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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

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

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.

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Oracle Cloud Infrastructure 2025 Data Science Professional Sample Questions (Q122-Q127):

NEW QUESTION # 122
Which statement about Oracle Cloud Infrastructure Anomaly Detection is true?

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

Answer: B

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 # 123
You are working as a data scientist for a healthcare company. They decided to analyze the data to find patterns in a large volume of electronic medical records. You are asked to build a PySpark solution to analyze these records in a JupyterLab notebook. What is the order of recommended steps to develop a PySpark application in OCI Data Science?

A. Launch a notebook session, configure core-site.xml, install a PySpark conda environment, develop your PySpark application, create a Data Flow application with the Accelerated Data Science (ADS) SDK
B. Configure core-site.xml, install a PySpark conda environment, create a Data Flow application with the Accelerated Data Science (ADS) SDK, develop your PySpark application, launch a notebook session
C. Install a Spark conda environment, configure core-site.xml, launch a notebook session, create a Data Flow application with the Accelerated Data Science (ADS) SDK, develop your PySpark application
D. Launch a notebook session, install a PySpark conda environment, configure core-site.xml, develop your PySpark application, create a Data Flow application with the Accelerated Data Science (ADS) SDK

Answer: D

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Sequence steps for a PySpark app in OCI Data Science.
* Evaluate Steps:
* Launch notebook: First-provides the environment.
* Install PySpark conda: Second-sets up Spark libraries.
* Configure core-site.xml: Third-connects to data (e.g., Object Storage).
* Develop app: Fourth-writes the PySpark code.
* Data Flow: Fifth-optional scaling, post-development.
* Check Options: D (1, 2, 3, 4, 5) matches this logical flow.
* Reasoning: Notebook first, then setup, coding, and scaling.
* Conclusion: D is correct.
OCI documentation recommends: "1) Launch a notebook session, 2) install a PySpark conda environment, 3) configure core-site.xml for data access, 4) develop your PySpark application, and 5) optionally use Data Flow for scale." D follows this-others (A, B, C) misorder critical steps like launching the notebook.
Oracle Cloud Infrastructure Data Science Documentation, "PySpark in Notebooks".

NEW QUESTION # 124
As a data scientist, you are trying to automate a machine learning (ML) workflow and have decided to use Oracle Cloud Infrastructure (OCI) AutoML Pipeline. Which THREE are part of the AutoML Pipeline?

A. Model Deployment
B. Feature Selection
C. Algorithm Selection
D. Adaptive Sampling
E. Feature Extraction

Answer: B,C,D

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify three stages in OCI AutoML Pipeline.
* Understand Pipeline: Automates ML steps from data to model training.
* Evaluate Options:
* A: Feature Selection-Selects relevant features-correct.
* B: Adaptive Sampling-Reduces data size-correct.
* C: Model Deployment-Post-pipeline step-incorrect.
* D: Feature Extraction-Not explicit in OCI AutoML-incorrect.
* E: Algorithm Selection-Chooses best model-correct.
* Reasoning: A, B, E are core automated stages; C and D are separate.
* Conclusion: A, B, E are correct.
OCI documentation lists "AutoML Pipeline stages as adaptive sampling (B), feature selection (A), algorithm selection (E), and hyperparameter tuning." Deployment (C) is post-pipeline, and extraction (D) isn't highlighted-only A, B, E are included per OCI's design.
Oracle Cloud Infrastructure AutoML Documentation, "Pipeline Components".

NEW QUESTION # 125
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 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
B. 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
C. 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
D. 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

Answer: C

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 # 126
You are a data scientist leveraging Oracle Cloud Infrastructure (OCI) Data Science to create a model and need some additional Python libraries for processing genome sequencing data. Which of the following THREE statements are correct with respect to installing additional Python libraries to process the data?

A. You can install private or custom libraries from your own internal repositories
B. You cannot install a library that's not preinstalled in the provided image
C. You can install any open-source package available on a publicly accessible Python Package Index (PyPI) repository
D. You can only install libraries using yum and pip as a normal user
E. OCI Data Science allows root privileges in notebook sessions

Answer: A,B,C

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify correct statements about installing Python libraries in OCI Data Science.
* Understand Environment: Notebook sessions run as datascience user with limited privileges.
* Evaluate Options:
* A: False-Yum isn't available; pip is the primary tool.
* B: True-Custom repos work with proper network config.
* C: False-No root access; managed environment.
* D: True-PyPI packages installable with internet (NAT Gateway).
* E: False-Youcaninstall beyond preinstalled; likely meant opposite.
* Reasoning: B and D are true; E's intent seems reversed (common exam error)-corrected to B, D.
* Conclusion: B, D (assuming E typo).
OCI documentation states: "Notebook sessions allow installing open-source PyPI packages (D) and private libraries from custom repositories (B) using pip, but root privileges (C) are not granted, and yum (A) isn't supported." E contradicts capability-corrected, B and D are accurate.
Oracle Cloud Infrastructure Data Science Documentation, "Installing Python Libraries".

NEW QUESTION # 127
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