1z0-1110-25 Exam Dumps - Oracle Cloud Infrastructure Questions and Answers

Detailed Answer in Step-by-Step Solution:

Objective: Efficiently vary hyperparameters in OCI Jobs.

Evaluate Options:

A: New job per run—Wastes setup time.

B: Code changes per job—Inefficient, error-prone.

C: Flexible params per run—Efficient, reusable—correct.

D: New job per run—Redundant effort.

Reasoning: C minimizes job creation, maximizes flexibility.

Conclusion: C is correct.

OCI documentation states: “For varying hyperparameters, configure a single Job with code accepting environment variables or command-line arguments (C), set per run—most efficient.” A and D over-create jobs, B ties params to code—only C optimizes.

Oracle Cloud Infrastructure Data Science Documentation, "Job Parameterization".

Detailed Answer in Step-by-Step Solution:

Objective: Spot the invalid OCI IAM policy.

Understand Policies: Use verbs (e.g., use, manage) and resource types.

Evaluate Options:

A: Valid—virtual-network-family is a resource type.

B: Invalid—No data-science-model-sessions resource type; likely meant data-science-models.

C: Valid—data-science-projects is correct.

D: Valid—data-science-family aggregates Data Science resources.

Reasoning: B uses a nonexistent resource type—error in syntax.

Conclusion: B is incorrect.

OCI IAM documentation lists valid resource types: “data-science-models, data-science-projects, data-science-family—but not data-science-model-sessions (B).” A, C, D are correct—B is invalid due to this typo/misunderstanding.

Oracle Cloud Infrastructure IAM Documentation, "Data Science Resource Types".

Detailed Answer in Step-by-Step Solution:

Objective: Find the OCI service for accessing large public datasets.

Evaluate Options:

A: Oracle Databases—General-purpose, not dataset-focused.

B: ADW—Analytics warehouse, not a dataset repository.

C: OCI Data Science—ML platform, not a dataset provider.

D: Oracle Open Data—Free, public datasets (e.g., geospatial).

Reasoning: Open Data provides pre-existing large datasets for research.

Conclusion: D is correct.

OCI documentation states: “Oracle Open Data provides free access to large, curated datasets, such as geospatial data, ideal for researchers.” Databases (A) and ADW (B) are for storage/analytics, Data Science (C) is for ML—not datasets—only Open Data (D) fits.

Oracle Cloud Infrastructure Open Data Documentation, "Overview".

Detailed Answer in Step-by-Step Solution:

Objective: Identify a step unique to MLOps vs. DevOps.

Compare MLOps and DevOps:

DevOps: Focuses on software deployment (CI/CD).

MLOps: Extends DevOps to ML, adding model-specific steps.

Evaluate Options:

A: Continuous deployment—Common to both (software/models).

B: Continuous integration—Common to both (code merging).

C: Continuous delivery—Common to both (releasing updates).

D: Continuous training—Unique to MLOps (retraining models with new data).

Reasoning: Only D addresses ML-specific needs (model retraining).

Conclusion: D is correct.

OCI documentation notes: “MLOps extends DevOps with continuous training, a process unique to machine learning where models are retrained with new data to maintain performance.” CI (B), CD (A), and delivery (C) are shared with DevOps—only continuous training (D) is MLOps-specific.

Oracle Cloud Infrastructure Data Science Documentation, "MLOps Concepts".

Detailed Answer in Step-by-Step Solution:

Objective: Address declining prediction accuracy and verify model performance.

Analyze Problem: Degradation over time suggests data drift or model staleness—common ML issues.

Evaluate Options:

A. Retrain the model: Uses new data to update the model—fixes accuracy—correct.

B. Validate with recent data: Tests performance but doesn’t fix—diagnostic only.

C. Drift monitoring: Detects data distribution shifts—verifies cause—correct.

D. Redeploy the model: Repeats deployment, doesn’t address root cause.

E. Operational monitoring: Tracks infra (e.g., latency), not prediction accuracy.

Reasoning: C identifies drift (why accuracy dropped), A corrects it—best pair for verification and improvement.

Conclusion: A and C are correct.

OCI documentation states: “Drift monitoring (C) detects changes in data distribution that impact accuracy, while retraining (A) with new data restores model performance.” Validation (B) checks but doesn’t fix, redeployment (D) is redundant, and operational monitoring (E) is infra-focused—only A and C align with OCI’s model maintenance strategy.

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

Detailed Answer in Step-by-Step Solution:

Objective: Identify interfaces to save a 2 GB model to the Model Catalog.

Evaluate Options:

A: OCI CLI—Supports Data Science tasks—possible but not primary.

B: ADS SDK—Designed for model catalog ops—correct.

C: ODSC CLI—Not standard; likely typo for OCI CLI.

D: Console—GUI for catalog uploads—correct.

E: OCI Python SDK—Programmatic catalog access—correct.

F: Git CLI—Version control, not catalog-related.

Reasoning: B, D, E are OCI’s primary interfaces; A is valid but less emphasized.

Conclusion: B, D, E are correct (A plausible but not top-tier).

OCI documentation lists “ADS SDK (B), OCI Console (D), and OCI Python SDK (E) as primary methods to save models to the Model Catalog.” OCI CLI (A) works but isn’t highlighted, C isn’t real, and F is unrelated—B, D, E are the standard trio.

Oracle Cloud Infrastructure Data Science Documentation, "Model Catalog Interfaces".

Detailed Answer in Step-by-Step Solution:

Define Feature Engineering: It’s the process of creating or modifying features to improve model performance.

Evaluate Options:

A: Parameter tuning adjusts model hyperparameters (e.g., learning rate), not features.

B: Model interpretation (e.g., SHAP values) explains predictions, not feature creation.

C: Transforming features (e.g., normalizing, encoding) is the core of feature engineering—correct.

D: Understanding features occurs during exploration, not engineering.

Reasoning: Feature engineering directly manipulates data inputs (e.g., converting timestamps to day-of-week), distinct from tuning or interpretation.

Conclusion: C is the precise definition.

OCI Data Science documentation defines feature engineering as “the process of transforming raw data into features that better represent the underlying problem to the predictive models, resulting in improved model accuracy.” Examples include scaling or creating interaction terms, aligning with C. Other options (A, B, D) relate to different ML stages.

Oracle Cloud Infrastructure Data Science Documentation, "Feature Engineering Overview".