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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Machine Learning Fundamentals | 20% | - Unsupervised learning
|
| Topic 2: OCI Data Science Service | 30% | - Projects and notebooks
|
| Topic 3: MLOps and OCI Integration | 20% | - Automation and pipelines
|
| Topic 4: Model Development and Deployment | 30% | - Model training
|
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NEW QUESTION # 67
Which stage in the machine learning life cycle helps in identifying the imbalance present in the data?
Answer: B
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Find the stage where data imbalance (e.g., skewed classes) is identified.
Understand Stages:
Data Modeling: Training models—assumes data is prepared.
Data Monitoring: Post-deployment tracking—not for initial analysis.
Data Exploration: Analyzing data properties (e.g., distributions)—key for imbalance.
Data Access: Retrieving data—no analysis yet.
Evaluate Options:
A: Modeling uses data, doesn’t detect imbalance—incorrect.
B: Monitoring tracks performance, not initial data issues—incorrect.
C: Exploration (e.g., via pandas) reveals imbalances—correct.
D: Access is just retrieval—incorrect.
Reasoning: Imbalance is assessed during exploration (e.g., class counts).
Conclusion: C is correct.
OCI documentation notes: “Data Exploration involves analyzing the dataset to understand its characteristics, such as identifying class imbalances or missing values, using tools like ADS SDK or Jupyter notebooks.” Modeling (A) and Monitoring (B) occur later, while Access (D) is pre-analysis—only Exploration (C) fits this role.
1: Oracle Cloud Infrastructure Data Science Documentation, "Data Exploration Stage".
NEW QUESTION # 68
Which is NOT a part of Observability and Management Services?
Answer: D
Explanation:
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".
NEW QUESTION # 69
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,C,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 # 70
Which of the following analytical and statistical techniques do data scientists commonly use?
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify common data science techniques.
Define Techniques:
Classification: Predicts categories (e.g., spam vs. not).
Regression: Predicts continuous values (e.g., sales).
Clustering: Groups data (e.g., customer segments).
Evaluate Options:
A, B, C: All are standard ML/statistical methods.
D: Encompasses all—correct as they’re widely used.
Reasoning: These are foundational in data science workflows.
Conclusion: D is correct.
OCI documentation lists “classification, regression, and clustering as core techniques in data science, supported by tools like ADS SDK and AutoML.” All (D) are common per OCI’s ML framework, not just subsets (A, B, C).
1: Oracle Cloud Infrastructure Data Science Documentation, "Analytical Techniques".
NEW QUESTION # 71
What is the first step in the data science process?
Answer: C
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify the initial data science step.
Define Process: Starts with problem definition, then data and modeling.
Evaluate Options:
A: Data collection—Second step after problem definition.
B: Modeling—Later stage.
C: Hypothesis—Sets the goal, first step—correct.
D: Data owners—Collaboration, not the start.
Reasoning: Hypothesis drives the process (e.g., “Can we predict churn?”).
Conclusion: C is correct.
OCI documentation states: “The data science process begins with defining an analytical hypothesis to address a business problem, followed by data collection and analysis.” C precedes A, B, and D—aligning with OCI’s structured approach.
1: Oracle Cloud Infrastructure Data Science Documentation, "Data Science Process".
NEW QUESTION # 72
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