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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	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 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	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 (Q150-Q155):

NEW QUESTION # 150
In machine learning, what is the primary difference between supervised and unsupervised learning?

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

Answer: B

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 # 151
You have received machine learning model training code, without clear information about the optimal shape to run the training. How would you proceed to identify the optimal compute shape for your model training that provides a balanced cost and processing time?

A. Start with the strongest compute shape Jobs support and monitor the Job Run metrics and time required to complete the model training. Tune the model so that it utilizes as much compute resources as possible, even at an increased cost
B. Start with a random compute shape and monitor the utilization metrics and time required to finish the model training. Perform model training optimizations and performance tests in advance to identify the right compute shape before running the model training as a job
C. Start with a smaller shape and monitor the Job Run metrics and time required to complete the model training. If the compute shape is not fully utilized, tune the model parameters, and rerun the job. Repeat the process until the shape resources are fully utilized
D. Start with a smaller shape and monitor the utilization metrics and time required to complete the model training. If the compute shape is fully utilized, change to compute that has more resources and rerun the job. Repeat the process until the processing time does not improve

Answer: D

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Optimize compute shape for cost and time.
* Evaluate Options:
* A: Tuning params-Focuses on model, not shape.
* B: Strongest shape-Costly, unbalanced.
* C: Scale up when utilized-Balances cost/time-correct.
* D: Random start-Unsystematic.
* Reasoning: C iteratively optimizes based on utilization.
* Conclusion: C is correct.
OCI documentation advises: "Start with a small shape, monitor utilization and time (C); scale up if fully utilized until performance stabilizes-optimizes cost and speed." A misfocuses, B overspends, D lacks method-only C aligns.
Oracle Cloud Infrastructure Data Science Documentation, "Compute Shape Optimization".

NEW QUESTION # 152
The Oracle AutoML pipeline automates hyperparameter tuning by training the model with different parameters in parallel. You have created an instance of Oracle AutoML as oracle_automl and now you want an output with all the different trials performed by Oracle AutoML. Which of the following commands gives you the results of all trials?

A. oracle_automl.visualize_adaptive_sampling_trials()
B. oracle_automl.visualize_tuning_trials()
C. oracle_automl.print_trials()
D. oracle_automl.visualize_algorithm_selection_trials()

Answer: C

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Get all AutoML trial results.
* Understand AutoML: Trials include hyperparameter tuning outcomes.
* Evaluate Options:
* A: print_trials()-Displays all trial results-correct.
* B: visualize_tuning_trials()-Visualizes tuning, not full list.
* C: visualize_adaptive_sampling_trials()-Specific to sampling, not all trials.
* D: visualize_algorithm_selection_trials()-Specific to algorithms, not all.
* Reasoning: A provides comprehensive trial output.
* Conclusion: A is correct.
OCI AutoML documentation states: "print_trials() outputs a table of all trials performed, including hyperparameters and scores." Visualization methods (B, C, D) focus on specific aspects-only A gives the full list.
Oracle Cloud Infrastructure AutoML Documentation, "Trial Output Methods".

NEW QUESTION # 153
You are a data scientist working inside a notebook session and you attempt to pip install a package from a public repository that is not included in your conda environment. After running this command, you get a network timeout error. What might be missing from your networking configuration?

A. Primary Virtual Network Interface Card (VNIC)
B. Service Gateway with private subnet access
C. FastConnect to an on-premises network
D. NAT Gateway with public internet access

Answer: D

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Fix network timeout for pip install in a notebook.
* Evaluate Options:
* A: FastConnect-On-premises link, not public internet.
* B: VNIC-Default, not the issue.
* C: NAT Gateway-Grants internet access-correct.
* D: Service Gateway-OCI services, not PyPI.
* Reasoning: C enables outbound traffic to public repos.
* Conclusion: C is correct.
OCI documentation states: "A NAT Gateway (C) is required for notebook sessions in private subnets to access public internet repositories like PyPI." A, B, and D don't provide this-only C resolves the timeout.
Oracle Cloud Infrastructure Data Science Documentation, "Notebook Networking".

NEW QUESTION # 154
Which statement about Oracle Cloud Infrastructure Data Science Jobs is true?

A. Jobs comes with a set of standard tasks that cannot be customized
B. You must use a single Shell/Bash or Python artifact to run a job
C. You must create and manage your own Jobs infrastructure
D. Jobs provisions the infrastructure to run a process on-demand

Answer: D

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify a true statement about OCI Data Science Jobs.
* Understand OCI Jobs: Jobs automate ML tasks (e.g., training) on managed infrastructure.
* Evaluate Options:
* A: True-Jobs provision OCI compute resources on-demand for task execution.
* B: False-Users define custom tasks (e.g., Python scripts), not limited to standard ones.
* C: False-Infrastructure is fully managed by OCI, not user-managed.
* D: False-Multiple artifacts (e.g., ZIP with dependencies) can be used, not just one file.
* Reasoning: A reflects OCI's managed, on-demand provisioning model for Jobs.
* Conclusion: A is correct.
The OCI Data Science documentation states: "Jobs provision compute infrastructure on-demand to execute user-defined tasks, such as model training or data processing, on fully managed OCI resources." B is incorrect (customization is allowed), C contradicts the managed nature, and D misstates artifact flexibility-only A accurately describes Jobs.
Oracle Cloud Infrastructure Data Science Documentation, "Jobs Overview".

NEW QUESTION # 155
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Pass Guaranteed 2025 Oracle Reliable 1z0-1110-25: Oracle Cloud Infrastructure 2025 Data Science Professional New Braindumps Ebook

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