Examcollection 1z0-1110-25 Questions Answers | 1z0-1110-25 Latest Exam Notes

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Oracle 1z0-1110-25 Latest Exam Notes | 1z0-1110-25 Free Practice

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Oracle 1z0-1110-25 Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	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 3	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.
Topic 4	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 5	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.

Oracle Cloud Infrastructure 2025 Data Science Professional Sample Questions (Q112-Q117):

NEW QUESTION # 112
You are given a task of writing a program that sorts document images by language. Which Oracle AI Service would you use?

A. Oracle Digital Assistant
B. OCI Speech
C. OCI Vision
D. OCI Language

Answer: D

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Select an OCI AI service to sort images by language.
* Evaluate Options:
* A: Digital Assistant-Chatbots, not image/language processing.
* B: Vision-Image analysis (e.g., object detection), not language sorting.
* C: Speech-Audio-to-text, not image-based.
* D: Language-Text analysis (e.g., language detection) after OCR-correct.
* Reasoning: Images need OCR (Vision) then language detection (Language)-D fits the sorting task.
* Conclusion: D is correct.
OCI Language "detects and classifies languages in text," often paired with OCI Vision's OCR to process document images. Vision (B) extracts text, but Language (D) sorts by language-Digital Assistant (A) and Speech (C) don't apply. Documentation supports this workflow.
Oracle Cloud Infrastructure Language Documentation, "Language Detection".

NEW QUESTION # 113
Which statement best describes Oracle Cloud Infrastructure Data Science Jobs?

A. Jobs let you define and run repeatable tasks on fully managed third-party cloud infrastructures.
B. Jobs let you define and run all Oracle Cloud DevOps workloads.
C. Jobs let you define and run repeatable tasks on fully managed infrastructure.
D. Jobs let you define and run repeatable tasks on customer-managed infrastructure.

Answer: C

Explanation:
Detailed Answer in Step-by-Step Solution:
* Understand OCI Data Science Jobs: This service automates ML tasks (e.g., training, evaluation) with configurable, repeatable executions.
* Key Characteristics: Jobs run on OCI's infrastructure, managed by Oracle, not the customer or third parties, and are specific to Data Science, not general DevOps.
* Evaluate Options:
* A: Correct-Jobs are defined by users (e.g., via scripts) and executed on OCI's fully managed compute resources.
* B: Incorrect-Infrastructure is managed by OCI, not the customer.
* C: Incorrect-No third-party cloud integration; it's OCI-specific.
* D: Incorrect-Jobs are for Data Science tasks (e.g., ML training), not all DevOps workloads (e.
g., CI/CD pipelines).
* Reasoning: "Fully managed" means OCI handles provisioning and scaling, aligning with A.
* Conclusion: A accurately reflects the service's purpose and operation.
OCI Data Science Jobs "allow users to define and execute repeatable machine learning tasks, such as model training or batch processing, on fully managed OCI infrastructure." This eliminates customer management (B), third-party clouds (C), or broad DevOps scope (D). The documentation emphasizes automation and management by OCI, making A the precise description.
Oracle Cloud Infrastructure Data Science Documentation, "Overview of Jobs" section.

NEW QUESTION # 114
A bike sharing platform has collected user commute data for the past 3 years. For increasing profitability and making useful inferences, a machine learning model needs to be built from the accumulated data. Which of the following options has the correct order of the required machine learning tasks for building a model?

A. Data Access, Feature Exploration, Data Exploration, Feature Engineering, Modeling
B. Data Access, Data Exploration, Feature Exploration, Feature Engineering, Modeling
C. Data Access, Feature Exploration, Feature Engineering, Data Exploration, Modeling
D. Data Access, Data Exploration, Feature Engineering, Feature Exploration, Modeling

Answer: D

Explanation:
Detailed Answer in Step-by-Step Solution:
* Data Access: The first step in any machine learning workflow is accessing the raw data. This involves retrieving the user commute data collected over the past 3 years from the bike-sharing platform's storage system.
* Data Exploration: Once data is accessed, it's explored to understand its structure, quality, and patterns (e.g., missing values, distributions). This step helps identify what preprocessing is needed.
* Feature Engineering: After understanding the data, features are created or transformed (e.g., commute duration, time of day) to improve model performance. This step precedes feature exploration because you need engineered features to analyze further.
* Feature Exploration: This involves analyzing the engineered features (e.g., correlation analysis, importance ranking) to refine them or select the most relevant ones for modeling.
* Modeling: Finally, the prepared data and features are used to train and evaluate a machine learning model.
Option C (Data Access, Data Exploration, Feature Engineering, Feature Exploration, Modeling) follows this logical sequence, aligning with standard ML workflows.
The correct order reflects the machine learning lifecycle as outlined in Oracle's OCI Data Science documentation. Data Access is the initial step to retrieve data, followed by Data Exploration to assess it (e.g., using OCI Data Science Notebook Sessions with tools like pandas). Feature Engineering transforms raw data into meaningful inputs, followed by Feature Exploration to analyze feature importance (e.g., using ADS SDK' s correlation tools). Modeling is the final step where the model is built and trained. This sequence is consistent with Oracle's recommended practices for building ML models in OCI Data Science (Reference: Oracle Cloud Infrastructure Data Science Service Documentation, "Machine Learning Lifecycle").

NEW QUESTION # 115
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. show_corr()
B. compute()
C. to_xgb()
D. show_in_notebook()

Answer: D

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 # 116
You want to write a Python script to create a collection of different projects for your data science team. Which Oracle Cloud Infrastructure (OCI) Data Science interface would you use?

A. Mobile App
B. Command Line Interface (CLI)
C. The OCI Software Development Kit (SDK)
D. OCI Console

Answer: C

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Choose an interface for a Python script to manage projects.
* Evaluate Options:
* A: OCI SDK-Python-based, scriptable-correct.
* B: Console-GUI, not scriptable.
* C: CLI-Command-based, not Python-native.
* D: Mobile App-Not for scripting.
* Reasoning: A enables programmatic project creation.
* Conclusion: A is correct.
OCI documentation states: "Use the OCI Python SDK (A) to programmatically manage Data Science resources, like creating projects, via Python scripts." B, C, and D don't support Python scripting-only A fits.
Oracle Cloud Infrastructure SDK Documentation, "Data Science API".

NEW QUESTION # 117
......

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Examcollection 1z0-1110-25 Questions Answers | 1z0-1110-25 Latest Exam Notes

Oracle 1z0-1110-25 Latest Exam Notes | 1z0-1110-25 Free Practice

Oracle 1z0-1110-25 Exam Syllabus Topics:

Oracle Cloud Infrastructure 2025 Data Science Professional Sample Questions (Q112-Q117):

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