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[General] Oracle 1z0-1110-25 Prüfungsübungen - 1z0-1110-25 Lernhilfe

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【General】 Oracle 1z0-1110-25 Prüfungsübungen - 1z0-1110-25 Lernhilfe

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Oracle 1z0-1110-25 Prüfungsplan:
ThemaEinzelheiten
Thema 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.
Thema 2
  • 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.
Thema 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.
Thema 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.
Thema 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.

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Oracle Cloud Infrastructure 2025 Data Science Professional 1z0-1110-25 Prüfungsfragen mit Lösungen (Q118-Q123):118. Frage
You have created a model and want to use Accelerated Data Science (ADS) SDK to deploy the model. Where are the artifacts to deploy this model with ADS?
  • A. Data Science Artifactory
  • B. OCI Vault
  • C. Model Depository
  • D. Model Catalog
Antwort: D
Begründung:
Detailed Answer in Step-by-Step Solution:
* Objective: Locate artifacts for ADS model deployment.
* Understand ADS Deployment: Requires model artifacts (e.g., score.py) stored in OCI.
* Evaluate Options:
* A: Vault-Stores secrets, not models.
* B: Depository-Not an OCI term.
* C: Model Catalog-Stores models/artifacts for deployment-correct.
* D: Artifactory-Not an OCI service.
* Reasoning: Model Catalog is OCI's model repository for ADS.
* Conclusion: C is correct.
OCI documentation states: "ADS SDK deploys models from the Model Catalog, where trainedmodels and artifacts (e.g., score.py) are stored." Vault (A) is for secrets, B and D aren't real-only C supports ADS deployment.
Oracle Cloud Infrastructure Data Science Documentation, "ADS Model Deployment".

119. Frage
Which OCI service provides a scalable environment for developers and data scientists to run Apache Spark applications at scale?
  • A. Data Flow
  • B. Anomaly Detection
  • C. Data Science
  • D. Data Labeling
Antwort: A
Begründung:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify the OCI service for scalable Spark applications.
* Evaluate Options:
* A: Data Science-ML platform, not Spark-focused.
* B: Anomaly Detection-Specific ML service, not general Spark.
* C: Data Labeling-Annotation tool, not Spark-related.
* D: Data Flow-Managed Spark service for big data.
* Reasoning: Data Flow is OCI's Spark execution engine.
* Conclusion: D is correct.
OCI Data Flow "provides a fully managed environment to run Apache Spark applications at scale, ideal for data processing and ML tasks." Data Science (A) supports Spark in notebooks, but Data Flow (D) is the dedicated, scalable solution-B and C are unrelated.
Oracle Cloud Infrastructure Data Flow Documentation, "Overview".

120. Frage
As a data scientist for a hardware company, you have been asked to predict the revenue demand for the upcoming quarter. You develop a time series forecasting model to analyze the data. Select the correct sequence of steps to predict the revenue demand values for the upcoming quarter.
  • A. Verify, prepare model, deploy, save, predict
  • B. Predict, deploy, save, verify, prepare model
  • C. Prepare model, deploy, verify, save, predict
  • D. Prepare model, verify, save, deploy, predict
Antwort: D
Begründung:
Detailed Answer in Step-by-Step Solution:
* Prepare Model: Build and train the time series model using historical data.
* Verify: Validate the model's accuracy (e.g., using metrics like MAE or RMSE).
* Save: Store the trained model (e.g., in the OCI Model Catalog).
* Deploy: Make the model available for predictions (e.g., via OCI Model Deployment).
* Predict: Generate revenue forecasts for the upcoming quarter.
* Evaluate Options: D follows this logical flow; others (e.g., A starts with "verify" before preparation) don't.
In OCI Data Science, the workflow for time series forecasting involves preparing the model (training), verifying its performance, saving it to the catalog, deploying it, and then predicting. This sequence is standard for ML deployment in OCI, as per the documentation. (Reference: Oracle Cloud Infrastructure Data Science Documentation, "Time Series Forecasting Workflow").

121. Frage
Why is data sampling useful for data scientists?
  • A. It reduces the amount of data storage space that's required for data science applications.
  • B. It enables them to use a representative subset of data to build accurate analytical models more quickly.
  • C. It lets them analyze datasets in small batches to reduce their use of system resources.
Antwort: B
Begründung:
Detailed Answer in Step-by-Step Solution:
* Objective: Determine the primary benefit of data sampling.
* Define Sampling: Selecting a subset of data to represent the whole-used in ML/statistics.
* Evaluate Options:
* A: Small batches reduce resources-True but not the main purpose.
* B: Reduces storage-Incidental, not the goal.
* C: Representative subset for faster, accurate models-Core purpose of sampling.
* Reasoning: Sampling speeds up analysis while maintaining accuracy (e.g., training on 10% of data).
* Conclusion: C is correct.
OCI documentation states: "Data sampling allows data scientists to use a representative subset of a large dataset to build accurate models more quickly, especially when processing full datasets is impractical." A focuses on resources (secondary), B on storage (not primary)-only C captures the analytical intent per OCI's AutoML sampling approach.
Oracle Cloud Infrastructure Data Science Documentation, "Data Sampling Techniques".

122. Frage
Which is NOT a part of Observability and Management Services?
  • A. OCI Management Service
  • B. Event Services
  • C. Logging Analytics
  • D. Logging
Antwort: A
Begründung:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify the non-Observability and Management (O&M) service in OCI.
* Understand O&M: Includes monitoring, logging, events tools.
* Evaluate Options:
* A: Event Services-Triggers actions, part of O&M-correct.
* B: OCI Management Service-Not a defined O&M service-incorrect.
* C: Logging Analytics-Log analysis, O&M component-correct.
* D: Logging-Log collection, O&M component-correct.
* Reasoning: B isn't listed in OCI's O&M suite-others are.
* Conclusion: B is correct (not part of O&M).
OCI documentation lists "Observability and Management Services as including Event Services (A), Logging Analytics (C), and Logging (D)-'OCI Management Service' (B) is not a recognized component." B appears to be a misnomer-only A, C, D are O&M per OCI's service catalog.
Oracle Cloud Infrastructure Observability and Management Documentation, "Service Overview".

123. Frage
......
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