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Oracle 1z0-1110-25 Exam Syllabus Topics:

Topic
Details

Topic 1

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 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

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.

Oracle Cloud Infrastructure 2025 Data Science Professional Sample Questions (Q152-Q157):

NEW QUESTION # 152
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. OCI Vault
B. Model Catalog
C. Model Depository
D. Data Science Artifactory

Answer: B

Explanation:
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".

NEW QUESTION # 153
Which statement about Oracle Cloud Infrastructure Anomaly Detection is true?

A. It is an important tool for detecting fraud, network intrusions, and discrepancies in sensor time series analysis
B. Data used for analysis can be text or numerical in nature
C. Accepted file types are SQL and Python
D. It is trained on a combination of customer and general industry datasets

Answer: A

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Find a true statement about OCI Anomaly Detection.
* Understand Service: Detects anomalies in multivariate data (e.g., time series).
* Evaluate Options:
* A: False-Accepted types are CSV/JSON, not SQL/Python.
* B: Partially true-Focuses on numerical data (e.g., sensors), not text broadly.
* C: True-Used for fraud, intrusions, and sensor anomalies (key use cases).
* D: False-Trained on customer data only, not general datasets.
* Reasoning: C aligns with documented applications; others misalign.
* Conclusion: C is correct.
OCI Anomaly Detection documentation states: "The service is designed to detect anomalies in time series data, making it valuable for fraud detection, network intrusion analysis, and sensor discrepancies." A is incorrect (file formats), B overgeneralizes (numerical focus), and D misstates training data-only C matches the service's purpose.
Oracle Cloud Infrastructure Anomaly Detection Documentation, "Use Cases".

NEW QUESTION # 154
Which statement about logs for Oracle Cloud Infrastructure Jobs is true?

A. Logs are automatically deleted when the job and job run is deleted
B. All stdout and stderr are automatically stored when automatic log creation is enabled
C. Integrating data science jobs resources with logging is mandatory
D. Each job run sends outputs to a single log for that job

Answer: B

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify a true statement about OCI Jobs logging.
* Understand Logging: Jobs can log stdout/stderr to OCI Logging service.
* Evaluate Options:
* A: False-Each run has its own log, not a single job log.
* B: False-Logging is optional, not mandatory.
* C: True-When enabled, stdout/stderr are auto-captured.
* D: False-Logs persist unless explicitly deleted.
* Reasoning: C matches OCI's automatic logging feature.
* Conclusion: C is correct.
OCI documentation states: "When automatic log creation is enabled for Data Science Jobs, all stdout and stderr outputs are captured and stored in the OCI Logging service." A is incorrect (per-run logs), B is optional, and D contradicts log retention-only C is accurate.
Oracle Cloud Infrastructure Data Science Documentation, "Jobs Logging".

NEW QUESTION # 155
Which of the following TWO non-open source JupyterLab extensions has Oracle Cloud Infrastructure (OCI) Data Science developed and added to the notebook session experience?

A. Command Palette
B. Notebook Examples
C. Environment Explorer
D. Table of Contents
E. Terminal

Answer: B,C

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify two OCI-developed, non-open-source JupyterLab extensions.
* Understand Extensions: OCI enhances JupyterLab with proprietary tools.
* Evaluate Options:
* A: Environment Explorer-OCI-specific, non-open-correct.
* B: Table of Contents-Open-source Jupyter-incorrect.
* C: Command Palette-Open-source Jupyter-incorrect.
* D: Notebook Examples-OCI-specific, non-open-correct.
* E: Terminal-Open-source Jupyter-incorrect.
* Reasoning: A and D are OCI proprietary; others are standard JupyterLab.
* Conclusion: A and D are correct.
OCI documentation states: "OCI Data Science adds non-open-source extensions like Environment Explorer (A) for conda management and Notebook Examples (D) for sample code-both proprietary enhancements." B, C, and E are open-source JupyterLab defaults-only A and D are OCI-specific per the notebook session design.
Oracle Cloud Infrastructure Data Science Documentation, "JupyterLab Extensions".

NEW QUESTION # 156
You want to create an anomaly detection model using the OCI Anomaly Detection service that avoids as many false alarms as possible. False Alarm Probability (FAP) indicates model performance. How would you set the value of the False Alarm Probability?

A. Low
B. Use a function
C. High
D. Zero

Answer: A

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Minimize false alarms in OCI Anomaly Detection.
* Understand FAP: False Alarm Probability-lower FAP means fewer false positives.
* Evaluate Options:
* A: High FAP-Increases false alarms-incorrect.
* B: Low FAP-Reduces false alarms-correct.
* C: Zero FAP-Unrealistic; risks missing true anomalies.
* D: Function-Vague, not a direct setting.
* Reasoning: Low FAP balances sensitivity and false positives- aligns with goal.
* Conclusion: B is correct.
OCI Anomaly Detection documentation states: "Set a low False Alarm Probability (FAP) to minimize false positives, though too low (e.g., zero) may miss anomalies." B fits the goal-high (A) increases errors, zero (C) is impractical, and function (D) isn't specified.
Oracle Cloud Infrastructure Anomaly Detection Documentation, "Configuring FAP".

NEW QUESTION # 157
......

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