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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

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 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

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

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 (Q125-Q130):

NEW QUESTION # 125
Where do calls to stdout and stderr from score.py go in the model deployment?

A. The predict log in the Oracle Cloud Infrastructure (OCI) Logging service as defined in the deployment
B. The OCI Cloud Shell, which can be accessed from the console
C. The OCI Console
D. The file that was defined for them on the virtual machine (VM)

Answer: A

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Locate stdout/stderr output from score.py in deployment.
* Understand Deployment: score.py runs in a model endpoint; logs are managed by OCI.
* Evaluate Options:
* A: False-No VM file is defined by default; logs go to OCI Logging.
* B: False-Console displays UI, not raw logs.
* C: False-Cloud Shell is a separate tool, not a log destination.
* D: True-Predict logs in OCI Logging capture stdout/stderr.
* Reasoning: OCI centralizes logs in its Logging service for deployments.
* Conclusion: D is correct.
OCI documentation states: "During model deployment, stdout and stderr from score.py are automatically sent to the predict log in the OCI Logging service, as configured in the deployment settings." A, B, and C don't align with this managed logging approach-only D is accurate.
Oracle Cloud Infrastructure Data Science Documentation, "Model Deployment - Logging".

NEW QUESTION # 126
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 network configuration?

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

Answer: C

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Diagnose a network timeout during pip install in a notebook session.
* Understand Notebook Networking: Sessions run in a VCN; internet access requires specific configs.
* Analyze Timeout: Indicates failure to reach the public PyPI repository-likely no internet outbound route.
* Evaluate Options:
* A: NAT Gateway-Provides internet access for private subnets-correct fix.
* B: Service Gateway-Accesses OCI services privately, not public internet.
* C: FastConnect-Links to on-premises, not public internet.
* D: VNIC-Essential but present by default; doesn't solve internet access.
* Reasoning: NAT Gateway enables outbound traffic to public repos like PyPI.
* Conclusion: A is correct.
OCI documentation notes: "Notebook sessions in a private subnet require a NAT Gateway to access public internet resources, such as PyPI, for package installation via pip. Without it, network timeouts occur." Service Gateway (B) is for OCI services, FastConnect (C) is irrelevant, and VNIC (D) is standard-only A resolves the issue.
Oracle Cloud Infrastructure Data Science Documentation, "Networking for Notebook Sessions".

NEW QUESTION # 127
Which of the following best describes the principal goal of data science?

A. Data science is focused on output of the analysis.
B. To collect and archive exhaustive datasets from various source systems for corporate record-keeping uses.
C. To mine and analyze large amounts of data in order to uncover information that can be used for operational improvements and business gains.
D. To collect and prepare data for use as part of analytics applications.

Answer: C

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Define data science's main goal.
* Evaluate Options:
* A: Archiving-Not the focus; too narrow.
* B: Analyze for insights/business value-Core purpose-correct.
* C: Prep for analytics-Means, not the end goal.
* D: Output-focused-Vague, incomplete.
* Reasoning: B captures the actionable insight generation central to data science.
* Conclusion: B is correct.
OCI documentation defines data science as "mining and analyzing large datasets to uncoveractionable insights for operational improvements and business value." A is storage-focused, C is preparatory, and D is unclear-only B reflects the principal goal per OCI's mission.
Oracle Cloud Infrastructure Data Science Documentation, "What is Data Science?".

NEW QUESTION # 128
You have trained three different models on your dataset using Oracle AutoML. You want to visualize the behavior of each of the models, including the baseline model, on the test set. Which class should be used from the Accelerated Data Science (ADS) SDK to visually compare the models?

A. EvaluationMetrics
B. ADSEvaluator
C. ADSTuner
D. ADSExplainer

Answer: B

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify the ADS SDK class for visualizing model performance comparison.
* Understand ADS Classes: Each serves a specific ML purpose-visualization requires evaluation tools.
* Evaluate Options:
* A. EvaluationMetrics: Likely a typo-meant EvaluationsMetrics? Not a standalone class for visualization.
* B. ADSEvaluator: Designed to evaluate and visualize model performance (e.g., ROC curves)- correct.
* C. ADSExplainer: Explains model predictions (e.g., SHAP), not comparative visualization.
* D. ADSTuner: Tunes hyperparameters, not for visualization.
* Reasoning: ADSEvaluator provides comparative plots (e.g., precision-recall) for multiple models, including baselines.
* Conclusion: B is correct.
OCI documentation states: "The ADSEvaluator class in ADS SDK (B) enables visualization of model performance metrics, such as ROC curves and confusion matrices, for multiple models on a test set, including baselines." EvaluationMetrics (A) isn't a class, ADSExplainer (C) focuses on interpretability, and ADSTuner (D) is for tuning-only B fits the visualization need per OCI's ADS toolkit.
Oracle Cloud Infrastructure ADS SDK Documentation, "ADSEvaluator Class".

NEW QUESTION # 129
The feature type TechJob has the following registered validators:
* TechJob.validator.register(name='is_tech_job', handler=is_tech_job_default_handler)
* TechJob.validator.register(name='is_tech_job', handler=is_tech_job_open_handler, condition= ('job_family',))
* TechJob.validator.register(name='is_tech_job', handler=is_tech_job_closed_handler, condition= ('job_family': 'IT'))When you run is_tech_job(job_family='Engineering'), what does the feature type validator system do?

A. Execute the is_tech_job_default_handler handler
B. Throw an error because the system cannot determine which handler to run
C. Execute the is_tech_job_closed_handler handler
D. Execute the is_tech_job_open_handler handler

Answer: D

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Determine which validator handler runs for is_tech_job(job_family='Engineering').
* Understand Validator System: Likely ADS SDK-executes handlers based on conditions.
* Analyze Validators:
* Default: is_tech_job_default_handler-No condition, fallback.
* Open: is_tech_job_open_handler-Condition ('job_family',)-requires job_family arg.
* Closed: is_tech_job_closed_handler-Condition ('job_family': 'IT')-requires job_family='IT'.
* Evaluate Call: job_family='Engineering'-Matches job_family presence, not IT.
* Reasoning:
* Open handler applies (tuple condition means arg exists).
* Closed fails (Engineering # IT).
* Default is overridden by specific matches.
* Conclusion: D is correct.
OCI ADS documentation states: "Validators execute the most specific handler matching the condition; for is_tech_job(job_family='Engineering'), is_tech_job_open_handler (D) runs as it matches job_family presence, while is_tech_job_closed_handler (C) requires IT-default (A) is bypassed, no error (B)." Only D fits per ADS validator logic.
Oracle Cloud Infrastructure ADS SDK Documentation, "Feature Type Validators".

NEW QUESTION # 130
......

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Martin Davis Martin Davis

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