100+ Free OCI Data Science Pro Practice Questions

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Question 1

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Which OCI Data Science resource serves as the top-level container that groups notebook sessions, models, jobs, and pipelines for a single team or initiative?

Project

Compartment

Tenancy

Application

to track

2026 Statistics

Key Facts: OCI Data Science Pro Exam

Questions

Oracle University 1Z0-1110-25 page

90 min

Exam Duration

Oracle University 1Z0-1110-25 page

68%

Passing Score

Oracle University 1Z0-1110-25 page

$245

Exam Fee (USD)

Oracle University pricing

45%

Largest Domain

Implement End-to-End ML Lifecycle

Pearson VUE

Test Provider

Oracle University / OnVUE

Oracle Cloud Infrastructure 2025 Data Science Professional (1Z0-1110-25) is a 50-question, 90-minute proctored multiple-choice exam delivered by Oracle University and Pearson VUE, with a 68% passing score and a $245 USD attempt fee. The current 2025 blueprint weights Implement end-to-end Machine Learning Lifecycle at 45%, Apply MLOps Practices at 20%, Workspace Design and Configuration at 15%, OCI Data Science Introduction and Configuration at 10%, and Use related OCI Services at 10%, with heavy emphasis on the ADS SDK, Model Catalog, Model Deployment, and ML Pipelines.

Sample OCI Data Science Pro Practice Questions

Try these sample questions to test your OCI Data Science Pro exam readiness. Each question includes a detailed explanation. Start the interactive quiz above for the full 100+ question experience with AI tutoring.

1Which OCI Data Science resource serves as the top-level container that groups notebook sessions, models, jobs, and pipelines for a single team or initiative?

A.Project

B.Compartment

C.Tenancy

D.Application

Explanation: In OCI Data Science a Project is the logical grouping that holds notebook sessions, models in the Model Catalog, ML Jobs, and Pipelines for a workstream. Compartments are tenancy-wide IAM containers, tenancy is the root, and Application is a Functions concept, not a Data Science resource.

2Which IAM construct lets a notebook session call other OCI services without storing API key credentials in the notebook?

A.Resource principals

B.Local API key files

C.Auth tokens stored in the notebook

D.Console password authentication

Explanation: Resource principals issue short-lived signed tokens to OCI Data Science notebook sessions, jobs, model deployments, and pipelines so they can authenticate to other OCI services without persistent secrets. API keys, auth tokens, and console passwords would all require storing long-lived credentials in the notebook environment.

3An admin needs to grant a group of data scientists permission to manage every Data Science resource inside a single compartment. Which IAM policy statement is correct?

A.Allow group DataScientists to manage data-science-family in compartment ds-prod

B.Allow group DataScientists to inspect data-science-family in tenancy

C.Allow group DataScientists to use object-family in compartment ds-prod

D.Allow any-user to manage data-science-family in compartment ds-prod

Explanation: The aggregate verb manage on the data-science-family resource grants full permissions on Data Science notebook sessions, models, jobs, and pipelines, scoped to the named compartment. Inspect is too narrow, object-family is for Object Storage, and any-user opens the resource to the entire tenancy and the internet via instance principals.

4Which IAM resource type lets you target a notebook session by its OCID inside a matching rule so it can act as a principal in policies?

A.Dynamic group

B.Identity domain

C.Tenancy admin group

D.Tag namespace

Explanation: A dynamic group uses matching rules on resource OCIDs and tags to assemble OCI resources, including notebook sessions, jobs, model deployments, and pipelines, into a virtual group that can appear as a principal in IAM policies. Identity domains hold users and groups, tenancy admin groups are user groups, and tag namespaces only define tags.

5Which compartment design best supports separating production model training from experimentation while still letting both teams share a common Data Catalog?

A.Two sibling compartments for prod and dev under a parent compartment that holds shared services like Data Catalog

B.Place every Data Science resource in the root compartment for simplicity

C.Put prod, dev, and Data Catalog in three unrelated tenancies

D.Use a single compartment and rely solely on tagging for separation

Explanation: A parent compartment for shared services with sibling compartments for prod and dev gives clear IAM boundaries while allowing policies to grant both children read access to the shared Data Catalog. The root compartment lacks isolation, separate tenancies break sharing, and tag-only separation does not enforce IAM scope.

6Which statement about how a notebook session typically authenticates to OCI services is correct?

A.Inside a notebook session, the recommended approach is to use resource principal authentication so calls inherit the session's identity.

B.A notebook session must always use the user's API key fingerprint and private key file.

C.A notebook session can only call OCI services through the Console UI.

D.Notebook sessions cannot make outbound API calls to OCI services.

Explanation: Resource principal authentication is the recommended pattern for OCI Data Science notebook sessions because the session itself is the principal and inherits permissions through dynamic groups and policies. API keys, console-only access, and 'no outbound calls' descriptions are incorrect.

7Which combination of factors is the most important to confirm before launching a notebook session in a custom networking configuration?

A.The selected subnet has egress to OCI service endpoints and to any required external repositories

B.The subnet uses an oversize CIDR block

C.The notebook session has a public IP

D.The compartment has at least two block volume backups

Explanation: When a notebook session uses customer-managed networking, you must ensure the subnet can reach OCI service endpoints, plus PyPI, conda channels, or other external repos required by the workload. CIDR size, public IPs, and block volume backups do not by themselves enable the session to download packages or call OCI services.

8Which OCI Data Science feature group is most likely to require a dynamic group rule that matches resource.type = 'datasciencenotebooksession'?

A.Granting a notebook session permission to read from an Object Storage bucket via resource principals

B.Letting a human user log in to the OCI Console with single sign-on

C.Configuring federated identity with an external SAML provider

D.Creating a tag namespace for cost tracking

Explanation: To give notebook sessions least-privilege access to other OCI services, you typically create a dynamic group whose matching rule is resource.type = 'datasciencenotebooksession' and then write IAM policies that grant that dynamic group the needed permissions. The other options are unrelated to notebook session principals.

9Which Oracle service is the recommended default for storing secrets such as database passwords or third-party API keys that a notebook session needs at runtime?

A.OCI Vault

B.Object Storage with public access

C.Plaintext environment variables in the notebook

D.Hardcoded values in a Git-tracked file

Explanation: OCI Vault stores encrypted secrets and integrates with IAM so a notebook session, via resource principals or a dynamic group, can fetch a password or API key at runtime without persisting it. Public Object Storage, plaintext env vars, and hardcoded secrets all leak credentials.

10An organization wants to enforce that all OCI Data Science notebook sessions are created in only one specific region for data residency. Which approach is most effective?

A.Use IAM policies with a 'where request.region = ...' condition and tagging policies to restrict notebook session creation

B.Rely on a runbook that asks engineers to choose the right region

C.Disable the OCI Data Science service everywhere except that region

D.Use Object Storage replication to enforce region-only access

Explanation: IAM policy conditions like where request.region = 'us-ashburn-1' combined with tag-based policies can enforce that notebook session creation requests only succeed in the chosen region. Runbooks rely on humans, services cannot be selectively disabled per region, and Object Storage replication is unrelated to where notebook sessions are created.

About the OCI Data Science Pro Exam

The OCI 2025 Data Science Professional exam (1Z0-1110-25) validates your ability to design and operate end-to-end machine learning workflows on Oracle Cloud Infrastructure. It tests OCI Data Science workspace setup, notebook sessions, conda environments, the Accelerated Data Science (ADS) Python SDK, Model Catalog, Model Deployment, ML Jobs, Pipelines, MLOps practices, and integration with related OCI services such as Data Flow, Functions, Vault, IAM, and Logging.

Assessment

50 multiple-choice questions

Time Limit

90 minutes

Passing Score

68%

Exam Fee

$245 USD (Oracle)

OCI Data Science Pro Exam Content Outline

10%

OCI Data Science Introduction and Configuration

OCI Data Science core components, console navigation, project and notebook session lifecycle, IAM compartments, dynamic groups, resource principals vs API key authentication, and tenancy networking prerequisites.

15%

Workspace Design and Configuration

Notebook session shapes and storage, custom networking with VCN egress, conda environments and bring-your-own conda packs, GPU sessions, Object Storage buckets for artifacts, and Vault for secrets and notebook credentials.

45%

Implement End-to-End Machine Learning Lifecycle

Data ingestion, feature engineering, model training and evaluation with the Accelerated Data Science (ADS) Python SDK, Model Catalog versioning and metadata, Model Deployment HTTP endpoints, AI Quick Actions, and ONNX export.

20%

Apply MLOps Practices

ML Jobs for repeatable training and batch inference, Pipelines with steps, artifacts, and conditional branching, Model Deployment scaling and BYOC images, monitoring with Logging and Alarms, and MLOps with OCI DevOps.

10%

Use Related OCI Services

Object Storage lifecycle, Data Flow Spark jobs, Data Catalog, Data Integration, Streaming for online inference, Functions for lightweight inference, OCI Generative AI service integration patterns, and cost optimization.

How to Pass the OCI Data Science Pro Exam

What You Need to Know

Passing score: 68%
Assessment: 50 multiple-choice questions
Time limit: 90 minutes
Exam fee: $245 USD

Keys to Passing

Complete 500+ practice questions
Score 80%+ consistently before scheduling
Focus on highest-weighted sections
Use our AI tutor for tough concepts

OCI Data Science Pro Study Tips from Top Performers

1Spend the most time on the Machine Learning Lifecycle domain because it carries 45% of the exam and ties together notebook sessions, ADS, Model Catalog, and Model Deployment.

2Build at least one end-to-end project in OCI: notebook session, ADS-trained model, Model Catalog entry, Model Deployment HTTP endpoint, and a Pipeline that retrains the model on a schedule.

3Memorize the difference between API key auth and resource principals, and know which one notebook sessions, Jobs, and Model Deployments default to when calling other OCI services.

4Practice writing IAM policies that grant a dynamic group of notebook sessions access to a specific Object Storage bucket and to read secrets from OCI Vault.

5Know when to choose Model Deployment, ML Jobs, and Functions for inference: HTTP real-time, scheduled or batch, and lightweight serverless respectively.

6Be fluent with conda packs: when to use a service-published pack, when to publish your own to Object Storage, and how the bring-your-own-conda flow works in notebook sessions and Jobs.

7Review ML Pipelines step types, artifact passing, and conditional branching, since pipeline orchestration questions are common at the professional level.

8Cross-train on the boundary services: Data Flow for Spark, Data Integration for ETL, Streaming for online inference inputs, and Logging plus Alarms for Model Deployment monitoring.

Frequently Asked Questions

What is the OCI 2025 Data Science Professional exam?

The OCI 2025 Data Science Professional exam (1Z0-1110-25) is Oracle's professional-level certification for data scientists and ML engineers who build, deploy, and operate machine learning solutions on Oracle Cloud Infrastructure. It validates skills with the OCI Data Science service, the Accelerated Data Science (ADS) SDK, Model Catalog, Model Deployment, ML Jobs, Pipelines, and integration with related OCI services such as Data Flow, Functions, Vault, IAM, Logging, and Object Storage.

How many questions are on the exam and how long do you have?

The 1Z0-1110-25 exam is 50 multiple-choice questions delivered in a 90-minute time window. The passing score is 68%, and the exam is proctored by Pearson VUE either in a test center or through OnVUE remote delivery.

How much does the OCI Data Science Professional exam cost?

The standard attempt fee for the OCI 2025 Data Science Professional exam is $245 USD per Oracle's published pricing for core OCI associate and professional certifications, with regional pricing variations possible. Oracle does not publish public exam-level pass-rate percentages.

Which domain carries the most weight on the exam?

Implement End-to-End Machine Learning Lifecycle is the largest domain at 45% of the exam. It covers data ingestion, feature engineering, training, evaluation, the ADS SDK, Model Catalog versioning, Model Deployment HTTP endpoints, AI Quick Actions, and ONNX export. Plan to spend the most study time here.

What experience should you have before taking 1Z0-1110-25?

Oracle positions this as a professional-level exam, so candidates should already be comfortable with Python, scikit-learn or similar ML frameworks, and have hands-on experience using OCI Data Science notebook sessions, Model Catalog, Model Deployment, and at least one Pipeline or ML Job. Familiarity with OCI IAM, compartments, and Object Storage is assumed.

How is OCI Data Science Professional different from OCI AI Foundations?

OCI AI Foundations (1Z0-1122-25) is an associate-level exam that tests broad concepts across AI, ML, deep learning, and the OCI AI services portfolio. The Data Science Professional exam (1Z0-1110-25) is deeper and narrower, focused specifically on building, deploying, and operationalizing custom ML models with the OCI Data Science service, ADS SDK, Model Deployment, ML Jobs, and Pipelines.