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Oracle Cloud Infrastructure 2025 Data Science Professional Sample Questions (Q107-Q112):
NEW QUESTION # 107
Which stage in the machine learning life cycle helps in identifying the imbalance present in the data?
Answer: A
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.
Oracle Cloud Infrastructure Data Science Documentation, "Data Exploration Stage".
NEW QUESTION # 108
You are a data scientist using Oracle AutoML to produce a model and you are evaluating the score metric for the model. Which of the following TWO prevailing metrics would you use for evaluating a multiclass classification model?
Answer: C,E
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Select two metrics for multiclass classification in AutoML.
* Understand Multiclass Metrics: Focus on class-specific performance-classification, not regression.
* Evaluate Options:
* A. Recall: Measures true positives per class-key for multiclass-correct.
* B. Mean squared error: Regression metric-incorrect.
* C. F1 Score: Balances precision and recall-standard for multiclass-correct.
* D. R-Squared: Regression fit-incorrect.
* E. Explained variance: Regression metric-incorrect.
* Reasoning: A and C assess classification accuracy across multiple classes-fit AutoML's evaluation.
* Conclusion: A and C are correct.
OCI AutoML documentation states: "For multiclass classification, common evaluation metrics include recall (A) for per-class sensitivity and F1 Score (C) for balanced performance." B, D, and E are regression- focused-only A and C are supported and relevant per OCI's AutoML metrics suite.
Oracle Cloud Infrastructure AutoML Documentation, "Evaluation Metrics for Classification".
NEW QUESTION # 109
Which statement about resource principals is true?
Answer: B
Explanation:
Detailed Answer in Step-by-Step Solution:
* Define Resource Principals: They allow OCI resources (e.g., notebook sessions) to authenticate to other OCI services without user credentials.
* Evaluate Options:
* A: False-Resource principals eliminate manual credential management.
* B: False-They're secure, leveraging IAM policies, not less secure than API keys.
* C: False-Data Science supports resource principals for accessing resources (e.g., Object Storage).
* D: True-Resource principals are an IAM feature authorizing resources as actors.
* Reasoning: D captures the essence of resource principals as an IAM mechanism.
* Conclusion: D is correct.
OCI documentation states: "A resource principal is an IAM feature that enables OCI resources, such as compute instances or notebook sessions, to act as principal actors and authenticate to other OCI services using policies." This refutes A (no credentials needed), B (secure method), and C (supported in Data Science), making D the accurate statement.
Oracle Cloud Infrastructure IAM Documentation, "Resource Principals".
NEW QUESTION # 110
You are a data scientist working for a utilities company. You have developed an algorithm that detects anomalies from a utility reader in the grid. The size of the model artifact is about 2 GB, and you are trying to store it in the model catalog. Which THREE interfaces could you use to save the model artifact into the model catalog?
Answer: B,C,D
Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify interfaces to save a 2 GB model to the Model Catalog.
* Evaluate Options:
* A: OCI CLI-Supports Data Science tasks-possible but not primary.
* B: ADS SDK-Designed for model catalog ops-correct.
* C: ODSC CLI-Not standard; likely typo for OCI CLI.
* D: Console-GUI for catalog uploads-correct.
* E: OCI Python SDK-Programmatic catalog access-correct.
* F: Git CLI-Version control, not catalog-related.
* Reasoning: B, D, E are OCI's primary interfaces; A is valid but less emphasized.
* Conclusion: B, D, E are correct (A plausible but not top-tier).
OCI documentation lists "ADS SDK (B), OCI Console (D), and OCI Python SDK (E) as primary methods to save models to the Model Catalog." OCI CLI (A) works but isn't highlighted, C isn't real, and F is unrelated- B, D, E are the standard trio.
Oracle Cloud Infrastructure Data Science Documentation, "Model Catalog Interfaces".
NEW QUESTION # 111
While working with Git on Oracle Cloud Infrastructure (OCI) Data Science, you notice that two of the operations are taking more time than the others due to your slow internet speed. Which TWO operations would experience the delay?
Answer: B,E
Explanation:
Detailed Answer in Step-by-Step Solution:
* Analyze Git Operations: Identify which depend on internet speed.
* Evaluate Options:
* A. Staging (git add): Local operation-adds files to the index; no network involved.
* B. Updating local repo (git pull): Downloads remote changes-requires internet, slowed by poor connectivity.
* C. Pushing changes (git push): Uploads local commits to remote-network-dependent, delayed by slow speed.
* D. Committing (git commit): Local snapshot-no network needed.
* E. Converting to Git repo (git init): Local initialization-no internet required.
* Reasoning: Only B and C involve network transfers, directly impacted by slow internet.
* Conclusion: B and C are the correct choices.
Git operations like git pull (B) and git push (C) rely on network communication with a remote repository, such as OCI Code Repository, and are documented as "bandwidth-sensitive" in OCI's guides. Local actions like staging (A), committing (D), and initializing (E) occur on the user's machine, unaffected by internet speed. This matches standard Git behavior and OCI's implementation.
Oracle Cloud Infrastructure Data Science Documentation, "Using Git in Notebook Sessions".
NEW QUESTION # 112
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