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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

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

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 4

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 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 (Q20-Q25):

NEW QUESTION # 20
Which technique can be used for feature engineering in the machine learning lifecycle?

A. Gradient boosting
B. Support Vector Machines (SVM)
C. Principal Component Analysis (PCA)
D. K-means clustering

Answer: C

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify a feature engineering technique in ML.
* Understand Feature Engineering: Transforms raw data into model-ready features.
* Evaluate Options:
* A. PCA: Reduces dimensionality-feature engineering-correct.
* B. K-means: Clustering model-not feature engineering.
* C. SVM: Classification model-not feature engineering.
* D. Gradient boosting: Model training-not feature engineering.
* Reasoning: PCA creates new features via transformation-fits definition.
* Conclusion: A is correct.
OCI documentation states: "Feature engineering techniques like Principal Component Analysis (PCA) (A) transform data into new features to enhance model performance." B, C, and D are modeling techniques-only A aligns with OCI's feature engineering stage.
Oracle Cloud Infrastructure Data Science Documentation, "Feature Engineering Techniques".

NEW QUESTION # 21
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_closed_handler handler
B. Execute the is_tech_job_default_handler handler
C. Throw an error because the system cannot determine which handler to run
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 # 22
You have just received a new dataset from a colleague. You want to quickly find out summary information about the dataset, such as the types of features, the total number of observations, and distributions of the data.
Which Accelerated Data Science (ADS) SDK method from the ADSDataset class would you use?

A. to_xgb()
B. show_corr()
C. show_in_notebook()
D. compute()

Answer: C

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Get summary info from an ADSDataset object.
* Evaluate Options:
* A: Correlation matrix-Specific, not full summary.
* B: Converts to XGBoost-Not for summary.
* C: Executes computation-Not summary-focused.
* D: Displays summary (types, counts, dist)-correct.
* Reasoning: show_in_notebook() provides a comprehensive overview.
* Conclusion: D is correct.
OCI documentation states: "show_in_notebook() (D) from ADSDataset displays a summary of the dataset, including feature types, observation count, and distributions, in a notebook." A is partial, B and C are unrelated-only D meets the need per ADS SDK.
Oracle Cloud Infrastructure ADS SDK Documentation, "ADSDataset Methods".

NEW QUESTION # 23
Which statement is true about origin management in Web Application Firewall (WAF)?

A. Both the statements are true
B. Only a single origin can be active for a WAF
C. Only statement A is true
D. Both the statements are false
E. Multiple origins can be defined
F. Only statement B is true

Answer: A

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Determine truth about WAF origin management.
* Understand WAF: Protects apps by routing traffic via origins.
* Evaluate Statements:
* A: Multiple origins-True; WAF supports this.
* B: Single active origin-True; only one is active per policy.
* Evaluate Options:
* C: B only-False; A is true.
* D: Both false-Incorrect.
* E: Both true-Correct per OCI WAF.
* F: A only-False; B is true.
* Conclusion: E is correct.
OCI documentation states: "WAF allows defining multiple origins (A), but only one origin is active per WAF policy at a time (B)-both are true (E)." C, D, and F misalign-E matches OCI's WAF origin management.
Oracle Cloud Infrastructure WAF Documentation, "Origin Management".

NEW QUESTION # 24
You have a dataset with fewer than 1000 observations, and you are using Oracle AutoML to build a classifier.
While visualizing the results of each stage of the Oracle AutoML pipeline, you notice that no visualization has been generated for one of the stages. Which stage is not visualized?

A. Adaptive sampling
B. Feature selection
C. Hyperparameter tuning
D. Algorithm selection

Answer: A

Explanation:
Detailed Answer in Step-by-Step Solution:
* Objective: Identify the non-visualized AutoML stage with small data.
* Understand AutoML Pipeline: Includes sampling, feature/algorithm selection, tuning.
* Evaluate Options:
* A: Feature selection-Visualized (e.g., feature importance).
* B: Algorithm selection-Visualized (e.g., algorithm scores).
* C: Adaptive sampling-Skipped/visualization absent for <1000 rows.
* D: Hyperparameter tuning-Visualized (e.g., trial plots).
* Reasoning: Adaptive sampling optimizes large datasets; small data skips it, omitting visuals.
* Conclusion: C is correct.
OCI AutoML documentation notes: "Adaptive sampling is applied to large datasets (>1000 rows) to reduce size; for smaller datasets, it's skipped, and no visualization is generated." Other stages (A, B,D) produce visuals-only C is absent here.
Oracle Cloud Infrastructure AutoML Documentation, "Pipeline Stages".

NEW QUESTION # 25
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