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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

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 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

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

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.

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Oracle Cloud Infrastructure 2025 Data Science Professional Sample Questions (Q100-Q105):

NEW QUESTION # 100
What happens when a notebook session is deactivated?

A. Compute cost increases due to frequent deactivation
B. The data on boot volume is preserved
C. The block volume attached to the notebook is permanently deleted
D. The underlying compute instance stops

Answer: D

Explanation:
Detailed Answer in Step-by-Step Solution:
* Understand Notebook Sessions: These are OCI compute instances running JupyterLab.
* Deactivation Impact: Deactivating stops the session to save costs.
* Evaluate Options:
* A: False-Costs decrease as compute stops.
* B: False-Boot volume data isn't preserved; block volume data is.
* C: True-The compute instance shuts down, halting billing.
* D: False-Block volume persists unless explicitly deleted.
* Reasoning: Deactivation stops the instance (C), preserving block volume data separately.
* Conclusion: C is correct.
The OCI documentation states: "When a notebook session is deactivated, the underlying compute instance stops, and billing for compute resources ceases. Data on the attached block volume is preserved, but the boot volume is not." A is backwards, B misattributes preservation, and D overstates deletion-only C aligns with the process.
Oracle Cloud Infrastructure Data Science Documentation, "Notebook Session Lifecycle".

NEW QUESTION # 101
You are a data scientist; you use the Oracle Cloud Infrastructure (OCI) Language service to train custom models. Which types of custom models can be trained?

A. Text classification, Named Entity Recognition (NER)
B. Sentiment Analysis, Named Entity Recognition (NER)
C. Object detection, Text classification
D. Image classification, Named Entity Recognition (NER)

Answer: A

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify custom model types for OCI Language.
* Understand OCI Language: Focuses on text analysis.
* Evaluate Options:
* A: Image classification-Not text-based, incorrect.
* B: Text classification, NER-Both text tasks-correct.
* C: Sentiment, NER-Sentiment is pretrained, not custom.
* D: Object detection-Image-based, incorrect.
* Reasoning: B aligns with OCI Language's text custom models.
* Conclusion: B is correct.
OCI Language documentation states: "Custom models can be trained for text classification and Named Entity Recognition (NER) using your data." Image tasks (A, D) are for Vision, and sentiment (C) is pretrained- only B fits OCI Language's scope.
Oracle Cloud Infrastructure Language Documentation, "Custom Model Training".

NEW QUESTION # 102
You are a data scientist building a pipeline in the Oracle Cloud Infrastructure (OCI) Data Science service for your machine learning project. You want to optimize the pipeline completion time by running some steps in parallel. Which statement is true about running pipeline steps in parallel?

A. All pipeline steps are always run in parallel
B. Pipeline steps can be run in sequence or in parallel, as long as they create a directed acyclic graph (DAG)
C. Steps in a pipeline can be run only sequentially
D. Parallel steps cannot be run if they are completely independent of each other

Answer: B

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Understand parallel execution in OCI Pipelines.
* Evaluate Options:
* A: False-Pipelines support parallelism.
* B: True-DAG allows sequential/parallel steps-correct.
* C: False-Not all steps must be parallel.
* D: False-Independence enables parallelism.
* Reasoning: B reflects OCI's DAG-based flexibility.
* Conclusion: B is correct.
OCI documentation states: "Pipeline steps can run sequentially or in parallel, defined by a directedacyclic graph (DAG) (B), allowing optimization of completion time." A, C, and D misrepresent this-only B aligns with OCI's pipeline design.
Oracle Cloud Infrastructure Data Science Documentation, "Pipeline Parallelism".

NEW QUESTION # 103
You are a researcher who requires access to large datasets. Which OCI service would you use?

A. Oracle Open Data
B. ADW (Autonomous Data Warehouse)
C. Oracle Databases
D. OCI Data Science

Answer: A

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Find the OCI service for accessing large public datasets.
* Evaluate Options:
* A: Oracle Databases-General-purpose, not dataset-focused.
* B: ADW-Analytics warehouse, not a dataset repository.
* C: OCI Data Science-ML platform, not a dataset provider.
* D: Oracle Open Data-Free, public datasets (e.g., geospatial).
* Reasoning: Open Data provides pre-existing large datasets for research.
* Conclusion: D is correct.
OCI documentation states: "Oracle Open Data provides free access to large, curated datasets, such as geospatial data, ideal for researchers." Databases (A) and ADW (B) are for storage/analytics, Data Science (C) is for ML-not datasets-only Open Data (D) fits.
Oracle Cloud Infrastructure Open Data Documentation, "Overview".

NEW QUESTION # 104
You are a data scientist designing an air traffic control model, and you choose to leverage Oracle AutoML.
You understand that the Oracle AutoML pipeline consists of multiple stages and automatically operates in a certain sequence. What is the correct sequence for the Oracle AutoML pipeline?

A. Algorithm selection, Adaptive sampling, Feature selection, Hyperparameter tuning
B. Adaptive sampling, Feature selection, Algorithm selection, Hyperparameter tuning
C. Adaptive sampling, Algorithm selection, Feature selection, Hyperparameter tuning
D. Algorithm selection, Feature selection, Adaptive sampling, Hyperparameter tuning

Answer: B

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Sequence OCI AutoML pipeline stages.
* Stages:
* Adaptive sampling: Reduces data size if large.
* Feature selection: Picks relevant features.
* Algorithm selection: Chooses best model type.
* Hyperparameter tuning: Optimizes model params.
* Evaluate: C (sampling, features, algorithms, tuning) matches logical flow-data first, then model.
* Reasoning: Sampling precedes feature work-standard in OCI.
* Conclusion: C is correct.
OCI documentation states: "AutoML pipeline runs 1) adaptive sampling, 2) feature selection, 3) algorithm selection, 4) hyperparameter tuning (C)." Sampling reduces data first, then features and models are optimized-other orders (A, B, D) misalign with OCI's sequence.
Oracle Cloud Infrastructure AutoML Documentation, "Pipeline Sequence".

NEW QUESTION # 105
......

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