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

Detailed Answer in Step-by-Step Solution:

Objective: Select an explanation technique for feature-target relationships with correlated features.

Evaluate Options:

A: Permutation—Breaks with high correlation.

B: LIME—Local, not global relationships.

C: Dependence—Not a standard term; vague.

D: ALE—Handles correlation, shows feature effects—correct.

Reasoning: ALE is robust to correlated features, ideal here.

Conclusion: D is correct.

OCI documentation states: “Accumulated Local Effects (ALE) (D) evaluates feature-target relationships, accounting for correlations, unlike permutation importance (A) which falters with high correlation.” B is local, C isn’t defined—only D fits per OCI’s explanation tools.

Oracle Cloud Infrastructure Data Science Documentation, "Model Explanation Techniques".

Detailed Answer in Step-by-Step Solution:

Understand Multiclass Classification: Metrics evaluate how well the model predicts multiple classes.

Evaluate Metrics:

A. Mean squared error: Used for regression, not classification.

B. Explained variance score: Regression metric, not suitable.

C. Recall: Measures true positive rate per class—key for classification.

D. F1-score: Balances precision and recall—widely used in multiclass.

E. R-squared: Regression metric, not applicable.

Select Two: Recall (C) and F1-score (D) are standard for multiclass classification.

Oracle AutoML supports metrics like recall and F1-score for multiclass classification, as they assess per-class performance and overall precision-recall balance, respectively. Regression metrics (A, B,E) are irrelevant here. (Reference: Oracle Cloud Infrastructure Data Science Documentation, "AutoML Metrics").

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: Classify a PagerDuty notification in OCI Monitoring.

Understand OCI Monitoring: Involves events, rules, and actions for notifications.

Evaluate Options:

A: Action—executes a response (e.g., notify PagerDuty) when triggered—correct.

B: Rule—defines conditions for triggering actions—precedes the action.

C: Function—serverless code, not directly tied to notifications.

D: Event—state change triggering a rule, not the notification itself.

Reasoning: The notification is the action taken after an event/rule—fits A.

Conclusion: A is correct.

OCI documentation states: “Actions in the Monitoring service execute responses, such as sending notifications to PagerDuty, when a rule’s condition is met based on an event.” Rules (B) set conditions, Functions (C) are unrelated, and Events (D) are triggers—only Action (A) describes the notification step.

Oracle Cloud Infrastructure Monitoring Documentation, "Actions Overview".

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:

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: 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: 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".

Detailed Answer in Step-by-Step Solution:

Objective: Identify IAM components in OCI.

Understand IAM: Manages access via policies, groups, etc.

Evaluate Options:

A: Policies—Core IAM component—correct.

B: Subnets—Networking, not IAM.

C: Instances—Compute, not IAM.

D: VCN—Networking, not IAM.

Reasoning: Only A is an IAM element—others are resources.

Conclusion: A is correct.

OCI documentation states: “IAM includes components like policies (A), groups, and compartments to control resource access.” B, C, and D are infrastructure, not IAM—only A fits per OCI’s IAM framework.

Oracle Cloud Infrastructure IAM Documentation, "IAM Components".

Detailed Answer in Step-by-Step Solution:

Understand OCI Data Labeling Capabilities: OCI Data Labeling is designed to annotate data for machine learning, supporting specific file types like images, text documents, and videos.

Evaluate Options:

A. Video footage: Supported for tasks like object detection or action recognition.

B. Images: Supported for image classification, object detection, etc.

C. Typewritten document: Supported as text data for tasks like entity extraction or classification.

D. Purchase orders: While potentially text-based, this is ambiguous without format clarification (e.g., PDF, image). OCI supports text annotation, but “purchase orders” isn’t a specific file type—it’s assumed as text here.

E. Audio recording: Not supported, as OCI Data Labeling focuses on visual and textual data, not audio.

Select Three: A (video), B (images), and C (text documents) are explicitly supported file types.

OCI Data Labeling supports annotating datasets of images, text, and videos, as per the official documentation. Video footage (A) can be annotated for tasks like object tracking, images (B) for classification or detection, and typewritten documents (C) for text-based annotations (e.g., named entity recognition). Audio files (E) are not supported, and while purchase orders (D) could be text, the question specifies “typewritten document” as a clearer match. (Reference: Oracle Cloud Infrastructure Data Labeling Service Documentation, "Supported Data Types").