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| Section | Objectives |
|---|---|
| Topic 1: 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 |
| Topic 2: 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 |
| Topic 3: 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 |
| Topic 4: Apply MLOps Practices | - Monitor and maintain machine learning models - Use best practices for operationalizing ML solutions - Implement model lifecycle management |
| Topic 5: Implement End-to-End Machine Learning Lifecycle | - Deploy models and consume model endpoints - Prepare and manage datasets - Save and manage models using Model Catalog - Automate machine learning workflows and pipelines - Build, train, and evaluate machine learning models |
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NEW QUESTION # 70
True or false? Bias is a common problem in data science applications.
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Assess if bias is a common issue in data science.
Define Bias: Systematic errors in data/models (e.g., skewed training data).
Evaluate Statement:
Bias arises from unrepresentative data, poor feature selection, or algorithmic flaws—widely recognized in ML.
Examples: Gender bias in hiring models, racial bias in facial recognition.
Reasoning: Literature and practice (e.g., fairness in AI) confirm bias as prevalent.
Conclusion: A (True) is correct.
OCI documentation notes: “Bias is a common challenge in data science, stemming from imbalanced datasets or flawed assumptions, requiring techniques like re-weighting or fairness checks.” This aligns with industry standards—bias is a well-documented issue, making A true.
1: Oracle Cloud Infrastructure Data Science Documentation, "Addressing Bias in Models".
NEW QUESTION # 71
As a data scientist, you are trying to automate a machine learning (ML) workflow and have decided to use Oracle Cloud Infrastructure (OCI) AutoML Pipeline. Which THREE are part of the AutoML Pipeline?
Answer: A,D,E
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify three stages in OCI AutoML Pipeline.
Understand Pipeline: Automates ML steps from data to model training.
Evaluate Options:
A: Feature Selection—Selects relevant features—correct.
B: Adaptive Sampling—Reduces data size—correct.
C: Model Deployment—Post-pipeline step—incorrect.
D: Feature Extraction—Not explicit in OCI AutoML—incorrect.
E: Algorithm Selection—Chooses best model—correct.
Reasoning: A, B, E are core automated stages; C and D are separate.
Conclusion: A, B, E are correct.
OCI documentation lists “AutoML Pipeline stages as adaptive sampling (B), feature selection (A), algorithm selection (E), and hyperparameter tuning.” Deployment (C) is post-pipeline, and extraction (D) isn’t highlighted—only A, B, E are included per OCI’s design.
1: Oracle Cloud Infrastructure AutoML Documentation, "Pipeline Components".
NEW QUESTION # 72
Which statement about logs for Oracle Cloud Infrastructure Jobs is true?
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify a true statement about OCI Jobs logging.
Understand Logging: Jobs can log stdout/stderr to OCI Logging service.
Evaluate Options:
A: False—Each run has its own log, not a single job log.
B: False—Logging is optional, not mandatory.
C: True—When enabled, stdout/stderr are auto-captured.
D: False—Logs persist unless explicitly deleted.
Reasoning: C matches OCI’s automatic logging feature.
Conclusion: C is correct.
OCI documentation states: “When automatic log creation is enabled for Data Science Jobs, all stdout and stderr outputs are captured and stored in the OCI Logging service.” A is incorrect (per-run logs), B is optional, and D contradicts log retention—only C is accurate.
1: Oracle Cloud Infrastructure Data Science Documentation, "Jobs Logging".
NEW QUESTION # 73
On which option do you set Oracle Cloud Infrastructure Budget?
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Determine where OCI budgets are set.
Understand Budgets: Track spending across OCI resources.
Evaluate Options:
A: Compartments—Scoped within tenancy, not budget root.
B: Instances—Specific resources, not budget scope.
C: Tags—Filter costs, not budget setting.
D: Tenancy—Top-level scope for budgets—correct.
Reasoning: Budgets apply at tenancy, optionally filtered (e.g., by compartment).
Conclusion: D is correct.
OCI documentation states: “Budgets are set at the tenancy level (D), with optional filters like compartments or tags to monitor spending.” A, B, and C are sub-elements—only D is the primary scope per OCI’s cost management.
1: Oracle Cloud Infrastructure Cost Management Documentation, "Setting Budgets".
NEW QUESTION # 74
How are datasets exported in the OCI Data Labeling service?
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
Understand OCI Data Labeling Export: After annotation, datasets are exported for ML use.
Check Supported Formats: OCI Data Labeling exports annotations in a structured, machine-readable format.
Evaluate Options:
A: Binary isn’t a standard export format for annotations.
B: XML isn’t used; JSON is preferred for flexibility.
C: Line-delimited JSON is the correct format, aligning with ML workflows.
D: CSV is common but not the default for OCI Data Labeling.
Conclusion: C matches the official export format.
OCI Data Labeling exports annotated datasets as line-delimited JSON files, which store each annotation as a separate JSON object per line, suitable for ML pipelines. This is explicitly stated in the documentation. (Oracle Cloud Infrastructure Data Labeling Service Documentation, "Exporting Datasets").
NEW QUESTION # 75
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