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Oracle 1z0-1110-25 Exam Syllabus Topics:

Topic
Details

Topic 1

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 2

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 3

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 4

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 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 (Q159-Q164):

NEW QUESTION # 159
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. Updating the local repo to match the content from a remote repository
B. Converting an existing local project folder to a Git repository
C. Moving the changes into staging area for the next commit
D. Making a commit that is taking a snapshot of the local repository for the next push
E. Pushing changes to a remote repository

Answer: A,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 # 160
You have created a model and want to use Accelerated Data Science (ADS) SDK to deploy the model. Where are the artifacts to deploy this model with ADS?

A. Data Science Artifactory
B. Model Catalog
C. Model Depository
D. OCI Vault

Answer: B

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Locate artifacts for ADS model deployment.
* Understand ADS Deployment: Requires model artifacts (e.g., score.py) stored in OCI.
* Evaluate Options:
* A: Vault-Stores secrets, not models.
* B: Depository-Not an OCI term.
* C: Model Catalog-Stores models/artifacts for deployment-correct.
* D: Artifactory-Not an OCI service.
* Reasoning: Model Catalog is OCI's model repository for ADS.
* Conclusion: C is correct.
OCI documentation states: "ADS SDK deploys models from the Model Catalog, where trainedmodels and artifacts (e.g., score.py) are stored." Vault (A) is for secrets, B and D aren't real-only C supports ADS deployment.
Oracle Cloud Infrastructure Data Science Documentation, "ADS Model Deployment".

NEW QUESTION # 161
Which Security Zone policy is NOT valid?

A. A boot volume can be moved from a security zone to a standard compartment
B. Resources in a security zone should not be accessible from the public internet
C. Resources in a security zone must be automatically backed up regularly
D. A compute instance cannot be moved from a security zone to a standard compartment

Answer: A

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Spot the invalid Security Zone policy.
* Understand Security Zones: Enforce strict security-resources can't leave easily.
* Evaluate Options:
* A: Boot volume move-Not allowed; violates zone rules-invalid.
* B: Compute no-move-True, enforces security.
* C: No public access-True, standard policy.
* D: Auto-backup-True, required for compliance.
* Reasoning: A contradicts Security Zone immutability.
* Conclusion: A is incorrect.
OCI documentation states: "Resources like boot volumes (A) cannot be moved from a Security Zone to a standard compartment-B, C, and D are enforced policies." A violates OCI's Security Zone restrictions- only A is not valid.
Oracle Cloud Infrastructure Security Zones Documentation, "Policy Restrictions".

NEW QUESTION # 162
What does the Data Science Service template in Oracle Resource Manager (ORM) NOTautomatically create?

A. Dynamic groups
B. Policies for a basic use case
C. Required user groups
D. Individual Data Science users

Answer: D

Explanation:
Detailed Answer in Step-by-Step Solution:
* Understand ORM Template: It automates OCI Data Science setup with predefined configurations.
* Evaluate Components:
* A: User groups are created for role-based access-automated.
* B: Dynamic groups (e.g., for notebook sessions) are included-automated.
* C: Individual users require manual creation via IAM-not automated.
* D: Basic policies (e.g., access to Data Science resources) are included-automated.
* Reasoning: ORM focuses on infrastructure and permissions, not user accounts.
* Conclusion: C is the exception.
The OCI Resource Manager template for Data Science "automatically provisions user groups, dynamic groups, and policies for basic use cases," but "individual users must be created separately in IAM and assigned to groups." C is the only item not handled by the template, per the documentation.
Oracle Cloud Infrastructure Resource Manager Documentation, "Data Science Template".

NEW QUESTION # 163
You are working as a Data Scientist for a healthcare company. You have a series of neurophysiological data on OCI Data Science and have developed a convolutional neural network (CNN) classification model. It predicts the source of seizures in drug-resistant epileptic patients. You created a model artifact with all the necessary files. When you deployed the model, it failed to run because you did not point to the correct conda environment in the model artifact. Where would you provide instructions to use the correct conda environment?

A. requirements.txt
B. runtime.yaml
C. score.py
D. model_artifact_validate.py

Answer: B

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Determine where to specify the conda environment for an OCI model deployment.
* Understand Model Deployment: Requires artifacts like score.py and runtime.yaml to define runtime settings.
* Evaluate Options:
* A. score.py: Contains inference logic (e.g., load_model(), predict())-not for environment specs.
* B. runtime.yaml: Defines deployment runtime, including conda environment path-correct.
* C. requirements.txt: Lists pip dependencies-not used in OCI for conda environments.
* D. model_artifact_validate.py: Not a standard artifact; doesn't exist in OCI deployment.
* Reasoning: runtime.yaml specifies the conda env (e.g., slug: pyspark30_p37_cpu_v2)-failure to set this causes deployment errors.
* Conclusion: B is correct.
OCI documentation states: "The runtime.yaml file in a model artifact specifies the runtime environment, including the conda environment path (e.g., ENVIRONMENT_SLUG: pyspark30_p37_cpu_v2), ensuring the deployed model uses the correct dependencies." score.py (A) handles inference, requirements.txt (C) is for pip (not conda in OCI), and D isn't valid-only B addresses the conda issue per OCI's deployment process.
Oracle Cloud Infrastructure Data Science Documentation, "Model Deployment - runtime.yaml".

NEW QUESTION # 164
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