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| Section | Objectives |
|---|---|
| Apply MLOps Practices | - Use best practices for operationalizing ML solutions - Implement model lifecycle management - Monitor and maintain machine learning models |
| 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 |
| Implement End-to-End Machine Learning Lifecycle | - Prepare and manage datasets - Save and manage models using Model Catalog - Automate machine learning workflows and pipelines - Deploy models and consume model endpoints - Build, train, and evaluate machine learning models |
| Use Related OCI Services | - Integrate OCI Data and AI services - Design machine learning solutions for business use cases - Apply OCI services for data ingestion, storage, and processing |
| OCI Data Science - Introduction and Configuration | - Use OCI Data Science notebooks and sessions - Understand OCI Data Science service concepts and architecture - Configure and manage Data Science resources |
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NEW QUESTION # 153
You have built a machine model to predict whether a bank customer is going to default on a loan. You want to use Local Interpretable Model-Agnostic Explanations (LIME) to understand a specific prediction. What is the key idea behind LIME?
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Define LIME’s core concept.
Understand LIME: Explains individual predictions with local surrogate models.
Evaluate Options:
A: Complex global, simple local—Correct LIME principle.
B: Agnosticism—True but not the key idea.
C: Global/local similarity—False.
D: Local vs. global agnosticism—Incorrect distinction.
Reasoning: A captures LIME’s local approximation focus.
Conclusion: A is correct.
OCI documentation notes: “LIME (A) explains predictions by approximating complex global models with simpler local surrogate models around specific instances.” B, C, and D misalign—only A reflects LIME’s foundational idea per OCI’s interpretability tools.
1: Oracle Cloud Infrastructure Data Science Documentation, "Model Interpretability - LIME".
NEW QUESTION # 154
Which technique can be used for feature engineering in the machine learning lifecycle?
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify a feature engineering technique in ML.
Understand Feature Engineering: Transforms raw data into model-ready features.
Evaluate Options:
A . PCA: Reduces dimensionality—feature engineering—correct.
B . K-means: Clustering model—not feature engineering.
C . SVM: Classification model—not feature engineering.
D . Gradient boosting: Model training—not feature engineering.
Reasoning: PCA creates new features via transformation—fits definition.
Conclusion: A is correct.
OCI documentation states: “Feature engineering techniques like Principal Component Analysis (PCA) (A) transform data into new features to enhance model performance.” B, C, and D are modeling techniques—only A aligns with OCI’s feature engineering stage.
1: Oracle Cloud Infrastructure Data Science Documentation, "Feature Engineering Techniques".
NEW QUESTION # 155
True or false? Data scientists typically need a combination of technical skills, nontechnical ones, and suitable personality traits to be successful.
Answer: B
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Assess required skills for data scientists.
Analyze Skills:
Technical: Coding, stats, ML.
Nontechnical: Communication, business acumen.
Traits: Curiosity, problem-solving.
Reasoning: Success requires this mix—e.g., explaining models to stakeholders.
Conclusion: A (True) is correct.
OCI documentation states: “Effective data scientists combine technical skills (e.g., Python), nontechnical skills (e.g., storytelling), and traits like analytical thinking.” This holistic requirement is true (A), not false (B).
1: Oracle Cloud Infrastructure Data Science Documentation, "Data Scientist Skills".
NEW QUESTION # 156
You realize that your model deployment is about to reach its utilization limit. What would you do to avoid the issue before requests start to fail? Which THREE steps would you perform?
Answer: B,C,D
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Prevent deployment failure due to utilization limits.
Understand Utilization: High load requires capacity or throttling.
Evaluate Options:
A: More instances—Scales horizontally—correct.
B: Delete—Stops service, not a fix—incorrect.
C: Fewer instances—Worsens issue—incorrect.
D: Larger VM—Scales vertically—correct.
E: Reduce bandwidth—Controls load—correct.
Reasoning: A and D increase capacity, E manages demand—effective trio.
Conclusion: A, D, E are correct.
OCI documentation states: “To avoid utilization limits, increase instances (A), use a larger compute shape (D), or reduce load balancer bandwidth (E) to manage request rates.” B stops service, C reduces capacity—only A, D, E align with OCI’s deployment scaling options.
1: Oracle Cloud Infrastructure Data Science Documentation, "Model Deployment Scaling".
NEW QUESTION # 157
What is a common maxim about data scientists?
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify a widely accepted maxim about data scientists’ time allocation.
Understand Data Science Workflow: Involves data collection, preparation, and analysis—time distribution is key.
Evaluate Options:
A: 80% on finding/preparing, 20% analyzing—Reflects the data wrangling challenge.
B: 80% analyzing, 20% finding/preparing—Inverts the common perception.
C: 80% on failed projects, 20% useful—Pessimistic, not a standard maxim.
Reasoning: Industry consensus (e.g., “80/20 rule”) emphasizes data prep as the bulk of effort due to messy real-world data.
Conclusion: A is correct.
OCI Data Science documentation aligns with industry norms: “Data scientists typically spend 80% of their time finding, cleaning, and preparing data, and 20% on analysis and modeling, due to the complexity of raw data.” B reverses this, and C isn’t supported—only A reflects this widely cited maxim from sources like Forbes and OCI’s practical guidance.
1: Oracle Cloud Infrastructure Data Science Documentation, "Data Science WorkflowOverview".
NEW QUESTION # 158
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