Pass the Oracle Cloud Solutions Infrastructure 1z0-1110-25 Questions and answers with ExamsMirror

Detailed Answer in Step-by-Step Solution:

Objective: Identify supported annotation classes in OCI Data Labeling.

Understand Tool: Supports image/text annotations for ML.

Evaluate Options:

A: Object detection—Yes (bounding boxes).

B: Named entity—Text-specific, not primary for images.

C: Classification—Yes (labels for images/text).

D: Key-point—Not listed in OCI docs.

E: Polygonal—Not explicitly supported.

F: Semantic segmentation—Yes (pixel-level).

Reasoning: A, C, F match OCI’s image/text focus.

Conclusion: A, C, F are correct.

OCI Data Labeling supports “object detection (A), classification (C), and semantic segmentation (F) for images and text,” per documentation. B is text-specific, D and E aren’t highlighted—only A, C, F are core classes.

Oracle Cloud Infrastructure Data Labeling Documentation, "Annotation Types".

Detailed Answer in Step-by-Step Solution:

Objective: Identify a step in OCI’s AutoML pipeline.

Understand AutoML: Automates model building—includes preprocessing, selection, and tuning.

Evaluate Options:

A: Feature Extraction (e.g., PCA) isn’t explicitly part of OCI AutoML—too specific.

B: Saving to Model Catalog is post-AutoML, not a pipeline step.

C: Deployment is a separate action after AutoML—incorrect.

D: Feature Selection (e.g., choosing relevant features) is a core AutoML step—correct.

Reasoning: OCI AutoML automates feature selection, algorithm choice, and tuning—D fits.

Conclusion: D is correct.

OCI AutoML’s pipeline includes “feature selection, algorithm selection, adaptive sampling, and hyperparameter tuning,” per the documentation. Extraction (A) isn’t highlighted, while saving (B) and deployment (C) are post-process actions—only Feature Selection (D) is an integral automated step.

Oracle Cloud Infrastructure Data Science Documentation, "AutoML Pipeline".

Detailed Answer in Step-by-Step Solution:

Objective: Sequence OCI AutoML pipeline stages.

Stages:

Adaptive sampling: Reduces data size if large.

Feature selection: Picks relevant features.

Algorithm selection: Chooses best model type.

Hyperparameter tuning: Optimizes model params.

Evaluate: C (sampling, features, algorithms, tuning) matches logical flow—data first, then model.

Reasoning: Sampling precedes feature work—standard in OCI.

Conclusion: C is correct.

OCI documentation states: “AutoML pipeline runs 1) adaptive sampling, 2) feature selection, 3) algorithm selection, 4) hyperparameter tuning (C).” Sampling reduces data first, then features and models are optimized—other orders (A, B, D) misalign with OCI’s sequence.

Oracle Cloud Infrastructure AutoML Documentation, "Pipeline Sequence".

Detailed Answer in Step-by-Step Solution:

Objective: Differentiate data scientist vs. data engineer roles.

Define Roles:

Data Engineer: Builds pipelines, prepares data.

Data Scientist: Analyzes data, builds models.

Evaluate Options:

A: Engineer preps, scientist analyzes—Correct division.

B: Reverses roles—Incorrect.

C: Overlaps roles—Scientist doesn’t typically build pipelines.

D: Misaligns—Analyst isn’t the focus.

Reasoning: A reflects standard role separation.

Conclusion: A is correct.

OCI documentation notes: “Data engineers focus on collecting and preparing data through pipelines, while data scientists analyze it to derive insights and build models.” A aligns, B inverts, C overcomplicates, and D shifts focus—only A is accurate.

Oracle Cloud Infrastructure Data Science Documentation, "Roles in Data Science".

Detailed Answer in Step-by-Step Solution:

Objective: Locate sources for ADS SDK in OCI.

Understand ADS SDK: A Python library for Data Science tasks (e.g., EDA).

Evaluate Options:

A: Big Data Service—Spark-focused, not ADS source.

B: Machine Learning—Separate service, not ADS-related.

C: Conda in OCI Data Science—Preinstalled ADS in notebook sessions.

D: PyPI—Public source to install ADS (pip install oracle-ads).

E: ADW—Database, not an SDK source.

Reasoning: C (preinstalled) and D (installable) are practical access points.

Conclusion: C and D are correct.

OCI documentation states: “The ADS SDK is available in OCI Data Science notebook sessions via preinstalled conda environments (C) and can be installed from PyPI (D) using pip install oracle-ads.” Big Data (A), Machine Learning (B), and ADW (E) don’t host ADS—only C and D apply.

Oracle Cloud Infrastructure Data Science Documentation, "ADS SDK Installation".

Detailed Answer in Step-by-Step Solution:

Objective: Determine where to specify the conda environment for an OCI model deployment.

Understand Model Deployment: Requires artifacts like score.py and runtime.yaml to define runtime settings.

Evaluate Options:

A. score.py: Contains inference logic (e.g., load_model(), predict())—not for environment specs.

B. runtime.yaml: Defines deployment runtime, including conda environment path—correct.

C. requirements.txt: Lists pip dependencies—not used in OCI for conda environments.

D. model_artifact_validate.py: Not a standard artifact; doesn’t exist in OCI deployment.

Reasoning: runtime.yaml specifies the conda env (e.g., slug: pyspark30_p37_cpu_v2)—failure to set this causes deployment errors.

Conclusion: B is correct.

OCI documentation states: “The runtime.yaml file in a model artifact specifies the runtime environment, including the conda environment path (e.g., ENVIRONMENT_SLUG: pyspark30_p37_cpu_v2), ensuring the deployed model uses the correct dependencies.” score.py (A) handles inference, requirements.txt (C) is for pip (not conda in OCI), and D isn’t valid—only B addresses the conda issue per OCI’s deployment process.

Oracle Cloud Infrastructure Data Science Documentation, "Model Deployment - runtime.yaml".

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".

Detailed Answer in Step-by-Step Solution:

Objective: Apply all recommended transformations from get_recommendations() in ADS.

Understand ADS Tools: get_recommendations() suggests fixes (e.g., missing values).

Evaluate Options:

A: Returns transformed data—Not for applying—incorrect.

B: Sklearn-style, not ADS-specific—incorrect.

C: auto_transform()—Applies all recommendations—correct.

D: Visualizes, doesn’t apply—incorrect.

Reasoning: auto_transform() executes the fixes suggested by get_recommendations().

Conclusion: C is correct.

OCI documentation states: “After get_recommendations() identifies issues, use auto_transform() (C) on the ADSDataset to apply all recommended transformations at once.” A retrieves, B is external, D visualizes—only C aligns with OCI’s ADS transformation workflow.

Oracle Cloud Infrastructure ADS SDK Documentation, "Data Transformation Methods".

Detailed Answer in Step-by-Step Solution:

Objective: Identify the OCI service for ML model lifecycle.

Evaluate Options:

A: Data Catalog—Metadata management, not ML.

B: Data Integration—ETL, not ML.

C: Data Science—Full ML lifecycle—correct.

D: Data Flow—Spark processing, not full ML.

Reasoning: C supports building, training, deploying models.

Conclusion: C is correct.

OCI documentation states: “OCI Data Science (C) provides tools to build, train, and deploy machine learning models in the cloud, including notebooks and model catalog.” A, B, and D serve other purposes—only C fits the ML lifecycle per OCI’s offerings.

Oracle Cloud Infrastructure Data Science Documentation, "Service Overview".

Detailed Answer in Step-by-Step Solution:

Objective: Address declining model performance and improve churn prediction.

Analyze Issue: Poor predictions suggest data drift or model staleness—common ML challenges.

Evaluate Options:

A. Drift monitoring: Tracks changes in data distribution—identifies root cause of accuracy drop—correct.

B. Redeploy the model: Repeats deployment without fixing the issue—ineffective alone.

C. Operational monitoring: Tracks system health (e.g., latency), not prediction quality.

D. Retrain the model: Updates model with new data—directly improves accuracy—correct.

E. Validate with recent data: Checks performance but doesn’t fix—diagnostic, not corrective.

Reasoning: A diagnoses drift (cause), D retrains to adapt (solution)—best combo to verify and lower churn.

Conclusion: A and D are correct.

OCI documentation advises: “Drift monitoring (A) detects shifts in data distribution that degrade model performance, while retraining (D) with fresh data restores accuracy.” Redeployment (B) doesn’t address drift, operational monitoring (C) focuses on infra, and validation (E) is a check—not a fix. A and D align with OCI’s model maintenance strategy.

Oracle Cloud Infrastructure Data Science Documentation, "Model Monitoring and Retraining".