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Oracle 1Z0-1110-26 Exam Syllabus Topics:

Section	Objectives
Topic 1: Apply MLOps Practices	- Use best practices for operationalizing ML solutions - Monitor and maintain machine learning models - Implement model lifecycle management
Topic 2: Use Related OCI Services	- Apply OCI services for data ingestion, storage, and processing - Design machine learning solutions for business use cases - Integrate OCI Data and AI services
Topic 3: Design and Set Up Data Science Workspace	- Manage notebook sessions and compute resources - Create and configure Data Science projects - Use Accelerated Data Science SDK and open source tools
Topic 4: Implement End-to-End Machine Learning Lifecycle	- Save and manage models using Model Catalog - Build, train, and evaluate machine learning models - Deploy models and consume model endpoints - Automate machine learning workflows and pipelines - Prepare and manage datasets
Topic 5: OCI Data Science - Introduction and Configuration	- Use OCI Data Science notebooks and sessions - Configure and manage Data Science resources - Understand OCI Data Science service concepts and architecture

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Oracle Cloud Infrastructure Data Science Professional Sample Questions (Q57-Q62):

NEW QUESTION # 57
Which two statements are true about published conda environments?

A. They are curated by Oracle Cloud Infrastructure (OCI) Data Science
B. In addition to service job run environment variables, conda environment variables can be used inData Science Jobs
C. The odsc conda init command is used to configure the location of published conda environments
D. Your notebook session acts as the source to share published conda environments with team members
E. You can only create a published conda environment by modifying a Data Science conda environment

Answer: B,C

Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify true statements about published conda environments in OCI.
Understand Published Conda: Custom envs shared via Object Storage.
Evaluate Options:
A: False—Users create them, not OCI-curated.
B: True—odsc conda init sets up access to published envs.
C: False—Object Storage, not notebook, is the source.
D: False—Can create from scratch, not just modifications.
E: True—Conda env vars extend job configs.
Reasoning: B configures access, E enhances flexibility—match OCI behavior.
Conclusion: B and E are correct.
OCI documentation states: “odsc conda init (B) configures the bucket for published conda environments, and Jobs can use conda environment variables (E) alongside service vars.” A misattributes curation, C misplaces source, and D limits creation—only B and E are true per OCI’s conda management.
1: Oracle Cloud Infrastructure Data Science Documentation, "Published Conda Environments".

NEW QUESTION # 58
You have a complex Python code project that could benefit from using Data Science Jobs as it is a repeatable machine learning model training task. The project contains many sub-folders and classes. What is the best way to run this project as a Job?

A. ZIP the entire code project folder and upload it as a Job artifact on job creation. Jobs identifies the main executable file automatically
B. ZIP the entire code project folder, upload it as a Job artifact on job creation, and set JOB_RUN_ENTRYPOINT to point to the main executable file
C. Rewrite your code so that it is a single executable Python or Bash/Shell script file
D. ZIP the entire code project folder and upload it as a Job artifact. Jobs automatically identifies the main top-level where the code is run

Answer: B

Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Run a complex Python project as an OCI Job.
Evaluate Options:
A: Auto-identification—False; entrypoint must be set.
B: Rewrite—Unnecessary, inefficient.
C: Auto-executable—False; needs explicit entrypoint.
D: ZIP with entrypoint—Correct, flexible approach.
Reasoning: D preserves structure, specifies execution.
Conclusion: D is correct.
OCI documentation states: “For complex projects, ZIP the folder and upload as a Job artifact, then set JOB_RUN_ENTRYPOINT (D) to the main executable (e.g., main.py).” Auto-detection (A, C) isn’t supported, and B discards structure—D is best.
1: Oracle Cloud Infrastructure Data Science Documentation, "Job Artifacts".

NEW QUESTION # 59
What happens when a notebook session is deactivated?

A. Compute cost increases due to frequent deactivation
B. The underlying compute instance stops
C. The block volume attached to the notebook is permanently deleted
D. The data on boot volume is preserved

Answer: B

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.
1: Oracle Cloud Infrastructure Data Science Documentation, "Notebook Session Lifecycle".

NEW QUESTION # 60
Which is NOT a valid OCI Data Science notebook session approach?

A. Run the process directly in the terminal and use Python logging to get updates on the progress of your job
B. Authenticate using your notebook session’s resource principal to access other OCI resources. Resource principals provide a more secure way to authenticate to resources compared to the OCI configuration and API approach
C. Avoid having multiple users in the same notebook session due to the possibility of resource contention and write conflicts
D. Ensure you don’t execute long-running Python processes in a notebook cell
E. While connecting to data in OCI Object Storage from your notebook session, the best practice is to make a local copy on the device and then upload it to your notebook session block volume

Answer: E

Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify an invalid notebook session practice.
Evaluate Options:
A: Valid—Long runs should use Jobs, not notebooks.
B: Valid—Terminal runs with logging are supported.
C: Valid—Multi-user conflicts are a concern.
D: Invalid—Copying from Object Storage to block volume is unnecessary; direct access is best.
E: Valid—Resource principals are secure and recommended.
Reasoning: D contradicts OCI’s direct-access efficiency.
Conclusion: D is incorrect.
OCI documentation advises: “Access data in Object Storage directly from notebook sessions using SDKs or resource principals—avoid local copies (D) unless necessary.” A, B, C, and E are best practices—D is inefficient and not standard.
1: Oracle Cloud Infrastructure Data Science Documentation, "Notebook Session Best Practices".

NEW QUESTION # 61
Which feature of Oracle Cloud Infrastructure Data Science provides an interactive coding environment for building and training machine learning models?

A. Notebook Sessions
B. Jobs
C. Projects
D. Model Catalog

Answer: A

Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify the interactive coding environment in OCI Data Science.
Evaluate Options:
A: Model Catalog stores models—not for coding.
B: Jobs run predefined tasks—not interactive.
C: Notebook Sessions provide JupyterLab for coding and training—interactive.
D: Projects organize work—not a coding environment.
Reasoning: Notebook Sessions are OCI’s Jupyter-based tool for interactive ML development.
Conclusion: C is correct.
OCI Data Science Notebook Sessions “provide an interactive JupyterLab environment where data scientists can write code, explore data, and train machine learning models.” Model Catalog (A) is for storage, Jobs (B) for automation, and Projects (D) for organization—only C offers interactivity.
1: Oracle Cloud Infrastructure Data Science Documentation, "Notebook Sessions Overview".

NEW QUESTION # 62
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