Oracle 1z0-1110-25 - Oracle Cloud Infrastructure 2025 Data Science Professional

Detailed Answer in Step-by-Step Solution:

Objective: Configure conda env for a pipeline step.

Evaluate Options:

A: Shape—Infra, not env config.

B: Volume—Storage, not env.

C: Command-line—Step args, not env.

D: Env variables—Sets conda path—correct.

Reasoning: D specifies runtime env (e.g., CONDA_ENV_SLUG).

Conclusion: D is correct.

OCI documentation states: “Configure a pipeline step’s conda environment using environment variables (D), such as CONDA_ENV_SLUG, in the step definition.” A, B, and C address other aspects—only D fits env config.

Oracle Cloud Infrastructure Data Science Documentation, "Pipeline Step Configuration".

Detailed Answer in Step-by-Step Solution:

Objective: Identify export formats for OCI Data Labeling.

Understand Export: Annotated data is exported for ML use.

Evaluate Options:

A: CONLL V2003—Text-specific, not OCI-supported.

B: COCO—Standard for image annotations—correct.

C: Proprietary JSON—OCI’s default format—correct.

D: Spacy—NLP format, not supported.

Reasoning: B and C are image-compatible per OCI docs.

Conclusion: B and C are correct.

OCI documentation states: “Data Labeling exports annotations in COCO format (B) for image tasks and a proprietary JSON format (C) specific to the service.” CONLL (A) and Spacy (D) are text/NLP-focused—not supported for OCI’s image annotations.

Oracle Cloud Infrastructure Data Labeling Documentation, "Export Formats".

Detailed Answer in Step-by-Step Solution:

Objective: Identify the OCI service for restricted resource access.

Evaluate Options:

A: Bastion—Secure, temporary access to resources—correct.

B: Internet Gateway—Public access, not restricted.

C: Load Balancer—Distributes traffic, not access control.

D: SSL Certificate—Secures comms, not access.

Reasoning: Bastion limits access (e.g., SSH) to specific targets.

Conclusion: A is correct.

OCI documentation states: “OCI Bastion (A) provides restricted, audited access to target resources like instances, typically via SSH.” B, C, and D don’t restrict—only A fits per OCI’s security services.

Oracle Cloud Infrastructure Bastion Documentation, "Overview".

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".

Detailed Answer in Step-by-Step Solution:

Objective: Optimize an embarrassingly parallel job in OCI Data Science Jobs.

Define Embarrassingly Parallel: Tasks are independent, ideal for simultaneous runs.

Evaluate Options:

A: Multiple simultaneous runs—Leverages parallelism—correct.

B: One job per run—Misstates capability; unnecessary complexity.

C: Sequential runs—Inefficient, ignores parallelism.

D: False—Jobs support parallelism.

Reasoning: A maximizes efficiency for parallel tasks.

Conclusion: A is correct.

OCI documentation states: “For embarrassingly parallel workloads, create a single Job and launch multiple simultaneous Job Runs to process data in parallel.” B misinterprets limits, C wastes time, and D denies capability—only A fits OCI’s design.

Oracle Cloud Infrastructure Data Science Documentation, "Parallel Job Runs".

Detailed Answer in Step-by-Step Solution:

Objective: Predict AutoML’s output when time runs out during tuning.

Understand AutoML Tuning: Iteratively tests hyperparameters, tracks best results.

Evaluate Options:

A: Best-known config—Logical, reflects optimization goal—correct.

B: Last config—Ignores prior better results—incorrect.

C: Minimum learning rate—Arbitrary, not performance-based.

D: Random—Defeats tuning purpose.

Reasoning: AutoML prioritizes the best config found within the budget.

Conclusion: A is correct.

OCI AutoML documentation states: “If the time budget expires, AutoML returns the best hyperparameter configuration (A) identified during tuning based on performance metrics.” Last (B), minimum (C), or random (D) configs aren’t selected—only A aligns with OCI’s optimization strategy.

Oracle Cloud Infrastructure AutoML Documentation, "Hyperparameter Tuning - Time Budget".

Detailed Answer in Step-by-Step Solution:

Objective: Minimize false alarms in OCI Anomaly Detection.

Understand FAP: False Alarm Probability—lower FAP means fewer false positives.

Evaluate Options:

A: High FAP—Increases false alarms—incorrect.

B: Low FAP—Reduces false alarms—correct.

C: Zero FAP—Unrealistic; risks missing true anomalies.

D: Function—Vague, not a direct setting.

Reasoning: Low FAP balances sensitivity and false positives— aligns with goal.

Conclusion: B is correct.

OCI Anomaly Detection documentation states: “Set a low False Alarm Probability (FAP) to minimize false positives, though too low (e.g., zero) may miss anomalies.” B fits the goal—high (A) increases errors, zero (C) is impractical, and function (D) isn’t specified.

Oracle Cloud Infrastructure Anomaly Detection Documentation, "Configuring FAP".