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1z0-1110-25 Lernhilfe - 1z0-1110-25 Testantworten

Die Oracle 1z0-1110-25 Zertifizierungsprüfung stellt eine wichtige Position in der IT-Branche dar, worüber viele IT-Experten sich einig sind. Die Oracle 1z0-1110-25 (Oracle Cloud Infrastructure 2025 Data Science Professional) Zertifizierungsprüfung zu bestehen ist jedoch nicht einfach. Es erfordert umfangreiche Fachkenntnisse und Erfahrungen, weil die Oracle 1z0-1110-25 Zertifizierungsprüfung sowieso eine autoritäre Prüfung, die das Niveau der IT-Fachkenntnissen überprüft. Wenn Sie das Oracle 1z0-1110-25 Zertifikat bekommen, wird Ihre Fähigkeit von den Firmen akzeptiert. Das bedeutet, dass die zielgerichteten Schulungsunterlagen von It-Pruefung sehr wirksam ist. Mit unseren Prüfungsmaterialien können Sie 100% die Prüfung bestehen.

Oracle 1z0-1110-25 Prüfungsplan:

Thema
Einzelheiten

Thema 1

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 2

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.

Thema 3

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 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.

Thema 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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1z0-1110-25 Testantworten - 1z0-1110-25 Simulationsfragen

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Oracle Cloud Infrastructure 2025 Data Science Professional 1z0-1110-25 Prüfungsfragen mit Lösungen (Q126-Q131):

126. Frage
Where do calls to stdout and stderr from score.py go in a model deployment?

A. The file that was defined for them on the Virtual Machine (VM)
B. The OCI console
C. The predict log in the Oracle Cloud Infrastructure (OCI) Logging service as defined in the deployment
D. The OCI Cloud Shell, which can be accessed from the console

Antwort: C

Begründung:
Detailed Answer in Step-by-Step Solution:
* Objective: Locate score.py output in OCI model deployment.
* Understand Deployment: Logs are centralized in OCI Logging.
* Evaluate Options:
* A: VM file-Not default; requires custom config-incorrect.
* B: Predict log in OCI Logging-Standard destination-correct.
* C: Cloud Shell-Separate tool, not logs-incorrect.
* D: Console-UI, not raw logs-incorrect.
* Reasoning: B aligns with OCI's logging integration.
* Conclusion: B is correct.
OCI documentation states: "score.py stdout and stderr are captured in the predict log within OCI Logging service (B), configured during deployment." A isn't standard, C and D don't receive logs-only B fits OCI's logging setup.
Oracle Cloud Infrastructure Data Science Documentation, "Model Deployment Logging".

127. Frage
While working with Git on Oracle Cloud Infrastructure (OCI) Data Science, you notice that two of the operations are taking more time than the others due to your slow internet speed. Which TWO operations would experience the delay?

A. Moving the changes into staging area for the next commit
B. Making a commit that is taking a snapshot of the local repository for the next push
C. Converting an existing local project folder to a Git repository
D. Pushing changes to a remote repository
E. Updating the local repo to match the content from a remote repository

Antwort: D,E

Begründung:
Detailed Answer in Step-by-Step Solution:
* Analyze Git Operations: Identify which depend on internet speed.
* Evaluate Options:
* A. Staging (git add): Local operation-adds files to the index; no network involved.
* B. Updating local repo (git pull): Downloads remote changes-requires internet, slowed by poor connectivity.
* C. Pushing changes (git push): Uploads local commits to remote-network-dependent, delayed by slow speed.
* D. Committing (git commit): Local snapshot-no network needed.
* E. Converting to Git repo (git init): Local initialization-no internet required.
* Reasoning: Only B and C involve network transfers, directly impacted by slow internet.
* Conclusion: B and C are the correct choices.
Git operations like git pull (B) and git push (C) rely on network communication with a remote repository, such as OCI Code Repository, and are documented as "bandwidth-sensitive" in OCI's guides. Local actions like staging (A), committing (D), and initializing (E) occur on the user's machine, unaffected by internet speed. This matches standard Git behavior and OCI's implementation.
Oracle Cloud Infrastructure Data Science Documentation, "Using Git in Notebook Sessions".

128. Frage
Which Oracle Accelerated Data Science (ADS) classes can be used for easy access to datasets fromreference libraries and index websites, such as scikit-learn?

A. SecretKeeper
B. DatasetFactory
C. ADSTuner
D. DatasetBrowser

Antwort: B

Begründung:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify ADS class for accessing datasets (e.g., scikit-learn).
* Evaluate Options:
* A: DatasetBrowser-Not an ADS class.
* B: DatasetFactory-Loads datasets from sources like scikit-learn-correct.
* C: ADSTuner-Hyperparameter tuning, not data access.
* D: SecretKeeper-Manages credentials, not datasets.
* Reasoning: DatasetFactory simplifies dataset loading (e.g., DatasetFactory.open()).
* Conclusion: B is correct.
OCI documentation states: "DatasetFactory in ADS SDK provides methods to easily load datasets from libraries like scikit-learn or other sources (e.g., DatasetFactory.open('sklearn.datasets:load_iris'))." A isn't real, C tunes models, and D handles secrets-only B fits.
Oracle Cloud Infrastructure ADS SDK Documentation, "DatasetFactory".

129. Frage
Which OCI service provides a managed Kubernetes service for deploying, scaling, and managing containerized applications?

A. Oracle Cloud Infrastructure Load Balancing
B. Oracle Cloud Infrastructure Container Engine for Kubernetes
C. Oracle Cloud Infrastructure Container Registry
D. Oracle Cloud Infrastructure Streaming

Antwort: B

Begründung:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify OCI's managed Kubernetes service.
* Evaluate Options:
* A: Container Registry-Stores images, not Kubernetes.
* B: Load Balancing-Network service, not Kubernetes.
* C: Container Engine (OKE)-Managed Kubernetes-correct.
* D: Streaming-Data streaming, not containers.
* Reasoning: C is OCI's Kubernetes offering-OKE.
* Conclusion: C is correct.
OCI documentation states: "OCI Container Engine for Kubernetes (OKE) (C) provides a managed service to deploy, scale, and manage containerized applications using Kubernetes." A, B, and D serve other purposes- only C matches per OCI's container services.
Oracle Cloud Infrastructure OKE Documentation, "Overview".

130. 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. Model Catalog
B. OCI Vault
C. Data Science Artifactory
D. Model Depository

Antwort: A

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".

131. Frage
......

Es ist Ihnen weis, It-Pruefung zu wählen, um die Oracle 1z0-1110-25 Zertifizierungsprüfung zu bestehen. Sie können im Internet die Fragenkataloge zur Oracle 1z0-1110-25 Zertifizierungsprüfung von It-Pruefung teilweise kostenlos herunterladen. Dann werden Sie mehr Vertrauen in unsere Produkte haben. Sie können sich dann gut auf Ihre Oracle 1z0-1110-25 Zertifizierungsprüfung vorbereiten. Für den Durchfall in der Prüfung, zahlen wir Ihnen die gesammte Summe zurück.

1z0-1110-25 Testantworten: https://www.it-pruefung.com/1z0-1110-25.html

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