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

Detailed Answer in Step-by-Step Solution:

Context Analysis: You need to investigate an existing deep neural network model in the OCI Model Catalog with no prior information.

Understand Model Catalog: The Model Catalog stores trained models along with metadata, hyperparameters, and provenance (origin and history) details.

Evaluate Options:

A. Refer to the code inside the model: The model artifact (e.g., a serialized file like .pkl) doesn’t typically include readable source code; it’s a trained object, not the training script.

B. Check for model taxonomy details: Taxonomy (e.g., classification vs. regression) provides high-level categorization but lacks specifics like framework or architecture.

C. Check for metadata tags: Metadata includes name, description, and tags, offering some context but not detailed framework info (e.g., TensorFlow vs. PyTorch).

D. Check for provenance details: Provenance tracks the model’s creation process, including the framework, training environment, and data sources, providing the most comprehensive insight.

Reasoning: Provenance details are designed to document the “how” and “what” of model creation, making them ideal for uncovering the framework (e.g., Keras, PyTorch) and other specifics absent from initial handover.

Conclusion: D is the best approach for detailed investigation.

In OCI Data Science, the Model Catalog stores provenance information, which includes “details about the model’s origin, such as the framework used (e.g., TensorFlow, PyTorch), the training environment, and dataset references.” This is more informative than metadata tags (C), which are user-defined and less structured, or taxonomy (B), which is broad. The model artifact (A) is a binary file (e.g., pickle), not a readable codebase. Provenance (D) offers a detailed audit trail, critical for analyzing an undocumented deep neural network model like this one.

Oracle Cloud Infrastructure Data Science Documentation, "Model Catalog - Provenance Details" section.

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".

Detailed Answer in Step-by-Step Solution:

Objective: Identify the non-Observability and Management (O&M) service in OCI.

Understand O&M: Includes monitoring, logging, events tools.

Evaluate Options:

A: Event Services—Triggers actions, part of O&M—correct.

B: OCI Management Service—Not a defined O&M service—incorrect.

C: Logging Analytics—Log analysis, O&M component—correct.

D: Logging—Log collection, O&M component—correct.

Reasoning: B isn’t listed in OCI’s O&M suite—others are.

Conclusion: B is correct (not part of O&M).

OCI documentation lists “Observability and Management Services as including Event Services (A), Logging Analytics (C), and Logging (D)—‘OCI Management Service’ (B) is not a recognized component.” B appears to be a misnomer—only A, C, D are O&M per OCI’s service catalog.

Oracle Cloud Infrastructure Observability and Management Documentation, "Service Overview".

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: Identify the OCI Data Science interaction method that doesn’t require scripting.

Understand Interaction Methods: OCI provides multiple ways to interact with Data Science services—some are GUI-based, others script-based.

Evaluate Options:

A. OCI Console: A web-based graphical interface allowing users to manage resources (e.g., create notebook sessions, deploy models) via point-and-click—no scripting needed.

B. CLI: Command Line Interface requires writing commands (scripts) to execute tasks (e.g., oci data-science notebook-session create).

C. Language SDKs: Software Development Kits (e.g., Python SDK) require coding to interact programmatically (e.g., oci.data_science.DataScienceClient).

D. REST APIs: Application Programming Interfaces require scripted HTTP requests (e.g., using curl or a programming language).

Reasoning: Only the OCI Console (A) offers a no-code, user-friendly interface, while B, C, and D rely on scripting or programming.

Conclusion: A is the correct answer as it eliminates the need for scripting.

The OCI Console is described in the documentation as “a browser-based interface that allows users to manage OCI Data Science resources, such as creating notebook sessions or jobs, without writing code or scripts.” In contrast, the CLI (B) requires command-line scripts, SDKs (C) need programming (e.g., Python), and REST APIs (D) involve scripted API calls. The Console’s GUI distinguishes it as the only option functioning without scripting, aligning with Oracle’s design for accessibility to non-programmers.

Oracle Cloud Infrastructure Data Science Documentation, "Getting Started with OCI Console" section.

Detailed Answer in Step-by-Step Solution:

Objective: Select an OCI AI service for text and table extraction from documents.

Evaluate Options:

A: Language—Text analysis, not extraction—incorrect.

B: Digital Assistant—Chatbots, not document tasks—incorrect.

C: Speech—Audio transcription, not documents—incorrect.

D: Vision—OCR for text/tables—correct.

Reasoning: Vision’s OCR extracts text and tables from document images.

Conclusion: D is correct.

OCI documentation states: “OCI Vision (D) uses OCR to extract text and tables from documents, supporting document analysis tasks.” A analyzes text post-extraction, B and C are unrelated—only D fits per OCI’s AI services.

Oracle Cloud Infrastructure Vision Documentation, "Document Analysis Features".

Detailed Answer in Step-by-Step Solution:

Objective: Define the role of score.py in OCI model artifacts.

Understand Artifacts: score.py is key for deployment runtime.

Evaluate Options:

A: Infra config—Handled by OCI settings, not score.py.

B: Inference logic—Correct; runs load_model(), predict().

C: Scaling—Set in deployment, not score.py.

D: Dependencies—In runtime.yaml, not score.py.

Reasoning: B aligns with score.py’s execution role.

Conclusion: B is correct.

OCI documentation states: “score.py (B) contains the inference logic, including functions to load the model and predict outputs, executed by the deployment endpoint.” A, C, and D are managed elsewhere—only B matches OCI’s design.

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

Detailed Answer in Step-by-Step Solution:

Objective: Identify two OCI Language AI capabilities.

Understand OCI Language: Focuses on text analysis tasks.

Evaluate Options:

A: Table extraction—Vision, not Language—incorrect.

B: Punctuation correction—Not offered—incorrect.

C: Sentence diagramming—Not supported—incorrect.

D: Topic classification—Supported (custom/pretrained)—correct.

E: Sentiment analysis—Supported (pretrained)—correct.

Reasoning: D and E are core text analysis features of OCI Language.

Conclusion: D and E are correct.

OCI documentation states: “OCI Language offers topic classification (D) and sentiment analysis (E) for text analysis, among other features.” A belongs to Vision, B and C aren’t available—only D and E match OCI Language’s capabilities.

Oracle Cloud Infrastructure Language Documentation, "Text Analysis Features".

Detailed Answer in Step-by-Step Solution:

Objective: Visualize correlation between continuous and categorical features.

Evaluate Options:

A: Pearson—Continuous vs. continuous—incorrect.

B: Cramer’s V—Categorical vs. categorical—incorrect.

C: Correlation ratio—Continuous vs. categorical—correct.

D: General correlation—Not specific to mixed types.

Reasoning: Correlation ratio handles mixed feature types for heatmaps.

Conclusion: C is correct.

OCI documentation states: “correlation_ratio_plot() (C) in ADS SDK visualizes correlations between continuous and categorical features, ideal for mixed-type heatmaps.” Pearson (A) and Cramer’s (B) are type-specific, corr() (D) is broad—only C fits per ADS capabilities.

Oracle Cloud Infrastructure ADS SDK Documentation, "Correlation Visualization".

Detailed Answer in Step-by-Step Solution:

Objective: Prevent deployment failure due to utilization limits.

Understand Utilization: High load requires capacity or throttling.

Evaluate Options:

A: More instances—Scales horizontally—correct.

B: Delete—Stops service, not a fix—incorrect.

C: Fewer instances—Worsens issue—incorrect.

D: Larger VM—Scales vertically—correct.

E: Reduce bandwidth—Controls load—correct.

Reasoning: A and D increase capacity, E manages demand—effective trio.

Conclusion: A, D, E are correct.

OCI documentation states: “To avoid utilization limits, increase instances (A), use a larger compute shape (D), or reduce load balancer bandwidth (E) to manage request rates.” B stops service, C reduces capacity—only A, D, E align with OCI’s deployment scaling options.

Oracle Cloud Infrastructure Data Science Documentation, "Model Deployment Scaling".