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Oracle 1z0-1110-25 Exam Syllabus Topics:

Topic
Details

Topic 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.

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

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 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

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 Sample Questions (Q40-Q45):

NEW QUESTION # 40
What is the first step in the data science process?

A. Experimenting with and tuning different analytical models
B. Working with data owners
C. Collecting data and preparing it for analysis
D. Defining an analytical hypothesis that could provide business value

Answer: D

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify the initial data science step.
* Define Process: Starts with problem definition, then data and modeling.
* Evaluate Options:
* A: Data collection-Second step after problem definition.
* B: Modeling-Later stage.
* C: Hypothesis-Sets the goal, first step-correct.
* D: Data owners-Collaboration, not the start.
* Reasoning: Hypothesis drives the process (e.g., "Can we predict churn?").
* Conclusion: C is correct.
OCI documentation states: "The data science process begins with defining an analytical hypothesis to address a business problem, followed by data collection and analysis." C precedes A, B, and D-aligning with OCI's structured approach.
Oracle Cloud Infrastructure Data Science Documentation, "Data Science Process".

NEW QUESTION # 41
As you are working in your notebook session, you find that your notebook session does not have enough compute CPU and memory for your workload. How would you scale up your notebook session without losing your work?

A. Deactivate your notebook session, provision a new notebook session on a larger compute shapeand re- create all of your file changes
B. Download all your files and data to your local machine, delete your notebook session, provision a new notebook session on a larger compute shape, and upload your files from your local machine to the new notebook session
C. Create a temporary bucket on Object Storage, write all your files and data to Object Storage, delete your notebook session, provision a new notebook session on a larger compute shape, and copy your files and data from your temporary bucket onto your new notebook session
D. Ensure your files and environments are written to the block volume storage under the /home
/datascience directory, deactivate the notebook session, and activate the notebook session with a larger compute shape selected

Answer: D

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Scale up notebook without losing work.
* Evaluate Options:
* A: Object Storage-Extra steps, inefficient.
* B: Block volume-Persists data, seamless scale-correct.
* C: Local machine-Risky, cumbersome.
* D: Recreate-Loses work, impractical.
* Reasoning: B uses OCI's built-in persistence.
* Conclusion: B is correct.
OCI documentation states: "Files in /home/datascience (B) persist on block volume; deactivate, then reactivate with a larger shape to scale up without data loss." A, C, and D add complexity or risk-only B is optimal per OCI's design.
Oracle Cloud Infrastructure Data Science Documentation, "Scaling Notebook Sessions".

NEW QUESTION # 42
After you have created and opened a notebook session, you want to use the Accelerated Data Science (ADS) SDK to access your data and get started with exploratory data analysis. From which TWO places can you access the ADS SDK?

A. Oracle Big Data Service
B. Python Package Index (PyPI)
C. Oracle Machine Learning
D. Oracle Autonomous Data Warehouse
E. Conda environment in OCI Data Science

Answer: B,E

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Locate sources for ADS SDK in OCI.
* Understand ADS SDK: A Python library for Data Science tasks (e.g., EDA).
* Evaluate Options:
* A: Big Data Service-Spark-focused, not ADS source.
* B: Machine Learning-Separate service, not ADS-related.
* C: Conda in OCI Data Science-Preinstalled ADS in notebook sessions.
* D: PyPI-Public source to install ADS (pip install oracle-ads).
* E: ADW-Database, not an SDK source.
* Reasoning: C (preinstalled) and D (installable) are practical access points.
* Conclusion: C and D are correct.
OCI documentation states: "The ADS SDK is available in OCI Data Science notebook sessions via preinstalled conda environments (C) and can be installed from PyPI (D) using pip install oracle-ads." Big Data (A), Machine Learning (B), and ADW (E) don't host ADS-only C and D apply.
Oracle Cloud Infrastructure Data Science Documentation, "ADS SDK Installation".

NEW QUESTION # 43
Which step is a part of the AutoML pipeline?

A. Model saved to Model Catalog
B. Feature Selection
C. Model Deployment
D. Feature Extraction

Answer: B

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify a step in OCI's AutoML pipeline.
* Understand AutoML: Automates model building-includes preprocessing, selection, and tuning.
* Evaluate Options:
* A: Feature Extraction (e.g., PCA) isn't explicitly part of OCI AutoML-too specific.
* B: Saving to Model Catalog is post-AutoML, not a pipeline step.
* C: Deployment is a separate action after AutoML-incorrect.
* D: Feature Selection (e.g., choosing relevant features) is a core AutoML step-correct.
* Reasoning: OCI AutoML automates feature selection, algorithm choice, and tuning-D fits.
* Conclusion: D is correct.
OCI AutoML's pipeline includes "feature selection, algorithm selection, adaptive sampling, and hyperparameter tuning," per the documentation. Extraction (A) isn't highlighted, while saving (B) and deployment (C) are post-process actions-only Feature Selection (D) is an integral automated step.
Oracle Cloud Infrastructure Data Science Documentation, "AutoML Pipeline".

NEW QUESTION # 44
Which step is unique to MLOps, as opposed to DevOps?

A. Continuous training
B. Continuous deployment
C. Continuous delivery
D. Continuous integration

Answer: A

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify a step unique to MLOps vs. DevOps.
* Compare MLOps and DevOps:
* DevOps: Focuses on software deployment (CI/CD).
* MLOps: Extends DevOps to ML, adding model-specific steps.
* Evaluate Options:
* A: Continuous deployment-Common to both (software/models).
* B: Continuous integration-Common to both (code merging).
* C: Continuous delivery-Common to both (releasing updates).
* D: Continuous training-Unique to MLOps (retraining models with new data).
* Reasoning: Only D addresses ML-specific needs (model retraining).
* Conclusion: D is correct.
OCI documentation notes: "MLOps extends DevOps with continuous training, a process unique to machine learning where models are retrained with new data to maintain performance." CI (B), CD (A), and delivery (C) are shared with DevOps-only continuous training (D) is MLOps-specific.
Oracle Cloud Infrastructure Data Science Documentation, "MLOps Concepts".

NEW QUESTION # 45
......

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