QUIZ 2025 1Z0-1110-25: UPDATED LATEST ORACLE CLOUD INFRASTRUCTURE 2025 DATA SCIENCE PROFESSIONAL MOCK TEST

Quiz 2025 1z0-1110-25: Updated Latest Oracle Cloud Infrastructure 2025 Data Science Professional Mock Test

Quiz 2025 1z0-1110-25: Updated Latest Oracle Cloud Infrastructure 2025 Data Science Professional Mock Test

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Oracle 1z0-1110-25 Exam Syllabus Topics:

TopicDetails
Topic 1
  • Implement End-to-End Machine Learning Lifecycle: This section evaluates the abilities of Machine Learning Engineers and includes an end-to-end walkthrough of the ML lifecycle within OCI. It involves data acquisition from various sources, data preparation, visualization, profiling, model building with open-source libraries, Oracle AutoML, model evaluation, interpretability with global and local explanations, and deployment using the model catalog.
Topic 2
  • Use Related OCI Services: This final section measures the competence of Machine Learning Engineers in utilizing OCI-integrated services to enhance data science capabilities. It includes creating Spark applications through OCI Data Flow, utilizing the OCI Open Data Service, and integrating other tools to optimize data handling and model execution workflows.
Topic 3
  • OCI Data Science - Introduction & Configuration: This section of the exam measures the skills of Machine Learning Engineers and covers foundational concepts of Oracle Cloud Infrastructure (OCI) Data Science. It includes an overview of the platform, its architecture, and the capabilities offered by the Accelerated Data Science (ADS) SDK. It also addresses the initial configuration of tenancy and workspace setup to begin data science operations in OCI.
Topic 4
  • Apply MLOps Practices: This domain targets the skills of Cloud Data Scientists and focuses on applying MLOps within the OCI ecosystem. It covers the architecture of OCI MLOps, managing custom jobs, leveraging autoscaling for deployed models, monitoring, logging, and automating ML workflows using pipelines to ensure scalable and production-ready deployments.
Topic 5
  • Create and Manage Projects and Notebook Sessions: This part assesses the skills of Cloud Data Scientists and focuses on setting up and managing projects and notebook sessions within OCI Data Science. It also covers managing Conda environments, integrating OCI Vault for credentials, using Git-based repositories for source code control, and organizing your development environment to support streamlined collaboration and reproducibility.

Oracle Cloud Infrastructure 2025 Data Science Professional Sample Questions (Q62-Q67):

NEW QUESTION # 62
You are using Oracle Cloud Infrastructure (OCI) Anomaly Detection to train a model to detect anomalies in pump sensor data. How does the required False Alarm Probability setting affect an anomaly detection model?

  • A. It changes the sensitivity of the model to detecting anomalies
  • B. It determines how many false alarms occur before an error message is generated
  • C. It is used to disable the reporting of false alarms
  • D. It adds a score to each signal indicating the probability that it's a false alarm

Answer: A

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Understand the effect of False Alarm Probability (FAP) in OCI Anomaly Detection.
* Understand FAP: Controls false positive rate-threshold for anomaly flagging.
* Evaluate Options:
* A: Disable reporting-Incorrect; FAP sets sensitivity, not on/off.
* B: Changes sensitivity-Correct; lower FAP = fewer false positives-correct.
* C: Count-based error-Incorrect; not a counter.
* D: Score per signal-Incorrect; FAP is a global setting.
* Reasoning: FAP adjusts detection threshold-direct impact on sensitivity.
* Conclusion: B is correct.
OCI documentation states: "False Alarm Probability (FAP) (B) adjusts the model's sensitivity in Anomaly Detection-lower values increase specificity, reducing false positives." A, C, and D misinterpret FAP's role- only B aligns with OCI's anomaly detection tuning.
Oracle Cloud Infrastructure Anomaly Detection Documentation, "FAP Settings".


NEW QUESTION # 63
What is a conda environment?

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

Answer: C

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 # 64
What happens when a notebook session is deactivated?

  • A. The underlying compute instance stops
  • B. The block volume attached to the notebook is permanently deleted
  • C. Compute cost increases due to frequent deactivation
  • D. The data on boot volume is preserved

Answer: A

Explanation:
Detailed Answer in Step-by-Step Solution:
* Understand Notebook Sessions: These are OCI compute instances running JupyterLab.
* Deactivation Impact: Deactivating stops the session to save costs.
* Evaluate Options:
* A: False-Costs decrease as compute stops.
* B: False-Boot volume data isn't preserved; block volume data is.
* C: True-The compute instance shuts down, halting billing.
* D: False-Block volume persists unless explicitly deleted.
* Reasoning: Deactivation stops the instance (C), preserving block volume data separately.
* Conclusion: C is correct.
The OCI documentation states: "When a notebook session is deactivated, the underlying compute instance stops, and billing for compute resources ceases. Data on the attached block volume is preserved, but the boot volume is not." A is backwards, B misattributes preservation, and D overstates deletion-only C aligns with the process.
Oracle Cloud Infrastructure Data Science Documentation, "Notebook Session Lifecycle".


NEW QUESTION # 65
Which of the following best describes the principal goal of data science?

  • A. To collect and prepare data for use as part of analytics applications.
  • B. To collect and archive exhaustive datasets from various source systems for corporate record-keeping uses.
  • C. Data science is focused on output of the analysis.
  • D. To mine and analyze large amounts of data in order to uncover information that can be used for operational improvements and business gains.

Answer: D

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Define data science's main goal.
* Evaluate Options:
* A: Archiving-Not the focus; too narrow.
* B: Analyze for insights/business value-Core purpose-correct.
* C: Prep for analytics-Means, not the end goal.
* D: Output-focused-Vague, incomplete.
* Reasoning: B captures the actionable insight generation central to data science.
* Conclusion: B is correct.
OCI documentation defines data science as "mining and analyzing large datasets to uncoveractionable insights for operational improvements and business value." A is storage-focused, C is preparatory, and D is unclear-only B reflects the principal goal per OCI's mission.
Oracle Cloud Infrastructure Data Science Documentation, "What is Data Science?".


NEW QUESTION # 66
You want to make your model more parsimonious to reduce the cost of collecting and processing data. You plan to do this by removing features that are highly correlated. You would like to create a heatmap that displays the correlation so that you can identify candidate features to remove. Which Accelerated Data Science (ADS) SDK method would be appropriate to display the correlation between Continuous and Categorical features?

  • A. corr()
  • B. cramersv_plot()
  • C. correlation_ratio_plot()
  • D. pearson_plot()

Answer: C

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Visualize correlation between continuous and categorical features using ADS SDK.
* Understand Correlation Types:
* Continuous vs. Continuous: Pearson correlation.
* Categorical vs. Categorical: Cramer's V.
* Continuous vs. Categorical: Correlation ratio (eta).
* Evaluate Options:
* A. corr(): General correlation (Pearson), not suited for mixed types-incorrect.
* B. correlation_ratio_plot(): Plots correlation ratio for continuous-categorical-correct.
* C. pearson_plot(): Not an ADS method; Pearson is continuous-only-incorrect.
* D. cramersv_plot(): Cramer's V for categorical-categorical-incorrect.
* Reasoning: Correlation ratio measures association between continuous and categorical variables-ideal for heatmap in this mixed scenario.
* Conclusion: B is correct.
OCI documentation states: "The correlation_ratio_plot() method (B) in ADS SDK generates a heatmap displaying the correlation ratio between continuous and categorical features, suitable for identifying highly correlated features for removal." corr() (A) defaults to Pearson, pearson_plot() (C) isn't real, and cramersv_plot() (D) is for categorical pairs-only B aligns with OCI's ADS capabilities for this use case.
Oracle Cloud Infrastructure ADS SDK Documentation, "Correlation Visualization Methods".


NEW QUESTION # 67
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