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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	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 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	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 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 (Q89-Q94):

NEW QUESTION # 89
In machine learning, what is the primary difference between supervised and unsupervised learning?

A. Supervised learning is monitored closely by data scientists, while they don't play a role inunsupervised learning.
B. Supervised learning is created and managed by the Data Engineer.
C. Supervised learning is only used for image recognition, while unsupervised learning can be used for various analytics applications.
D. Supervised learning involves data that has been labeled and classified, while unsupervised learning data is unlabeled and unclassified.

Answer: D

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify the key difference between supervised and unsupervised learning.
* Define Types:
* Supervised: Uses labeled data (e.g., input-output pairs) to predict outcomes.
* Unsupervised: Uses unlabeled data to find patterns (e.g., clustering).
* Evaluate Options:
* A: Labeled vs. unlabeled-Core distinction, correct.
* B: Monitoring-Misleading, not the primary difference.
* C: Image recognition-False, supervised applies broadly.
* D: Data Engineer-Irrelevant to learning type.
* Reasoning: A captures the foundational data difference.
* Conclusion: A is correct.
OCI documentation states: "Supervised learning uses labeled data to train models for prediction, while unsupervised learning analyzes unlabeled data to discover patterns." B, C, and D misrepresent this-only A aligns with OCI's ML definitions and industry standards.
Oracle Cloud Infrastructure Data Science Documentation, "Machine Learning Types".

NEW QUESTION # 90
Which statement about logs for Oracle Cloud Infrastructure Jobs is true?

A. All stdout and stderr are automatically stored when automatic log creation is enabled
B. Logs are automatically deleted when the job and job run is deleted
C. Each job run sends outputs to a single log for that job
D. Integrating data science jobs resources with logging is mandatory

Answer: A

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify a true statement about OCI Jobs logging.
* Understand Logging: Jobs can log stdout/stderr to OCI Logging service.
* Evaluate Options:
* A: False-Each run has its own log, not a single job log.
* B: False-Logging is optional, not mandatory.
* C: True-When enabled, stdout/stderr are auto-captured.
* D: False-Logs persist unless explicitly deleted.
* Reasoning: C matches OCI's automatic logging feature.
* Conclusion: C is correct.
OCI documentation states: "When automatic log creation is enabled for Data Science Jobs, all stdout and stderr outputs are captured and stored in the OCI Logging service." A is incorrect (per-run logs), B is optional, and D contradicts log retention-only C is accurate.
Oracle Cloud Infrastructure Data Science Documentation, "Jobs Logging".

NEW QUESTION # 91
For your next data science project, you need access to public geospatial images. Which Oracle Cloud service provides free access to those images?

A. Oracle Open Data
B. Oracle Analytics Cloud
C. Oracle Big Data Service
D. Oracle Cloud Infrastructure (OCI) Data Science

Answer: A

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Find the OCI service for free geospatial images.
* Evaluate Options:
* A: Big Data Service-Spark processing, not datasets.
* B: Analytics Cloud-Visualization, not data source.
* C: Data Science-ML platform, not dataset provider.
* D: Open Data-Free public datasets, including geospatial-correct.
* Reasoning: Open Data is OCI's public dataset hub.
* Conclusion: D is correct.
OCI documentation states: "Oracle Open Data provides free access to curated datasets, including geospatial images, for public use." A, B, and C serve other purposes-only D delivers free geospatial data.
Oracle Cloud Infrastructure Open Data Documentation, "Dataset Offerings".

NEW QUESTION # 92
You have an embarrassingly parallel or distributed batch job with a large amount of data running using Data Science Jobs. What would be the best approach to run the workload?

A. Reconfigure the job run because Data Science Jobs does not support embarrassingly parallel
B. Create a job in Data Science Jobs and then start the number of simultaneous job runs required for your workload
C. Create a new job for every job run that you have to run in parallel, because the Data Science Job service can have only one job per job
D. Create the job in Data Science Jobs and start a job run. When it is done, start a new job run until you achieve the number of runs required

Answer: B

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Optimize an embarrassingly parallel job in OCI Data Science Jobs.
* Define Embarrassingly Parallel: Tasks are independent, ideal for simultaneous runs.
* Evaluate Options:
* A: Multiple simultaneous runs-Leverages parallelism-correct.
* B: One job per run-Misstates capability; unnecessary complexity.
* C: Sequential runs-Inefficient, ignores parallelism.
* D: False-Jobs support parallelism.
* Reasoning: A maximizes efficiency for parallel tasks.
* Conclusion: A is correct.
OCI documentation states: "For embarrassingly parallel workloads, create a single Job and launch multiple simultaneous Job Runs to process data in parallel." B misinterprets limits, C wastes time, and D denies capability-only A fits OCI's design.
Oracle Cloud Infrastructure Data Science Documentation, "Parallel Job Runs".

NEW QUESTION # 93
Select two reasons why it is important to rotate encryption keys when using Oracle Cloud Infrastructure (OCI) Vault to store credentials or other secrets.

A. Key rotation improves encryption efficiency
B. Periodically rotating keys makes it easier to reuse keys
C. Key rotation allows you to encrypt no more than five keys at a time
D. Key rotation reduces risk if a key is ever compromised
E. Periodically rotating keys limits the amount of data encrypted by one key version

Answer: D,E

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify two reasons for key rotation in OCI Vault.
* Understand Key Rotation: Enhances security by updating keys.
* Evaluate Options:
* A: Five-key limit-False, no such restriction.
* B: Efficiency-False, not the purpose.
* C: Reuse-False, rotation prevents reuse.
* D: Reduces risk-True, limits exposure-correct.
* E: Limits data-True, reduces breach scope-correct.
* Reasoning: D and E are security-focused-key Vault benefits.
* Conclusion: D and E are correct.
OCI documentation states: "Key rotation in Vault (D) reduces risk if a key is compromised and (E) limits the data encrypted by a single key version, enhancing security." A, B, and C misrepresent rotation's purpose- only D and E align with OCI's Vault best practices.
Oracle Cloud Infrastructure Vault Documentation, "Key Rotation Benefits".

NEW QUESTION # 94
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EXAM 1Z0-1110-25 SIMULATIONS, RELIABLE 1Z0-1110-25 BRAINDUMPS FILES

Exam 1z0-1110-25 Simulations, Reliable 1z0-1110-25 Braindumps Files