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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

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

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 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

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.

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Oracle Cloud Infrastructure 2025 Data Science Professional Sample Questions (Q71-Q76):

NEW QUESTION # 71
You're going to create an Oracle Cloud Infrastructure Anomaly Detection model for multivariate data. Where do you need to store the training data?

A. Autonomous Data Warehouse
B. Your local machine
C. MySQL database
D. Object Storage Bucket

Answer: D

Explanation:
Detailed Answer in Step-by-Step Solution:
* Understand OCI Anomaly Detection: This service trains models to detect anomalies in multivariate data (e.g., sensor readings), requiring data to be accessible within OCI's ecosystem.
* Assess Storage Requirements: The training data must be in a scalable, OCI-compatible location that the Anomaly Detection service can access programmatically.
* Evaluate Options:
* A. Your local machine: Data on a local machine isn't directly accessible to OCI services without upload, making it impractical for cloud-based training.
* B. MySQL database: While OCI supports MySQL, Anomaly Detection doesn't natively integrate with it for training data; it prefers file-based input.
* C. Autonomous Data Warehouse: This is a database for analytics, not the default storage for Anomaly Detection training data, which expects CSV/JSON files.
* D. Object Storage Bucket: OCI Object Storage is a scalable, durable storage service that Anomaly Detection uses to ingest training data (e.g., CSV files).
* Reasoning: Object Storage is the standard for large-scale data in OCI services, offering seamless integration with Anomaly Detection via APIs or SDKs.
* Conclusion: D is the correct choice as it aligns with the service's architecture.
The OCI Anomaly Detection service requires training data to be uploaded to an Object Storage Bucket in formats like CSV or JSON. This is explicitly outlined in the official documentation, which states that users must "upload the training dataset to an OCI Object Storage bucket" before creating a data asset for model training. Local storage (A) isn't viable for cloud processing, and databases like MySQL (B) or Autonomous Data Warehouse (C) aren't supported as primary inputs. Object Storage (D) provides the scalability and accessibility needed for multivariate anomaly detection workflows.
Oracle Cloud Infrastructure Anomaly Detection Documentation, "Preparing Training Data" section.

NEW QUESTION # 72
You are a data scientist trying to load data into your notebook session. You understand that Accelerated Data Science (ADS) SDK supports loading various data formats. Which of the following THREE are ADS- supported data formats?

A. DOCX
B. JSON
C. Pandas DataFrame
D. XML
E. Raw Images

Answer: B,C,D

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify three data formats supported by ADS SDK for loading data.
* Understand ADS SDK: Facilitates data loading into notebook sessions via DatasetFactory.
* Evaluate Options:
* A. DOCX: Not natively supported-requires conversion (e.g., to text).
* B. Pandas DataFrame: Supported-core format for data manipulation in ADS.
* C. JSON: Supported-common structured data format.
* D. Raw Images: Not directly supported-image data needs preprocessing (e.g., via Vision).
* E. XML: Supported-parseable structured format.
* Reasoning: ADS focuses on tabular/structured data-B, C, E align; A and D require external handling.
* Conclusion: B, C, E are correct.
OCI documentation states: "ADS SDK's DatasetFactory supports loading data from formats like Pandas DataFrames (B), JSON (C), and XML (E), enabling easy integration into notebook sessions." DOCX (A) isn't natively handled, and raw images (D) require preprocessing outside ADS-B, C, E match the supported list.
Oracle Cloud Infrastructure ADS SDK Documentation, "Supported Data Formats".

NEW QUESTION # 73
Which statement about logs for Oracle Cloud Infrastructure Jobs is true?

A. All stdout and stderr are automatically stored when automatic log creation is enabled
B. Logs are automatically deleted when the job and job run is deleted
C. Each job run sends outputs to a single log for that job
D. Integrating data science jobs resources with logging is mandatory

Answer: A

Explanation:
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".

NEW QUESTION # 74
You are attempting to save a model from a notebook session to the model catalog by using ADS SDK, with resource principal as the authentication signer, and you get a 404 authentication error. Which TWO should you look for to ensure permissions are set up correctly?

A. The networking configuration allows access to the Oracle Cloud Infrastructure services through a service gateway
B. The dynamic groups matching rule exists for notebook sessions in the compartment
C. The policy for the dynamic group grants manage permissions for the model catalog in this compartment
D. The policy for your user group grants manage permissions for the model catalog in this compartment
E. The model artifact is saved to the block volume of the notebook session

Answer: B,C

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Troubleshoot a 404 authentication error when saving a model using ADS SDK with resource principal.
* Understand Resource Principal: Allows notebook sessions to act as principals via dynamic groups and policies-no user credentials needed.
* Analyze 404 Error: Indicates an authorization failure-likely missing permissions or misconfigured resource principal.
* Evaluate Options:
* A: True-Dynamic group must include notebook sessions (e.g., resource.type =
'datasciencenotebooksession') to authenticate.
* B: False-Block volume stores artifacts locally, but saving to the catalog is a permission issue, not storage.
* C: True-Policy must grant manage data-science-models to the dynamic group for catalog access.
* D: False-Service gateway ensures network access, but 404 is auth-related, not connectivity.
* E: False-Resource principal uses dynamic group policies, not user group policies.
* Reasoning: A (group inclusion) and C (policy permission) are critical for resource principal auth- others are tangential.
* Conclusion: A and C are correct.
OCI documentation states: "To use resource principal with ADS SDK for model catalog operations, ensure (1) a dynamic group includes the notebook session with a matching rule (e.g., all {resource.type =
'datasciencenotebooksession'}) and (2) a policy grants the dynamic group manage data-science-models permissions in the compartment." B is unrelated (storage location), D is network-focused, and E applies to user auth-not resource principal. A 404 error flags missing auth, fixed by A and C.
Oracle Cloud Infrastructure Data Science Documentation, "Using Resource Principals with ADS SDK".

NEW QUESTION # 75
You are a computer vision engineer building an image recognition model. You decide to use Oracle Data Labeling to annotate your image data. Which of the following THREE are possible ways to annotate an image in Data Labeling?

A. Adding labels to an image using object detection, by drawing bounding boxes to an image
B. Adding multiple labels to an image
C. Adding labels to an image by drawing a bounding box to an image is not supported by Data Labeling
D. Adding a single label to an image
E. Adding labels to an image using semantic segmentation, by drawing multiple bounding boxes to an image

Answer: A,B,D

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify three annotation methods in OCI Data Labeling for images.
* Understand Data Labeling: Supports image annotations for ML.
* Evaluate Options:
* A: Semantic segmentation with boxes-Incorrect; segmentation is pixel-based, not boxes.
* B: Single label (classification)-Supported-correct.
* C: No bounding boxes-False; boxes are supported.
* D: Object detection with boxes-Supported-correct.
* E: Multiple labels (multi-label)-Supported-correct.
* Reasoning: B (classification), D (detection), E (multi-label) match OCI capabilities.
* Conclusion: B, D, E are correct.
OCI documentation states: "Data Labeling supports image annotations via single-label classification (B), object detection with bounding boxes (D), and multi-label classification (E)." A misdefines segmentation, C contradicts support-only B, D, E are valid per OCI's Data Labeling features.
Oracle Cloud Infrastructure Data Labeling Documentation, "Image Annotation Types".

NEW QUESTION # 76
......

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