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Oracle 1Z0-1110-25 Exam Questions
- Oracle Cloud Certifications
- Oracle Cloud Infrastructure Certifications
- 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: 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 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.
- Topic 4: 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 5: 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.
Free Oracle 1Z0-1110-25 Exam Actual Questions
Note: Premium Questions for 1Z0-1110-25 were last updated On May. 06, 2025 (see below)
Which statement about logs for Oracle Cloud Infrastructure Jobs is true?
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'.
Which CLI command allows the customized conda environment to be shared with co-workers?
Detailed Answer in Step-by-Step Solution:
Objective: Share a custom conda environment in OCI Data Science.
Understand Commands: OCI provides odsc CLI for environment management.
Evaluate Options:
A: clone duplicates an environment locally---not for sharing.
B: publish uploads the environment to Object Storage for team access---correct.
C: modify doesn't exist as a standard command.
D: install sets up an environment locally---not for sharing.
Reasoning: Sharing requires publishing to a shared location (Object Storage), which publish achieves.
Conclusion: B is the correct command.
The OCI Data Science CLI documentation states: ''Use odsc conda publish to package and upload a custom conda environment to an Object Storage Bucket, making it accessible to other users.'' clone (A) is for local duplication, modify (C) isn't valid, and install (D) is for local setup---not sharing. B is the designated sharing mechanism.
: Oracle Cloud Infrastructure Data Science CLI Reference, 'odsc conda publish'.
You have created a conda environment in your notebook session. This is the first time you are working with published conda environments. You have also created an Object Storage bucket with permission to manage the bucket. Which TWO commands are required to publish the conda environment?
Detailed Answer in Step-by-Step Solution:
Objective: Publish a conda env to Object Storage.
Process: Initialize bucket config, then publish env.
Evaluate Options:
A: Publishes env with slug---correct final step.
B: Lists envs---unrelated to publishing.
C: Sets bucket details---required setup---correct.
D: Creates env---not publishing.
E: Activates env---not for sharing.
Reasoning: C sets up, A executes---standard workflow.
Conclusion: A and C are correct.
OCI documentation states: ''To publish a conda environment, first run odsc conda init (C) with bucket namespace and name, then odsc conda publish (A) with a slug to upload to Object Storage.'' B, D, and E serve other purposes---only A and C are required per OCI's process.
: Oracle Cloud Infrastructure Data Science CLI Reference, 'Publishing Conda Environments'.
Which OCI service provides a scalable environment for developers and data scientists to run Apache Spark applications at scale?
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'.
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 dat
a. Select the correct sequence of steps to predict the revenue demand values for the upcoming quarter.
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').
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