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

Detailed Answer in Step-by-Step Solution:

Objective: Identify an open model format for cross-platform ML model execution.

Evaluate Options:

A. PySpark: A big data framework, not a model format.

B. PyTorch: An ML framework with its own format, not inherently cross-platform without conversion.

C. TensorFlow: An ML framework with its SavedModel format, not universally open across platforms.

D. ONNX: Open Neural Network Exchange, an open-source format for model interoperability across frameworks.

Reasoning: ONNX is designed for portability (e.g., convert PyTorch to ONNX, run in TensorFlow), unlike framework-specific options.

Conclusion: D is the correct choice.

ONNX (D) is “an open-source model format that enables interoperability between ML frameworks like PyTorch and TensorFlow,” per OCI documentation. PySpark (A) is a processing tool, while PyTorch (B) and TensorFlow (C) are frameworks with native formats—only ONNX ensures cross-platform compatibility.

Oracle Cloud Infrastructure Data Science Documentation, "Supported Model Formats".

Detailed Answer in Step-by-Step Solution:

Objective: Identify the OCI service for key and secret management.

Evaluate Options:

A: Data Safe—Database security, not key management.

B: Cloud Guard—Threat detection, not keys.

C: Data Guard—DB replication, not keys.

D: Vault—Key and secret management—correct.

Reasoning: Vault is OCI’s dedicated service for crypto keys and secrets.

Conclusion: D is correct.

OCI documentation states: “OCI Vault (D) centrally manages encryption keys and secrets, securing data and resource access.” A, B, and C serve other purposes—only D matches per OCI’s securityservices.

Oracle Cloud Infrastructure Vault Documentation, "Overview".

Detailed Answer in Step-by-Step Solution:

Objective: Identify custom model types for OCI Language.

Understand OCI Language: Focuses on text analysis.

Evaluate Options:

A: Image classification—Not text-based, incorrect.

B: Text classification, NER—Both text tasks—correct.

C: Sentiment, NER—Sentiment is pretrained, not custom.

D: Object detection—Image-based, incorrect.

Reasoning: B aligns with OCI Language’s text custom models.

Conclusion: B is correct.

OCI Language documentation states: “Custom models can be trained for text classification and Named Entity Recognition (NER) using your data.” Image tasks (A, D) are for Vision, and sentiment (C) is pretrained—only B fits OCI Language’s scope.

Oracle Cloud Infrastructure Language Documentation, "Custom Model Training".

Detailed Answer in Step-by-Step Solution:

Objective: Specify third-party libraries for model deployment.

Understand Artifacts: runtime.yaml defines runtime; score.py handles logic.

Evaluate Options:

A: Not a standard file—incorrect.

B: Inference code—not for dependencies.

C: Defines conda env with dependencies—correct.

D: Pip list—not used in OCI conda deployments.

Reasoning: runtime.yaml points to a conda env with all libraries.

Conclusion: C is correct.

OCI documentation states: “In runtime.yaml, specify the conda environment slug (e.g., ENVIRONMENT_SLUG: custom_env) containing all third-party libraries required by the model.” score.py (B) is for logic, requirements.txt (D) isn’t OCI-standard, and A doesn’t exist—C fixes the issue.

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

Detailed Answer in Step-by-Step Solution:

Objective: Identify the Oracle service to sort document images by language.

Task Breakdown: Requires extracting text from images (OCR) and detecting language—two potential services involved.

Evaluate Options:

A. Oracle Digital Assistant: Builds chatbots—irrelevant to image or language processing.

B. OCI Language: Detects and classifies languages in text—ideal for sorting after text extraction.

C. OCI Speech: Transcribes audio to text—not applicable to images.

D. OCI Vision: Performs OCR to extract text from images—necessary but not sufficient for language sorting.

Reasoning: The task emphasizes “sorting by language.” OCI Vision extracts text, but OCI Language identifies the language (e.g., English, Spanish). Since the question asks for one service and focuses on sorting, OCI Language (B) is the best fit, assuming text extraction is a precursor step.

Conclusion: B is correct.

OCI Language “provides language detection and classification capabilities, enabling identification of languages in text extracted from documents,” per the documentation. OCI Vision handles OCR, but the sorting task aligns with OCI Language (B). Digital Assistant (A) and Speech (C) don’t apply, and while Vision (D) is a prerequisite, B is the primary service for language sorting as per OCI’s AI service design.

Oracle Cloud Infrastructure Language Documentation, "Language Detection Features".

Detailed Answer in Step-by-Step Solution:

Objective: Transcribe Spanish video audio and identify profanity.

Evaluate Options:

A: Anomaly Detection—Not for transcription or text analysis.

B: Speech—Converts audio to text (e.g., Spanish), base for further analysis—correct.

C: Translation—Translates text, not transcription.

D: Language—Analyzes text (e.g., profanity), but needs transcribed input.

Reasoning: Speech (B) transcribes video audio; Language could follow for profanity.

Conclusion: B is correct for transcription.

OCI Speech “transcribes audio from video or audio files into text, supporting languages like Spanish.” Post-transcription, OCI Language could detect profanity, but B is the starting point—Anomaly (A) and Translation (C) don’t fit.

Oracle Cloud Infrastructure Speech Documentation, "Transcription Features".

Detailed Answer in Step-by-Step Solution:

Objective: Convert fixed to flexible load balancer in OCI.

Understand Load Balancers: Fixed (e.g., 10 Mbps) vs. flexible (dynamic shapes).

Evaluate Options:

A: False—Conversion possible via recreation.

B: Update Shape—For flexible only, not conversion.

C: Delete and recreate—Standard method—correct.

D: Edit Listener—Configures rules, not type.

Reasoning: OCI requires new creation for type change.

Conclusion: C is correct.

OCI documentation states: “To change from a fixed to a flexible load balancer, delete the existing fixed load balancer and create a new flexible one (C)—direct conversion isn’t supported.” A is too absolute, B and D don’t apply—only C matches OCI’s process.

Oracle Cloud Infrastructure Load Balancing Documentation, "Changing Load Balancer Type".