle Cloud Infrastructure Data Science Documentation, "Configuring Job Runs with Parameters".
問題 #121
Which is NOT a part of Observability and Management Services?
答案：C
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 #122
What is feature engineering in machine learning used for?
答案：A
解題說明：
Detailed Answer in Step-by-Step Solution:
Define Feature Engineering: It’s the process of creating or modifying features to improve model performance.
Evaluate Options:
A: Parameter tuning adjusts model hyperparameters (e.g., learning rate), not features.
B: Model interpretation (e.g., SHAP values) explains predictions, not feature creation.
C: Transforming features (e.g., normalizing, encoding) is the core of feature engineering—correct.
D: Understanding features occurs during exploration, not engineering.
Reasoning: Feature engineering directly manipulates data inputs (e.g., converting timestamps to day-of-week), distinct from tuning or interpretation.
Conclusion: C is the precise definition.
OCI Data Science documentation defines feature engineering as “the process of transforming raw data into features that better represent the underlying problem to the predictive models, resulting in improved model accuracy.” Examples include scaling or creating interaction terms, aligning 1with C. Other options (A, B, D) relate to different ML stages.
1: Oracle Cloud Infrastructure Data Science Documentation, "Feature Engineering Overview".
問題 #123
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