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Oracle 1z0-1110-25 Exam Syllabus Topics:

Topic
Details

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

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 3

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 4

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 5

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.

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Oracle Cloud Infrastructure 2025 Data Science Professional Sample Questions (Q104-Q109):

NEW QUESTION # 104
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. Notebook Examples
B. Table of Contents
C. Command Palette
D. Environment Explorer
E. Terminal

Answer: A,D

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 # 105
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. Converting an existing local project folder to a Git repository
C. Making a commit that is taking a snapshot of the local repository for the next push
D. Pushing changes to a remote repository
E. Updating the local repo to match the content from a remote repository

Answer: D,E

Explanation:
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".

NEW QUESTION # 106
You have received machine learning model training code, without clear information about the optimal shape to run the training. How would you proceed to identify the optimal compute shape for your model training that provides a balanced cost and processing time?

A. Start with a smaller shape and monitor the Job Run metrics and time required to complete the model training. If the compute shape is not fully utilized, tune the model parameters, and rerun the job. Repeat the process until the shape resources are fully utilized
B. Start with the strongest compute shape Jobs support and monitor the Job Run metrics and time required to complete the model training. Tune the model so that it utilizes as much compute resources as possible, even at an increased cost
C. Start with a random compute shape and monitor the utilization metrics and time required to finish the model training. Perform model training optimizations and performance tests in advance to identify the right compute shape before running the model training as a job
D. Start with a smaller shape and monitor the utilization metrics and time required to complete the model training. If the compute shape is fully utilized, change to compute that has more resources and rerun the job. Repeat the process until the processing time does not improve

Answer: D

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Optimize compute shape for cost and time.
* Evaluate Options:
* A: Tuning params-Focuses on model, not shape.
* B: Strongest shape-Costly, unbalanced.
* C: Scale up when utilized-Balances cost/time-correct.
* D: Random start-Unsystematic.
* Reasoning: C iteratively optimizes based on utilization.
* Conclusion: C is correct.
OCI documentation advises: "Start with a small shape, monitor utilization and time (C); scale up if fully utilized until performance stabilizes-optimizes cost and speed." A misfocuses, B overspends, D lacks method-only C aligns.
Oracle Cloud Infrastructure Data Science Documentation, "Compute Shape Optimization".

NEW QUESTION # 107
You have a complex Python code project that could benefit from using Data Science Jobs as it is a repeatable machine learning model training task. The project contains many sub-folders and classes. What is the best way to run this project as a Job?

A. Rewrite your code so that it is a single executable Python or Bash/Shell script file
B. ZIP the entire code project folder and upload it as a Job artifact. Jobs automatically identifies the main top-level where the code is run
C. ZIP the entire code project folder, upload it as a Job artifact on job creation, and set JOB_RUN_ENTRYPOINT to point to the main executable file
D. ZIP the entire code project folder and upload it as a Job artifact on job creation. Jobs identifies the main executable file automatically

Answer: C

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Run a complex Python project as an OCI Job.
* Evaluate Options:
* A: Auto-identification-False; entrypoint must be set.
* B: Rewrite-Unnecessary, inefficient.
* C: Auto-executable-False; needs explicit entrypoint.
* D: ZIP with entrypoint-Correct, flexible approach.
* Reasoning: D preserves structure, specifies execution.
* Conclusion: D is correct.
OCI documentation states: "For complex projects, ZIP the folder and upload as a Job artifact, then set JOB_RUN_ENTRYPOINT (D) to the main executable (e.g., main.py)." Auto-detection (A, C) isn't supported, and B discards structure-D is best.
Oracle Cloud Infrastructure Data Science Documentation, "Job Artifacts".

NEW QUESTION # 108
What is feature engineering in machine learning used for?

A. To perform parameter tuning
B. To interpret ML models
C. To transform existing features into new ones
D. To help understand the dataset features

Answer: C

Explanation:
Detailed Answer in Step-by-Step Solution:
* Define Feature Engineering: It's the process of creating or modifying features to improve model performance.
* Evaluate Options:
* A: Parameter tuning adjusts model hyperparameters (e.g., learning rate), not features.
* B: Model interpretation (e.g., SHAP values) explains predictions, not feature creation.
* C: Transforming features (e.g., normalizing, encoding) is the core of feature engineering-correct.
* D: Understanding features occurs during exploration, not engineering.
* Reasoning: Feature engineering directly manipulates data inputs (e.g., converting timestamps to day-of- week), distinct from tuning or interpretation.
* Conclusion: C is the precise definition.
OCI Data Science documentation defines feature engineering as "the process of transforming raw data into features that better represent the underlying problem to the predictive models, resulting in improved model accuracy." Examples include scaling or creating interaction terms, aligning with C. Other options (A, B, D) relate to different ML stages.
Oracle Cloud Infrastructure Data Science Documentation, "Feature Engineering Overview".

NEW QUESTION # 109
......

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