100% PASS QUIZ 1Z0-1110-25 - THE BEST ORACLE CLOUD INFRASTRUCTURE 2025 DATA SCIENCE PROFESSIONAL RELIABLE STUDY QUESTIONS

100% Pass Quiz 1z0-1110-25 - The Best Oracle Cloud Infrastructure 2025 Data Science Professional Reliable Study Questions

100% Pass Quiz 1z0-1110-25 - The Best Oracle Cloud Infrastructure 2025 Data Science Professional Reliable Study Questions

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Oracle Cloud Infrastructure 2025 Data Science Professional Sample Questions (Q85-Q90):

NEW QUESTION # 85
You have configured the Management Agent on an Oracle Cloud Infrastructure (OCI) Linux instance for log ingestion purposes. Which is a required configuration for OCI Logging Analytics service to collect data from multiple logs of this instance?

  • A. Source - Entity Association
  • B. Entity - Log Association
  • C. Log - Log Group Association
  • D. Log Group - Source Association

Answer: A

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify the required configuration for OCI Logging Analytics to collect logs from an instance.
* Understand Logging Analytics: Collects and analyzes logs from OCI resources via Management Agents.
* Key Concepts:
* Entity: Represents the instance (e.g., Linux VM).
* Source: Defines log locations (e.g., file paths).
* Log Group: Organizes logs for analysis.
* Evaluate Options:
* A: Log-Log Group-Groups logs, not collection setup.
* B: Entity-Log-Links instance to logs, but not source-specific.
* C: Source-Entity-Maps log sources to the instance-correct.
* D: Log Group-Source-Post-collection organization, not ingestion.
* Reasoning: C establishes the link between the instance and its log sources-key for ingestion.
* Conclusion: C is correct.
OCI documentation states: "To collect logs using Logging Analytics, configure a Source-Entity Association (C) to link the Management Agent on the instance (entity) to specific log sources (e.g., file paths)." A and D organize logs post-collection, B is less specific-only C is required for ingestion per OCI's Logging Analytics setup.
Oracle Cloud Infrastructure Logging Analytics Documentation, "Configuring Log Collection".


NEW QUESTION # 86
You have just received a new dataset from a colleague. You want to quickly find out summary information about the dataset, such as the types of features, the total number of observations, and distributions of the data.
Which Accelerated Data Science (ADS) SDK method from the ADSDataset class would you use?

  • A. compute()
  • B. show_in_notebook()
  • C. show_corr()
  • D. to_xgb()

Answer: B

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Get summary info from an ADSDataset object.
* Evaluate Options:
* A: Correlation matrix-Specific, not full summary.
* B: Converts to XGBoost-Not for summary.
* C: Executes computation-Not summary-focused.
* D: Displays summary (types, counts, dist)-correct.
* Reasoning: show_in_notebook() provides a comprehensive overview.
* Conclusion: D is correct.
OCI documentation states: "show_in_notebook() (D) from ADSDataset displays a summary of the dataset, including feature types, observation count, and distributions, in a notebook." A is partial, B and C are unrelated-only D meets the need per ADS SDK.
Oracle Cloud Infrastructure ADS SDK Documentation, "ADSDataset Methods".


NEW QUESTION # 87
You are preparing a configuration object necessary to create a Data Flow application. Which THREE parameter values should you provide?

  • A. The display name of the application
  • B. The local path to your PySpark script
  • C. The path to the archive.zip file
  • D. The compartment of the Data Flow application
  • E. The bucket used to read/write the PySpark script in Object Storage

Answer: A,D,E

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify three required params for an OCI Data Flow app config.
* Understand Data Flow: Runs Spark apps; needs compartment, storage, and identity.
* Evaluate Options:
* A: Archive path-Optional if script is in Object Storage-incorrect.
* B: Local script path-Not needed; script is uploaded-incorrect.
* C: Compartment-Required for resource scope-correct.
* D: Bucket-Required for script storage/access-correct.
* E: Display name-Required for app identification-correct.
* Reasoning: C, D, E are mandatory metadata for Data Flow creation-script location is specified via bucket.
* Conclusion: C, D, E are correct.
OCI documentation states: "To create a Data Flow application, configure the compartment OCID (C), Object Storage bucket for the PySpark script (D), and a display name (E) in the application object." Local paths (B) or archives (A) are optional or handled separately-only C, D, E are required per OCI's Data Flow API spec.
Oracle Cloud Infrastructure Data Flow Documentation, "Creating Applications".


NEW QUESTION # 88
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?

  • A. Global and local behaviours of machine learning models are similar
  • B. Local explanation techniques are model-agnostic, while global explanation techniques are not
  • C. Model-agnostic techniques are more interpretable than techniques that are dependent on the types of models
  • D. Global behaviour of a machine learning model may be complex, while the local behaviour may be approximated with a simpler surrogate model

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.
Oracle Cloud Infrastructure Data Science Documentation, "Model Interpretability - LIME".


NEW QUESTION # 89
Six months ago you created and deployed a model that predicts customer churn for a call center. Initially, it was yielding quality predictions. However, over the last two months, users have been questioning the credibility of the predictions. Which TWO methods would you employ to verify accuracy and lower customer churn?

  • A. Validate the model using recent data
  • B. Drift monitoring
  • C. Retrain the model
  • D. Redeploy the model
  • E. Operational monitoring

Answer: B,C

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Address declining model performance and improve churn prediction.
* Analyze Issue: Poor predictions suggest data drift or model staleness-common ML challenges.
* Evaluate Options:
* A. Drift monitoring: Tracks changes in data distribution-identifies root cause of accuracy drop- correct.
* B. Redeploy the model: Repeats deployment without fixing the issue-ineffective alone.
* C. Operational monitoring: Tracks system health (e.g., latency), not prediction quality.
* D. Retrain the model: Updates model with new data-directly improves accuracy-correct.
* E. Validate with recent data: Checks performance but doesn't fix-diagnostic, not corrective.
* Reasoning: A diagnoses drift (cause), D retrains to adapt (solution)-best combo to verify and lower churn.
* Conclusion: A and D are correct.
OCI documentation advises: "Drift monitoring (A) detects shifts in data distribution that degrade model performance, while retraining (D) with fresh data restores accuracy." Redeployment (B) doesn't address drift, operational monitoring (C) focuses on infra, and validation (E) is a check-not a fix. A and D align with OCI' s model maintenance strategy.
Oracle Cloud Infrastructure Data Science Documentation, "Model Monitoring and Retraining".


NEW QUESTION # 90
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