VCE 1Z0-1110-25 FILES | DUMPS 1Z0-1110-25 DOWNLOAD

Vce 1z0-1110-25 Files | Dumps 1z0-1110-25 Download

Vce 1z0-1110-25 Files | Dumps 1z0-1110-25 Download

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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
  • 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 4
  • 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 5
  • 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.

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Quiz Oracle - 1z0-1110-25 - Oracle Cloud Infrastructure 2025 Data Science Professional Updated Vce Files

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Oracle Cloud Infrastructure 2025 Data Science Professional Sample Questions (Q100-Q105):

NEW QUESTION # 100
You are a data scientist leveraging Oracle Cloud Infrastructure (OCI) to create a model and need some additional Python libraries for processing genome sequencing data. Which of the following THREE statements are correct with respect to installing additional Python libraries to process the data?

  • A. OCI Data Science allows root privileges in notebook sessions
  • B. You can install private or custom libraries from your own internal repositories
  • C. You cannot install a library that's not preinstalled in the provided image
  • D. You can only install libraries using yum and pip as a normal user
  • E. You can install any open-source package available in a publicly accessible Python Package Index (PyPI) repository

Answer: B,C,E

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify correct statements about installing Python libraries in OCI Data Science.
* Understand Notebook Sessions: Run in a managed environment with specific permissions.
* Evaluate Options:
* A: False-No root privileges; users operate as datascience with limited sudo.
* B: True-pip install from PyPI works with internet access (e.g., NAT Gateway).
* C: False-Yum isn't available; pip is the primary tool as a normal user.
* D: False-Misstated; youcaninstall non-preinstalled libraries-likely a typo (intended opposite).
* E: True-Custom repos are supported with proper network config.
* Correct Interpretation: Assuming D's intent was "Youcaninstall..." (common exam error), B, D (corrected), E are true.
* Conclusion: B, D (corrected), E are correct.
OCI documentation states: "In notebook sessions, you can install Python libraries from PyPI (B) or private repositories (E) using pip, but root privileges (A) are not granted-users operate asdatascience." Yum (C) isn' t supported, and D's phrasing contradicts capability-corrected, it's true you can install beyond preinstalled.
B, D (adjusted), E align with OCI's flexibility.
Oracle Cloud Infrastructure Data Science Documentation, "Installing Libraries in Notebook Sessions".


NEW QUESTION # 101
Which function's objective is to represent the difference between the predictive value and the target value?

  • A. Cost function
  • B. Update function
  • C. Fit function
  • D. Optimizer function

Answer: A

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify the function that measures the difference between predicted and actual values in machine learning.
* Understand ML Functions:
* Optimizer function: Adjusts model parameters to minimize error (e.g., gradient descent)-it uses the cost, not defines it.
* Fit function: Trains the model by fitting it to data-process-oriented, not a measure.
* Update function: Typically updates weights during training-not a standard term for error measurement.
* Cost function: Quantifies prediction error (e.g., MSE, cross-entropy)-directly represents the difference.
* Evaluate Options:
* A: Optimizer minimizes the cost, not the cost itself-incorrect.
* B: Fit executes training, not error definition-incorrect.
* C: Update is vague and not a standard ML term for this-incorrect.
* D: Cost function (e.g., loss) measures prediction vs. target-correct.
* Reasoning: The cost function (or loss function) is the mathematical representation of error, guiding optimization.
* Conclusion: D is the correct answer.
In OCI Data Science, the documentation explains: "The cost function (or loss function) measures the difference between the model's predicted values and the actual target values, such as mean squared error for regression or cross-entropy for classification." Optimizers (A) use this to adjust weights, fit (B) is a training step, and update (C) isn't a defined function here-only the cost function (D) fits the description. This aligns with standard ML terminology and OCI's AutoML processes.
Oracle Cloud Infrastructure Data Science Documentation, "Machine Learning Concepts - Cost Functions".


NEW QUESTION # 102
You are building a model and need input that represents data as morning, afternoon, or evening. However, the data contains a timestamp. What part of the Data Science lifecycle would you be in when creating the new variable?

  • A. Model type selection
  • B. Feature engineering
  • C. Data access
  • D. Model validation

Answer: B

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Locate the lifecycle stage for transforming timestamps into categories.
* Understand Lifecycle:
* Model Type Selection: Choosing algorithms-post-data prep.
* Model Validation: Evaluating performance-post-training.
* Data Access: Retrieving raw data-pre-transformation.
* Feature Engineering: Creating new features-correct.
* Evaluate Options:
* A: Too late-incorrect.
* B: Post-model-incorrect.
* C: Pre-transformation-incorrect.
* D: Feature creation-correct.
* Reasoning: Converting timestamps to time periods is feature engineering.
* Conclusion: D is correct.
OCI documentation states: "Feature engineering (D) transforms raw data, such as timestamps into categorical variables (e.g., morning/afternoon), to enhance model inputs." A, B, and C occur at different stages-only D fits OCI's lifecycle for this task.
Oracle Cloud Infrastructure Data Science Documentation, "Feature Engineering Stage".


NEW QUESTION # 103
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 # 104
You are a data scientist trying to load data into your notebook session. You understand that Accelerated Data Science (ADS) SDK supports loading various data formats. Which of the following THREE are ADS- supported data formats?

  • A. Raw Images
  • B. XML
  • C. DOCX
  • D. JSON
  • E. Pandas DataFrame

Answer: B,D,E

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify three data formats supported by ADS SDK for loading data.
* Understand ADS SDK: Facilitates data loading into notebook sessions via DatasetFactory.
* Evaluate Options:
* A. DOCX: Not natively supported-requires conversion (e.g., to text).
* B. Pandas DataFrame: Supported-core format for data manipulation in ADS.
* C. JSON: Supported-common structured data format.
* D. Raw Images: Not directly supported-image data needs preprocessing (e.g., via Vision).
* E. XML: Supported-parseable structured format.
* Reasoning: ADS focuses on tabular/structured data-B, C, E align; A and D require external handling.
* Conclusion: B, C, E are correct.
OCI documentation states: "ADS SDK's DatasetFactory supports loading data from formats like Pandas DataFrames (B), JSON (C), and XML (E), enabling easy integration into notebook sessions." DOCX (A) isn't natively handled, and raw images (D) require preprocessing outside ADS-B, C, E match the supported list.
Oracle Cloud Infrastructure ADS SDK Documentation, "Supported Data Formats".


NEW QUESTION # 105
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