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User-Friendly Oracle 1z0-1110-25 Exam Questions in PDF Format

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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

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 4

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 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 (Q158-Q163):

NEW QUESTION # 158
What is the minimum active storage duration for logs used by Logging Analytics to be archived?

A. 60 days
B. 15 days
C. 30 days
D. 10 days

Answer: C

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Determine minimum log storage duration before archiving in Logging Analytics.
* Understand Logging Analytics: Logs are active before archival.
* Evaluate Options:
* A: 60 days-Too long for minimum.
* B: 10 days-Too short.
* C: 30 days-Standard minimum-correct.
* D: 15 days-Not OCI's default.
* Reasoning: 30 days is OCI's documented minimum active period.
* Conclusion: C is correct.
OCI documentation states: "Logs in Logging Analytics remain active for a minimum of 30 days (C) before archiving, ensuring availability for analysis." B and D are shorter, A is longer-only C matches OCI's policy.
Oracle Cloud Infrastructure Logging Analytics Documentation, "Log Retention".

NEW QUESTION # 159
Which OCI cloud service lets you centrally manage the encryption keys that protect your data and the secret credentials that you use to securely access resources?

A. Data Guard
B. Cloud Guard
C. Vault
D. Data Safe

Answer: C

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify the OCI service for key and secret management.
* Evaluate Options:
* A: Data Safe-Database security, not key management.
* B: Cloud Guard-Threat detection, not keys.
* C: Data Guard-DB replication, not keys.
* D: Vault-Key and secret management-correct.
* Reasoning: Vault is OCI's dedicated service for crypto keys and secrets.
* Conclusion: D is correct.
OCI documentation states: "OCI Vault (D) centrally manages encryption keys and secrets, securing data and resource access." A, B, and C serve other purposes-only D matches per OCI's securityservices.
Oracle Cloud Infrastructure Vault Documentation, "Overview".

NEW QUESTION # 160
Which of these is a unique feature of the published conda environment?

A. Allows you to save the conda environment in a block volume
B. Allows you to save the conda environment to an Object Storage Bucket
C. Provides a comprehensive environment to solve business use cases
D. Provides availability on network session reactivation

Answer: B

Explanation:
Detailed Answer in Step-by-Step Solution:
* Understand Published Conda Environments: In OCI Data Science, these are custom conda environments shared across users via Object Storage.
* Evaluate Options:
* A: Vague-All conda environments can address use cases; not unique to "published."
* B: Incorrect-Availability on reactivation applies to session persistence, not publishing.
* C: Correct-Publishing saves the environment to Object Storage for sharing/reuse.
* D: Incorrect-Block volumes store session data, not published environments.
* Reasoning: The unique aspect of "published" environments is their storage in Object Storage (via odsc conda publish), enabling team access.
* Conclusion: C is the distinctive feature.
The OCI Data Science documentation highlights that "published conda environments are saved to an OCI Object Storage Bucket, allowing them to be shared across notebook sessions and users." This distinguishes C from A (generic), B (session-related), and D (block volume is for session state, not publishing). Publishing to Object Storage is the defining trait per Oracle's design.
Oracle Cloud Infrastructure Data Science Documentation, "Managing Conda Environments - Publishing" section.

NEW QUESTION # 161
What is the name of the machine learning library used in Apache Spark?

A. MLib
B. Structured Streaming
C. HadoopML
D. GraphX

Answer: A

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify Apache Spark's ML library.
* Understand Spark: A big data framework with specialized libraries.
* Evaluate Options:
* A: MLib (correctly MLlib)-Spark's machine learning library.
* B: GraphX-Graph processing, not ML.
* C: Structured Streaming-Streaming data, not ML.
* D: HadoopML-Not a Spark library (Hadoop-related).
* Reasoning: MLlib is Spark's official ML toolkit (e.g., regression, clustering).
* Conclusion: A is correct (noting "MLib" should be "MLlib").
OCI Data Science supports Spark via Data Flow, where "MLlib (Machine Learning library) provides scalable ML algorithms." GraphX (B) and Structured Streaming (C) serve other purposes, and HadoopML (D) isn't real-MLlib (A) is the standard, despite the typo.
Oracle Cloud Infrastructure Data Flow Documentation, "Apache Spark MLlib".

NEW QUESTION # 162
You are working in your notebook session and find that your notebook session does not have enough compute CPU and memory for your workload. How would you scale up your notebook session without losing your work?

A. Download your files and data to your local machine, delete your notebook session, provision a new notebook session on a larger compute shape, and upload your files from your local machine to the new notebook session
B. Ensure your files and environments are written to the block volume storage under the /home
/datascience directory, deactivate the notebook session, and activate the notebook with a larger compute shape selected
C. Deactivate your notebook session, provision a new notebook session on a larger compute shape, and recreate all your file changes
D. Create a temporary bucket in Object Storage, write all your files and data to Object Storage, delete the notebook session, provision a new notebook session on a larger compute shape, and copy your files and data from your temporary bucket to your new notebook session

Answer: B

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Scale up a notebook session without losing work.
* Understand Persistence: Block volume stores session data (e.g., /home/datascience).
* Evaluate Options:
* A: Recreating work-inefficient, risks loss.
* B: Local download/upload-cumbersome, unnecessary.
* C: Use block volume persistence, scale up-efficient, preserves work-correct.
* D: Object Storage-extra steps, not needed with block volume.
* Reasoning: C leverages OCI's built-in persistence for seamless scaling.
* Conclusion: C is correct.
OCI documentation states: "Files in /home/datascience are stored on the block volume. To scale up, deactivate the session, provision a new one with a larger shape, and the block volume persists your work." A loses data, B and D add complexity-only C is optimal.
Oracle Cloud Infrastructure Data Science Documentation, "Scaling Notebook Sessions".

NEW QUESTION # 163
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