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

Detailed Answer in Step-by-Step Solution:

Objective: Determine the purpose of score.py in OCI Data Science model deployment.

Understand Model Deployment: When deploying a model in OCI, artifacts include score.py, runtime.yaml, etc.

Evaluate Options:

A: Infrastructure configuration (e.g., compute shape) is handled by deployment settings, not score.py.

B: score.py contains the inference logic (e.g., load_model(), predict())—correct.

C: Conda environment is defined in runtime.yaml or a requirements file—not score.py.

D: Scaling (e.g., instance count) is set in deployment configuration—not score.py.

Reasoning: score.py is the script executed by the deployment endpoint to load the model and make predictions.

Conclusion: B is the correct purpose.

The OCI Data Science documentation states: “The score.py file is a required artifact for model deployment, containing the inference logic—functions like load_model() to load the model and predict() to generate predictions from input data.” Infrastructure (A) and scaling (D) are managed via the OCI Console or SDK, while the environment (C) is specified in runtime.yaml. B is the precise role of score.py in OCI’s deployment workflow.

Oracle Cloud Infrastructure Data Science Documentation, "Model Deployment - score.py".

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:

Define OCI Secrets: Secrets are sensitive data (e.g., API keys, passwords) managed securely in OCI.

Evaluate Options:

A: Object Storage is for general data, not secure secret management.

B: Vault is OCI’s service for storing and managing secrets securely.

C: Autonomous Data Warehouse is for analytics, not secret storage.

D: Oracle Databases store data, not OCI-specific secrets.

Reasoning: Vault is purpose-built for secrets with encryption and access control.

Conclusion: B is correct.

OCI Vault “provides a secure, centralized service to store and manage secrets, such as passwords and keys, with encryption at rest and fine-grained access policies.” Object Storage (A), Autonomous Data Warehouse (C), and Oracle Databases (D) serve other purposes—only Vault (B) is designed for secrets per OCI’s security architecture.

Oracle Cloud Infrastructure Vault Documentation, "Secrets Management".

Detailed Answer in Step-by-Step Solution:

Understand OCI Data Labeling Export: After annotation, datasets are exported for ML use.

Check Supported Formats: OCI Data Labeling exports annotations in a structured, machine-readable format.

Evaluate Options:

A: Binary isn’t a standard export format for annotations.

B: XML isn’t used; JSON is preferred for flexibility.

C: Line-delimited JSON is the correct format, aligning with ML workflows.

D: CSV is common but not the default for OCI Data Labeling.

Conclusion: C matches the official export format.

OCI Data Labeling exports annotated datasets as line-delimited JSON files, which store each annotation as a separate JSON object per line, suitable for ML pipelines. This is explicitly stated in the documentation. (Reference: Oracle Cloud Infrastructure Data Labeling Service Documentation, "Exporting Datasets").

Detailed Answer in Step-by-Step Solution:

Objective: Run a complex Python project as an OCI Job.

Evaluate Options:

A: Auto-identification—False; entrypoint must be set.

B: Rewrite—Unnecessary, inefficient.

C: Auto-executable—False; needs explicit entrypoint.

D: ZIP with entrypoint—Correct, flexible approach.

Reasoning: D preserves structure, specifies execution.

Conclusion: D is correct.

OCI documentation states: “For complex projects, ZIP the folder and upload as a Job artifact, then set JOB_RUN_ENTRYPOINT (D) to the main executable (e.g., main.py).” Auto-detection (A, C) isn’t supported, and B discards structure—D is best.

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

Detailed Answer in Step-by-Step Solution:

Objective: Identify the mechanism for sharing and reloading ML models in OCI Data Science.

Evaluate Options:

A. Model provenance: Tracks model origin—informative but not a sharing mechanism.

B. Model taxonomy: Categorizes models (e.g., regression)—not for sharing/loading.

C. Model deployment: Makes models accessible as endpoints, not for notebook reloading.

D. Model catalog: Stores models and artifacts, enabling sharing and loading into sessions.

Reasoning: The Model Catalog is OCI’s centralized repository for saving, sharing, and retrieving models (e.g., via ADS SDK).

Conclusion: D is the correct tool.

The OCI Model Catalog “enables data scientists to save trained models and their artifacts, share them with team members, and load them back into notebook sessions for further use or evaluation.” Provenance (A) and taxonomy (B) are metadata, while deployment (C) serves inference, not notebook access. D is explicitly designed for this purpose.

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

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".

Detailed Answer in Step-by-Step Solution:

Objective: Choose the better model for cancer prediction based on metrics.

Understand Metrics:

Accuracy: Overall correct predictions.

Recall: True positives / (True positives + False negatives)—crucial for cancer (minimizing misses).

Context: Cancer prediction prioritizes recall—false negatives (missed cancers) are critical.

Evaluate Models:

Model 1: 80% accuracy, 70% recall—Misses more cancers.

Model 2: 75% accuracy, 85% recall—Misses fewer cancers.

Evaluate Options:

A: High recall—True, but lacks context.

B: High accuracy—Misses recall’s importance.

C: Recall’s impact—Correct for cancer use case—best.

D: Lesser recall impact—Incorrect for this priority.

Reasoning: C emphasizes recall’s critical role—aligns with medical needs.

Conclusion: C is correct.

OCI documentation advises: “For critical predictions like cancer detection, prioritize recall (e.g., Model 2 at 85%) over accuracy (Model 1 at 80%) to minimize false negatives, as missing cases has severe consequences (C).” A is partial, B overlooks context, D reverses priority—only C fits OCI’s ML evaluation guidance for this scenario.

Oracle Cloud Infrastructure Data Science Documentation, "Evaluating Classification Models".

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".

Detailed Answer in Step-by-Step Solution:

Objective: Identify true statements about published conda environments in OCI.

Understand Published Conda: Custom envs shared via Object Storage.

Evaluate Options:

A: False—Users create them, not OCI-curated.

B: True—odsc conda init sets up access to published envs.

C: False—Object Storage, not notebook, is the source.

D: False—Can create from scratch, not just modifications.

E: True—Conda env vars extend job configs.

Reasoning: B configures access, E enhances flexibility—match OCI behavior.

Conclusion: B and E are correct.

OCI documentation states: “odsc conda init (B) configures the bucket for published conda environments, and Jobs can use conda environment variables (E) alongside service vars.” A misattributes curation, C misplaces source, and D limits creation—only B and E are true per OCI’s conda management.

Oracle Cloud Infrastructure Data Science Documentation, "Published Conda Environments".