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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

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 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

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.

Oracle Cloud Infrastructure 2025 Data Science Professional Sample Questions (Q41-Q46):

NEW QUESTION # 41
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. OCI Vault
B. Data Science Artifactory
C. Model Catalog
D. Model Depository

Answer: C

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 # 42
Which function's objective is to represent the difference between the predictive value and the target value?

A. Update function
B. Cost function
C. Fit function
D. Optimizer function

Answer: B

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify the function that measures the difference between predicted and actual values in machine learning.
* Understand ML Functions:
* Optimizer function: Adjusts model parameters to minimize error (e.g., gradient descent)-it uses the cost, not defines it.
* Fit function: Trains the model by fitting it to data-process-oriented, not a measure.
* Update function: Typically updates weights during training-not a standard term for error measurement.
* Cost function: Quantifies prediction error (e.g., MSE, cross-entropy)-directly represents the difference.
* Evaluate Options:
* A: Optimizer minimizes the cost, not the cost itself-incorrect.
* B: Fit executes training, not error definition-incorrect.
* C: Update is vague and not a standard ML term for this-incorrect.
* D: Cost function (e.g., loss) measures prediction vs. target-correct.
* Reasoning: The cost function (or loss function) is the mathematical representation of error, guiding optimization.
* Conclusion: D is the correct answer.
In OCI Data Science, the documentation explains: "The cost function (or loss function) measures the difference between the model's predicted values and the actual target values, such as mean squared error for regression or cross-entropy for classification." Optimizers (A) use this to adjust weights, fit (B) is a training step, and update (C) isn't a defined function here-only the cost function (D) fits the description. This aligns with standard ML terminology and OCI's AutoML processes.
Oracle Cloud Infrastructure Data Science Documentation, "Machine Learning Concepts - Cost Functions".

NEW QUESTION # 43
Which is NOT a compliance document?

A. Bridge letter
B. Certificate
C. Attestation
D. Penetration test report

Answer: D

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify a non-compliance document in OCI context.
* Understand Compliance Docs: Formal attestations of adherence (e.g., SOC, ISO).
* Evaluate Options:
* A: Certificate-Proof of compliance (e.g., ISO)-compliance doc.
* B: Pen test report-Security test result, not formal compliance-correct.
* C: Attestation-Statement of compliance-compliance doc.
* D: Bridge letter-Links audit periods-compliance doc.
* Reasoning: B is operational, not a compliance artifact.
* Conclusion: B is correct.
OCI documentation lists "compliance documents like certificates (A), attestations (C), and bridge letters (D) for standards like SOC or ISO; penetration test reports (B) are security assessments, not formal compliance docs." Only B stands apart per OCI's compliance terminology.
Oracle Cloud Infrastructure Compliance Documentation, "Compliance Artifacts".

NEW QUESTION # 44
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 # 45
During a job run, you receive an error message that no space is left on your disk device. To solve the problem, you must increase the size of the job storage. What would be the most efficient way to do this with Data Science Jobs?

A. On the job run, set the environment variable that helps increase the size of the storage
B. Edit the job, change the size of the storage of your job, and start a new job run
C. Create a new job with increased storage size and then run the job
D. Your code using too much disk space. Refactor the code to identify the problem

Answer: C

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Efficiently increase storage for an OCI Job.
* Understand Jobs: Storage (block volume) is set at job creation, not dynamically adjustable.
* Evaluate Options:
* A: False-Jobs can't edit storage post-creation; it's fixed.
* B: False-No environment variable adjusts storage size.
* C: True-Create a new job with larger storage (e.g., 200 GB) and run it.
* D: False-Refactoring code is inefficient compared to increasing storage.
* Reasoning: C is the standard OCI process for adjusting resources.
* Conclusion: C is correct.
OCI documentation states: "Storage size for a Data Science Job is specified during job creation (e.g., block volume size). To increase it, create a new job with a larger storage configuration and initiate a new run." Editing (A) isn't supported, variables (B) don't apply, and refactoring (D) avoids the issue-only C is efficient.
Oracle Cloud Infrastructure Data Science Documentation, "Jobs - Storage Configuration".

NEW QUESTION # 46
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