1Z0-1110-25 VALID EXAM TIPS | NEW 1Z0-1110-25 EXAM BOOK

1z0-1110-25 Valid Exam Tips | New 1z0-1110-25 Exam Book

1z0-1110-25 Valid Exam Tips | New 1z0-1110-25 Exam Book

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Oracle Cloud Infrastructure 2025 Data Science Professional Sample Questions (Q67-Q72):

NEW QUESTION # 67
You are working as a data scientist for a healthcare company. They decided to analyze the data to find patterns in a large volume of electronic medical records. You are asked to build a PySpark solution to analyze these records in a JupyterLab notebook. What is the order of recommended steps to develop a PySpark application in OCI Data Science?

  • A. Configure core-site.xml, install a PySpark conda environment, create a Data Flow application with the Accelerated Data Science (ADS) SDK, develop your PySpark application, launch a notebook session
  • B. Install a Spark conda environment, configure core-site.xml, launch a notebook session, create a Data Flow application with the Accelerated Data Science (ADS) SDK, develop your PySpark application
  • C. Launch a notebook session, install a PySpark conda environment, configure core-site.xml, develop your PySpark application, create a Data Flow application with the Accelerated Data Science (ADS) SDK
  • D. Launch a notebook session, configure core-site.xml, install a PySpark conda environment, develop your PySpark application, create a Data Flow application with the Accelerated Data Science (ADS) SDK

Answer: C

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Sequence steps for a PySpark app in OCI Data Science.
* Evaluate Steps:
* Launch notebook: First-provides the environment.
* Install PySpark conda: Second-sets up Spark libraries.
* Configure core-site.xml: Third-connects to data (e.g., Object Storage).
* Develop app: Fourth-writes the PySpark code.
* Data Flow: Fifth-optional scaling, post-development.
* Check Options: D (1, 2, 3, 4, 5) matches this logical flow.
* Reasoning: Notebook first, then setup, coding, and scaling.
* Conclusion: D is correct.
OCI documentation recommends: "1) Launch a notebook session, 2) install a PySpark conda environment, 3) configure core-site.xml for data access, 4) develop your PySpark application, and 5) optionally use Data Flow for scale." D follows this-others (A, B, C) misorder critical steps like launching the notebook.
Oracle Cloud Infrastructure Data Science Documentation, "PySpark in Notebooks".


NEW QUESTION # 68
Which of the following analytical and statistical techniques do data scientists commonly use?

  • A. Clustering
  • B. Regression
  • C. Classification
  • D. All of the above

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).
Oracle Cloud Infrastructure Data Science Documentation, "Analytical Techniques".


NEW QUESTION # 69
Which is NOT a valid OCI Data Science notebook session approach?

  • A. Authenticate using your notebook session's resource principal to access other OCI resources. Resource principals provide a more secure way to authenticate to resources compared to the OCI configuration and API approach
  • B. Avoid having multiple users in the same notebook session due to the possibility of resource contention and write conflicts
  • C. While connecting to data in OCI Object Storage from your notebook session, the best practice is to make a local copy on the device and then upload it to your notebook session block volume
  • D. Ensure you don't execute long-running Python processes in a notebook cell
  • E. Run the process directly in the terminal and use Python logging to get updates on the progress of your job

Answer: C

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify an invalid notebook session practice.
* Evaluate Options:
* A: Valid-Long runs should use Jobs, not notebooks.
* B: Valid-Terminal runs with logging are supported.
* C: Valid-Multi-user conflicts are a concern.
* D: Invalid-Copying from Object Storage to block volume is unnecessary; direct access is best.
* E: Valid-Resource principals are secure and recommended.
* Reasoning: D contradicts OCI's direct-access efficiency.
* Conclusion: D is incorrect.
OCI documentation advises: "Access data in Object Storage directly from notebook sessions using SDKs or resource principals-avoid local copies (D) unless necessary." A, B, C, and E are best practices-D is inefficient and not standard.
Oracle Cloud Infrastructure Data Science Documentation, "Notebook Session Best Practices".


NEW QUESTION # 70
You want to evaluate the relationship between feature values and target variables. You have a large number of observations having a near uniform distribution and the features are highly correlated. Which model explanation technique should you choose?

  • A. Accumulated Local Effects
  • B. Feature Permutation Importance Explanations
  • C. Feature Dependence Explanations
  • D. Local Interpretable Model-Agnostic Explanations

Answer: A

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Select an explanation technique for feature-target relationships with correlated features.
* Evaluate Options:
* A: Permutation-Breaks with high correlation.
* B: LIME-Local, not global relationships.
* C: Dependence-Not a standard term; vague.
* D: ALE-Handles correlation, shows feature effects-correct.
* Reasoning: ALE is robust to correlated features, ideal here.
* Conclusion: D is correct.
OCI documentation states: "Accumulated Local Effects (ALE) (D) evaluates feature-target relationships, accounting for correlations, unlike permutation importance (A) which falters with high correlation." B is local, C isn't defined-only D fits per OCI's explanation tools.
Oracle Cloud Infrastructure Data Science Documentation, "Model Explanation Techniques".


NEW QUESTION # 71
What is a conda environment?

  • A. An open-source environment management system
  • B. An environment deployment system on Oracle AI
  • C. A collection of kernels
  • D. A system that manages package dependencies

Answer: A

Explanation:
Detailed Answer in Step-by-Step Solution:
* Define Conda: Conda is a widely used tool for managing packages and environments in data science.
* Evaluate Options:
* A: Partially true-Conda manages dependencies, but it's broader (an environment system).
* B: Incorrect-Kernels (e.g., Jupyter) are separate; Conda manages environments.
* C: Correct-Conda is an open-source tool for creating isolated environments with specific packages.
* D: Incorrect-Not specific to Oracle AI; it's a general tool.
* Reasoning: C captures Conda's full scope as an open-source system, beyond just dependency management (A).
* Conclusion: C is the most accurate.
OCI documentation describes Conda as "an open-source package and environment management system that allows data scientists to create isolated environments with specific versions of Python and libraries." A is too narrow, B misaligns with kernel concepts, and D ties it incorrectly to Oracle AI. C aligns with Conda's official definition and OCI's usage.
Oracle Cloud Infrastructure Data Science Documentation, "Conda Environments Overview".


NEW QUESTION # 72
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