Free and Premium Oracle 1z0-1110-25 Dumps Questions Answers

Detailed Answer in Step-by-Step Solution:

Objective: Share a custom conda environment in OCI Data Science.

Understand Commands: OCI provides odsc CLI for environment management.

Evaluate Options:

A: clone duplicates an environment locally—not for sharing.

B: publish uploads the environment to Object Storage for team access—correct.

C: modify doesn’t exist as a standard command.

D: install sets up an environment locally—not for sharing.

Reasoning: Sharing requires publishing to a shared location (Object Storage), which publish achieves.

Conclusion: B is the correct command.

The OCI Data Science CLI documentation states: “Use odsc conda publish to package and upload a custom conda environment to an Object Storage Bucket, making it accessible to other users.” clone (A) is for local duplication, modify (C) isn’t valid, and install (D) is for local setup—not sharing. B is the designated sharing mechanism.

Oracle Cloud Infrastructure Data Science CLI Reference, "odsc conda publish".

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:

Understand OCI IAM Policies: Policies control access using verbs like “inspect,” “read,” “use,” and “manage.”

Define Requirement: Consultants need view-only access without modification rights.

Evaluate Verbs:

A. Use: Allows interaction (e.g., running jobs)—too permissive.

B. Inspect: Grants view-only access to resource details—matches requirement.

C. Manage: Full control (create, update, delete)—too permissive.

D. Read: Includes viewing content (e.g., data), not just metadata—slightly broader than needed.

Conclusion: “Inspect” (B) is the precise verb for view-only access to resource details.

In OCI Identity and Access Management (IAM), the “inspect” verb allows listing and viewing resource metadata without granting modification or data access rights, ideal for this scenario. This is confirmed in the IAM policy reference. (Reference: Oracle Cloud Infrastructure Documentation, "IAM Policy Verbs").

Detailed Answer in Step-by-Step Solution:

Objective: Efficiently manage varying hyperparameters in OCI Data Science Jobs.

Understand OCI Jobs: Jobs execute predefined tasks with configurable inputs (e.g., env vars, args).

Evaluate Options:

A: New job per run with env vars—Redundant job creation, inefficient.

B: New job per run with args—Similarly inefficient due to repeated setup.

C: Hardcode params, new job per change—Highly inefficient, requires code edits.

D: Single job, flexible params via env vars or args—Efficient, reusable—correct.

Reasoning: D minimizes job creation, allows runtime flexibility via configuration—best practice.

Conclusion: D is correct.

OCI documentation states: “For Jobs with varying hyperparameters, write code to accept environment variables or command-line arguments (D), then configure these per Job Run using the OCI Console or SDK—most efficient approach.” Options A, B, and C involve unnecessary job proliferation or code changes—only D aligns with OCI’s design for parameterized runs.

Oracle Cloud Infrastructure Data Science Documentation, "Configuring Job Runs with Parameters".

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").

Detailed Answer in Step-by-Step Solution:

Define Feature Engineering: It’s the process of creating or modifying features to improve model performance.

Evaluate Options:

A: Parameter tuning adjusts model hyperparameters (e.g., learning rate), not features.

B: Model interpretation (e.g., SHAP values) explains predictions, not feature creation.

C: Transforming features (e.g., normalizing, encoding) is the core of feature engineering—correct.

D: Understanding features occurs during exploration, not engineering.

Reasoning: Feature engineering directly manipulates data inputs (e.g., converting timestamps to day-of-week), distinct from tuning or interpretation.

Conclusion: C is the precise definition.

OCI Data Science documentation defines feature engineering as “the process of transforming raw data into features that better represent the underlying problem to the predictive models, resulting in improved model accuracy.” Examples include scaling or creating interaction terms, aligning with C. Other options (A, B, D) relate to different ML stages.

Oracle Cloud Infrastructure Data Science Documentation, "Feature Engineering Overview".

Detailed Answer in Step-by-Step Solution:

Objective: Identify correct statements about installing Python libraries in OCI Data Science.

Understand Notebook Sessions: Run in a managed environment with specific permissions.

Evaluate Options:

A: False—No root privileges; users operate as datascience with limited sudo.

B: True—pip install from PyPI works with internet access (e.g., NAT Gateway).

C: False—Yum isn’t available; pip is the primary tool as a normal user.

D: False—Misstated; youcaninstall non-preinstalled libraries—likely a typo (intended opposite).

E: True—Custom repos are supported with proper network config.

Correct Interpretation: Assuming D’s intent was “Youcaninstall…” (common exam error), B, D (corrected), E are true.

Conclusion: B, D (corrected), E are correct.

OCI documentation states: “In notebook sessions, you can install Python libraries from PyPI (B) or private repositories (E) using pip, but root privileges (A) are not granted—users operate asdatascience.” Yum (C) isn’t supported, and D’s phrasing contradicts capability—corrected, it’s true you can install beyond preinstalled. B, D (adjusted), E align with OCI’s flexibility.

Oracle Cloud Infrastructure Data Science Documentation, "Installing Libraries in Notebook Sessions".

Detailed Answer in Step-by-Step Solution:

Objective: Visualize correlation between continuous and categorical features using ADS SDK.

Understand Correlation Types:

Continuous vs. Continuous: Pearson correlation.

Categorical vs. Categorical: Cramer’s V.

Continuous vs. Categorical: Correlation ratio (eta).

Evaluate Options:

A. corr(): General correlation (Pearson), not suited for mixed types—incorrect.

B. correlation_ratio_plot(): Plots correlation ratio for continuous-categorical—correct.

C. pearson_plot(): Not an ADS method; Pearson is continuous-only—incorrect.

D. cramersv_plot(): Cramer’s V for categorical-categorical—incorrect.

Reasoning: Correlation ratio measures association between continuous and categorical variables—ideal for heatmap in this mixed scenario.

Conclusion: B is correct.

OCI documentation states: “The correlation_ratio_plot() method (B) in ADS SDK generates a heatmap displaying the correlation ratio between continuous and categorical features, suitable for identifying highly correlated features for removal.” corr() (A) defaults to Pearson, pearson_plot() (C) isn’t real, and cramersv_plot() (D) is for categorical pairs—only B aligns with OCI’s ADS capabilities for this use case.

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

Detailed Answer in Step-by-Step Solution:

Objective: Identify the file system type for OCI File Storage.

Understand File Storage: Network-attached storage in OCI.

Evaluate Options:

A: NFSv3—Network File System, used by File Storage—correct.

B: iSCSI—Block storage protocol, not File Storage.

C: Paravirtualized—Virtualization mode, not file system.

D: NVMe SSD—Hardware, not file system.

Reasoning: NFSv3 is OCI File Storage’s protocol.

Conclusion: A is correct.

OCI documentation states: “File Storage uses NFSv3 (A) as its file system protocol, providing shared storage across instances.” B, C, and D are unrelated—only A aligns with OCI’s File Storage design.

Oracle Cloud Infrastructure File Storage Documentation, "File System Protocol".

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:

Step 1: Install, configure, and authenticate Git: Git must be installed and configured (e.g., git config --global user.name) before any repository actions.

Step 2: Configure SSH keys: SSH keys are set up (e.g., ssh-keygen) and added to the remote service (e.g., GitHub, OCI Code Repository) for secure access.

Step 3: Create local and remote Git repository: Initialize a local repo (git init) and create/link a remote repo (e.g., git remote add origin).

Step 4: Commit files: Add files (git add .) and commit them locally (git commit -m "message").

Step 5: Push to remote: Push local commits to the remote repo (git push origin main).

Evaluate Options: Only D (1, 2, 3, 4, 5) follows this logical sequence; others (e.g., A starts with SSH before Git installation) are illogical.

The standard Git workflow in OCI Data Science or general practice begins with installing Git (1), configuring SSH for secure access (2), creating repositories (3), committing locally (4), and pushing remotely (5). The OCI Code Repository documentation aligns with this: “First, install Git and configure authentication (e.g., SSH), then set up repositories and manage code.” D is the only option reflecting this industry-standard process.

Oracle Cloud Infrastructure Code Repository Documentation, "Git Workflow Basics".

Detailed Answer in Step-by-Step Solution:

Objective: Troubleshoot a 404 authentication error when saving a model using ADS SDK with resource principal.

Understand Resource Principal: Allows notebook sessions to act as principals via dynamic groups and policies—no user credentials needed.

Analyze 404 Error: Indicates an authorization failure—likely missing permissions or misconfigured resource principal.

Evaluate Options:

A: True—Dynamic group must include notebook sessions (e.g., resource.type = 'datasciencenotebooksession') to authenticate.

B: False—Block volume stores artifacts locally, but saving to the catalog is a permission issue, not storage.

C: True—Policy must grant manage data-science-models to the dynamic group for catalog access.

D: False—Service gateway ensures network access, but 404 is auth-related, not connectivity.

E: False—Resource principal uses dynamic group policies, not user group policies.

Reasoning: A (group inclusion) and C (policy permission) are critical for resource principal auth—others are tangential.

Conclusion: A and C are correct.

OCI documentation states: “To use resource principal with ADS SDK for model catalog operations, ensure (1) a dynamic group includes the notebook session with a matching rule (e.g., all {resource.type = 'datasciencenotebooksession'}) and (2) a policy grants the dynamic group manage data-science-models permissions in the compartment.” B is unrelated (storage location), D is network-focused, and E applies to user auth—not resource principal. A 404 error flags missing auth, fixed by A and C.

Oracle Cloud Infrastructure Data Science Documentation, "Using Resource Principals with ADS SDK".

Detailed Answer in Step-by-Step Solution:

Objective: Define LIME’s core concept.

Understand LIME: Explains individual predictions with local surrogate models.

Evaluate Options:

A: Complex global, simple local—Correct LIME principle.

B: Agnosticism—True but not the key idea.

C: Global/local similarity—False.

D: Local vs. global agnosticism—Incorrect distinction.

Reasoning: A captures LIME’s local approximation focus.

Conclusion: A is correct.

OCI documentation notes: “LIME (A) explains predictions by approximating complex global models with simpler local surrogate models around specific instances.” B, C, and D misalign—only A reflects LIME’s foundational idea per OCI’s interpretability tools.

Oracle Cloud Infrastructure Data Science Documentation, "Model Interpretability - LIME".

Detailed Answer in Step-by-Step Solution:

Define Conda: Conda is a widely used tool for managing packages and environments in data science.

Evaluate Options:

A: Partially true—Conda manages dependencies, but it’s broader (an environment system).

B: Incorrect—Kernels (e.g., Jupyter) are separate; Conda manages environments.

C: Correct—Conda is an open-source tool for creating isolated environments with specific packages.

D: Incorrect—Not specific to Oracle AI; it’s a general tool.

Reasoning: C captures Conda’s full scope as an open-source system, beyond just dependency management (A).

Conclusion: C is the most accurate.

OCI documentation describes Conda as “an open-source package and environment management system that allows data scientists to create isolated environments with specific versions of Python and libraries.” A is too narrow, B misaligns with kernel concepts, and D ties it incorrectly to Oracle AI. C aligns with Conda’s official definition and OCI’s usage.

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

Detailed Answer in Step-by-Step Solution:

Objective: Sequence OCI AutoML pipeline stages.

Stages:

Adaptive sampling: Reduces data size if large.

Feature selection: Picks relevant features.

Algorithm selection: Chooses best model type.

Hyperparameter tuning: Optimizes model params.

Evaluate: C (sampling, features, algorithms, tuning) matches logical flow—data first, then model.

Reasoning: Sampling precedes feature work—standard in OCI.

Conclusion: C is correct.

OCI documentation states: “AutoML pipeline runs 1) adaptive sampling, 2) feature selection, 3) algorithm selection, 4) hyperparameter tuning (C).” Sampling reduces data first, then features and models are optimized—other orders (A, B, D) misalign with OCI’s sequence.

Oracle Cloud Infrastructure AutoML Documentation, "Pipeline Sequence".

Detailed Answer in Step-by-Step Solution:

Objective: Identify the firewall type protecting against web app attacks like SQL injection and XSS.

Understand Firewall Types:

Stateful Inspection: Tracks connection states, not app-specific.

Web Application Firewall (WAF): Targets web app vulnerabilities.

Incident Firewall: Not a recognized term.

Packet Filtering: Basic packet rules, not app-aware.

Evaluate Options:

A: Stateful—General network, not web-specific—incorrect.

B: WAF—Designed for SQLi, XSS—correct.

C: Incident—Non-existent—incorrect.

D: Packet—Low-level, not app-focused—incorrect.

Reasoning: WAF specializes in web app security—matches requirement.

Conclusion: B is correct.

OCI documentation states: “Web Application Firewall (WAF) (B) protects against web application attacks like SQL injection and cross-site scripting by inspecting HTTP traffic.” A and D handle network-level threats, C isn’t real—only B aligns with OCI’s WAF purpose.

Oracle Cloud Infrastructure WAF Documentation, "Overview".

Detailed Answer in Step-by-Step Solution:

Objective: Identify an invalid notebook session practice.

Evaluate Options:

A: Valid—Long runs should use Jobs, not notebooks.

B: Valid—Terminal runs with logging are supported.

C: Valid—Multi-user conflicts are a concern.

D: Invalid—Copying from Object Storage to block volume is unnecessary; direct access is best.

E: Valid—Resource principals are secure and recommended.

Reasoning: D contradicts OCI’s direct-access efficiency.

Conclusion: D is incorrect.

OCI documentation advises: “Access data in Object Storage directly from notebook sessions using SDKs or resource principals—avoid local copies (D) unless necessary.” A, B, C, and E are best practices—D is inefficient and not standard.

Oracle Cloud Infrastructure Data Science Documentation, "Notebook Session Best Practices".

Detailed Answer in Step-by-Step Solution:

Objective: Identify a feature engineering technique in ML.

Understand Feature Engineering: Transforms raw data into model-ready features.

Evaluate Options:

A. PCA: Reduces dimensionality—feature engineering—correct.

B. K-means: Clustering model—not feature engineering.

C. SVM: Classification model—not feature engineering.

D. Gradient boosting: Model training—not feature engineering.

Reasoning: PCA creates new features via transformation—fits definition.

Conclusion: A is correct.

OCI documentation states: “Feature engineering techniques like Principal Component Analysis (PCA) (A) transform data into new features to enhance model performance.” B, C, and D are modeling techniques—only A aligns with OCI’s feature engineering stage.

Oracle Cloud Infrastructure Data Science Documentation, "Feature Engineering Techniques".

Detailed Answer in Step-by-Step Solution:

Understand OCI Anomaly Detection: This service trains models to detect anomalies in multivariate data (e.g., sensor readings), requiring data to be accessible within OCI’s ecosystem.

Assess Storage Requirements: The training data must be in a scalable, OCI-compatible location that the Anomaly Detection service can access programmatically.

Evaluate Options:

A. Your local machine: Data on a local machine isn’t directly accessible to OCI services without upload, making it impractical for cloud-based training.

B. MySQL database: While OCI supports MySQL, Anomaly Detection doesn’t natively integrate with it for training data; it prefers file-based input.

C. Autonomous Data Warehouse: This is a database for analytics, not the default storage for Anomaly Detection training data, which expects CSV/JSON files.

D. Object Storage Bucket: OCI Object Storage is a scalable, durable storage service that Anomaly Detection uses to ingest training data (e.g., CSV files).

Reasoning: Object Storage is the standard for large-scale data in OCI services, offering seamless integration with Anomaly Detection via APIs or SDKs.

Conclusion: D is the correct choice as it aligns with the service’s architecture.

The OCI Anomaly Detection service requires training data to be uploaded to an Object Storage Bucket in formats like CSV or JSON. This is explicitly outlined in the official documentation, which states that users must “upload the training dataset to an OCI Object Storage bucket” before creating a data asset for model training. Local storage (A) isn’t viable for cloud processing, and databases like MySQL (B) or Autonomous Data Warehouse (C) aren’t supported as primary inputs. Object Storage (D) provides the scalability and accessibility needed for multivariate anomaly detection workflows.

Oracle Cloud Infrastructure Anomaly Detection Documentation, "Preparing Training Data" section.

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:

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: 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 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:

Objective: Identify the function that measures the difference between predicted and actual values in machine learning.

Understand ML Functions:

Optimizer function: Adjusts model parameters to minimize error (e.g., gradient descent)—it uses the cost, not defines it.

Fit function: Trains the model by fitting it to data—process-oriented, not a measure.

Update function: Typically updates weights during training—not a standard term for error measurement.

Cost function: Quantifies prediction error (e.g., MSE, cross-entropy)—directly represents the difference.

Evaluate Options:

A: Optimizer minimizes the cost, not the cost itself—incorrect.

B: Fit executes training, not error definition—incorrect.

C: Update is vague and not a standard ML term for this—incorrect.

D: Cost function (e.g., loss) measures prediction vs. target—correct.

Reasoning: The cost function (or loss function) is the mathematical representation of error, guiding optimization.

Conclusion: D is the correct answer.

In OCI Data Science, the documentation explains: “The cost function (or loss function) measures the difference between the model’s predicted values and the actual target values, such as mean squared error for regression or cross-entropy for classification.” Optimizers (A) use this to adjust weights, fit (B) is a training step, and update (C) isn’t a defined function here—only the cost function (D) fits the description. This aligns with standard ML terminology and OCI’s AutoML processes.

Oracle Cloud Infrastructure Data Science Documentation, "Machine Learning Concepts - Cost Functions".

Detailed Answer in Step-by-Step Solution:

Objective: Troubleshoot a 404 auth error when saving a model with resource principal.

Understand Resource Principal: Allows notebook sessions to act as principals via dynamic groups and policies—no user credentials.

Analyze 404 Error: Indicates permission failure—likely IAM misconfiguration.

Evaluate Options:

A: Block volume storage—Irrelevant to auth; it’s about saving locally—incorrect.

B: Dynamic group matching—Ensures notebook is recognized—correct.

C: User group policy—Not used with resource principal—incorrect.

D: Dynamic group policy—Grants catalog access—correct.

E: Service Gateway—Network-related, not auth-specific—incorrect.

Reasoning: Resource principal needs B (group inclusion) and D (policy perms)—404 points to these.

Conclusion: B and D are correct.

OCI documentation states: “For ADS SDK to save to the Model Catalog using resource principal, ensure (1) a dynamic group includes notebook sessions with matching rules (e.g., resource.type ='datasciencenotebooksession') (B), and (2) a policy grants manage data-science-models to that dynamic group (D).” A is storage, C is user-based, E is network—only B and D fix the auth issue per OCI’s IAM setup.

Oracle Cloud Infrastructure Data Science Documentation, "Resource Principal with Model Catalog".

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:

Understand OCI Data Science Pipelines: Pipelines automate ML workflows with configurable steps.

Check Override Mechanism: Defaults (e.g., compute shape, storage) can be modified before execution via the OCI Console, SDK, or CLI.

Evaluate Options:

A: False—Overrides can occur post-creation, before running.

B: False—Any authorized user, not just admins, can override defaults.

C: True—Settings can be adjusted before execution starts.

D: False—Defaults can be changed post-creation, pre-execution.

Conclusion: C is correct as it reflects the flexibility of pipeline configuration.

OCI Data Science Pipelines allow users to override default settings (e.g., compute resources, environment variables) before execution, as noted in the official documentation. This can be done via the UI or programmatically, offering flexibility beyond creation time (A) and without admin-only restrictions (B). (Reference: Oracle Cloud Infrastructure Data Science Pipelines Documentation, "Configuring Pipelines").

Detailed Answer in Step-by-Step Solution:

Objective: Prevent deployment failure due to high utilization.

Evaluate Options:

A: More instances—Scales capacity—correct.

B: Delete—Stops service, not a solution.

C: Fewer instances—Worsens utilization.

D: Larger VM—Increases resource capacity—correct.

E: Reduce bandwidth—Limits load—correct.

Reasoning: A and D boost capacity, E controls demand—proactive fixes.

Conclusion: A, D, E are correct.

OCI documentation advises: “To handle high utilization, increase instances (A), use a larger compute shape (D), or adjust load balancer bandwidth (E) to manage request volume.” B stops service, C reduces capacity—only A, D, E prevent failure per OCI’s scaling options.

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

Detailed Answer in Step-by-Step Solution:

Objective: Identify protection for data at rest/transit with customer control.

Evaluate Options:

A: Controls—Broad, not specific to encryption.

B: Isolation—Separates tenants, not crypto-focused.

C: Encryption—Secures data, allows key management—correct.

D: Federation—Auth sharing, not data protection.

Reasoning: C provides crypto control (e.g., Vault keys).

Conclusion: C is correct.

OCI documentation states: “Data encryption (C) protects data at rest and in transit, with customer-managed keys in OCI Vault meeting compliance needs.” A and B are broader, D is unrelated—only C fits per OCI’s security model.

Oracle Cloud Infrastructure Security Documentation, "Data Encryption".

Detailed Answer in Step-by-Step Solution:

Objective: Address declining model performance and improve churn prediction.

Analyze Issue: Poor predictions suggest data drift or model staleness—common ML challenges.

Evaluate Options:

A. Drift monitoring: Tracks changes in data distribution—identifies root cause of accuracy drop—correct.

B. Redeploy the model: Repeats deployment without fixing the issue—ineffective alone.

C. Operational monitoring: Tracks system health (e.g., latency), not prediction quality.

D. Retrain the model: Updates model with new data—directly improves accuracy—correct.

E. Validate with recent data: Checks performance but doesn’t fix—diagnostic, not corrective.

Reasoning: A diagnoses drift (cause), D retrains to adapt (solution)—best combo to verify and lower churn.

Conclusion: A and D are correct.

OCI documentation advises: “Drift monitoring (A) detects shifts in data distribution that degrade model performance, while retraining (D) with fresh data restores accuracy.” Redeployment (B) doesn’t address drift, operational monitoring (C) focuses on infra, and validation (E) is a check—not a fix. A and D align with OCI’s model maintenance strategy.

Oracle Cloud Infrastructure Data Science Documentation, "Model Monitoring and Retraining".

Detailed Answer in Step-by-Step Solution:

Objective: Encode categorical features in a Data Science context (likely ADS SDK).

Understand Encoding: Converts categories (e.g., countries) to numerical forms.

Evaluate Options:

A: Not a standard ADS method—incorrect.

B: General transformation, not specific encoding—incorrect.

C: OneHotEncoder—Standard for categorical encoding—correct.

D: Visualization, not encoding—incorrect.

Reasoning: One-hot encoding creates binary columns—ideal for multiple categories.

Conclusion: C is correct.

OCI documentation states: “In ADS SDK, use OneHotEncoder (C) from sklearn (or similar) to encode categorical features like ‘countries’ into binary vectors for modeling.” A isn’t real, B is too broad, D is unrelated—only C fits OCI’s encoding practice.

Oracle Cloud Infrastructure Data Science Documentation, "Feature Encoding with ADS".

Detailed Answer in Step-by-Step Solution:

Objective: Locate score.py output in OCI model deployment.

Understand Deployment: Logs are centralized in OCI Logging.

Evaluate Options:

A: VM file—Not default; requires custom config—incorrect.

B: Predict log in OCI Logging—Standard destination—correct.

C: Cloud Shell—Separate tool, not logs—incorrect.

D: Console—UI, not raw logs—incorrect.

Reasoning: B aligns with OCI’s logging integration.

Conclusion: B is correct.

OCI documentation states: “score.py stdout and stderr are captured in the predict log within OCI Logging service (B), configured during deployment.” A isn’t standard, C and D don’t receive logs—only B fits OCI’s logging setup.

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

Detailed Answer in Step-by-Step Solution:

Objective: Identify three required params for an OCI Data Flow app config.

Understand Data Flow: Runs Spark apps; needs compartment, storage, and identity.

Evaluate Options:

A: Archive path—Optional if script is in Object Storage—incorrect.

B: Local script path—Not needed; script is uploaded—incorrect.

C: Compartment—Required for resource scope—correct.

D: Bucket—Required for script storage/access—correct.

E: Display name—Required for app identification—correct.

Reasoning: C, D, E are mandatory metadata for Data Flow creation—script location is specified via bucket.

Conclusion: C, D, E are correct.

OCI documentation states: “To create a Data Flow application, configure the compartment OCID (C), Object Storage bucket for the PySpark script (D), and a display name (E) in the application object.” Local paths (B) or archives (A) are optional or handled separately—only C, D, E are required per OCI’s Data Flow API spec.

Oracle Cloud Infrastructure Data Flow Documentation, "Creating Applications".

Detailed Answer in Step-by-Step Solution:

Objective: Find the OCI service for accessing large public datasets.

Evaluate Options:

A: Oracle Databases—General-purpose, not dataset-focused.

B: ADW—Analytics warehouse, not a dataset repository.

C: OCI Data Science—ML platform, not a dataset provider.

D: Oracle Open Data—Free, public datasets (e.g., geospatial).

Reasoning: Open Data provides pre-existing large datasets for research.

Conclusion: D is correct.

OCI documentation states: “Oracle Open Data provides free access to large, curated datasets, such as geospatial data, ideal for researchers.” Databases (A) and ADW (B) are for storage/analytics, Data Science (C) is for ML—not datasets—only Open Data (D) fits.

Oracle Cloud Infrastructure Open Data Documentation, "Overview".

Detailed Answer in Step-by-Step Solution:

Objective: Differentiate data scientist vs. data engineer roles.

Define Roles:

Data Engineer: Builds pipelines, prepares data.

Data Scientist: Analyzes data, builds models.

Evaluate Options:

A: Engineer preps, scientist analyzes—Correct division.

B: Reverses roles—Incorrect.

C: Overlaps roles—Scientist doesn’t typically build pipelines.

D: Misaligns—Analyst isn’t the focus.

Reasoning: A reflects standard role separation.

Conclusion: A is correct.

OCI documentation notes: “Data engineers focus on collecting and preparing data through pipelines, while data scientists analyze it to derive insights and build models.” A aligns, B inverts, C overcomplicates, and D shifts focus—only A is accurate.

Oracle Cloud Infrastructure Data Science Documentation, "Roles in Data Science".

Detailed Answer in Step-by-Step Solution:

Objective: Identify the invalid MFA statement.

Evaluate Options:

A: True—Users can’t disable MFA; admin-controlled.

B: False—Multiple devices can be registered—invalid.

C: True—Authenticator app is required.

D: True—Admins can disable MFA.

Reasoning: B contradicts OCI’s multi-device support.

Conclusion: B is incorrect.

OCI documentation states: “Users can register multiple devices for MFA (B is false), must use an authenticator app (C), and cannot disable MFA themselves (A)—admins can (D).” Only B is not valid per OCI’s IAM MFA policy.

Oracle Cloud Infrastructure IAM Documentation, "Multi-Factor Authentication".

Detailed Answer in Step-by-Step Solution:

Objective: Identify the ADS SDK class for visualizing model performance comparison.

Understand ADS Classes: Each serves a specific ML purpose—visualization requires evaluation tools.

Evaluate Options:

A. EvaluationMetrics: Likely a typo—meant EvaluationsMetrics? Not a standalone class for visualization.

B. ADSEvaluator: Designed to evaluate and visualize model performance (e.g., ROC curves)—correct.

C. ADSExplainer: Explains model predictions (e.g., SHAP), not comparative visualization.

D. ADSTuner: Tunes hyperparameters, not for visualization.

Reasoning: ADSEvaluator provides comparative plots (e.g., precision-recall) for multiple models, including baselines.

Conclusion: B is correct.

OCI documentation states: “The ADSEvaluator class in ADS SDK (B) enables visualization of model performance metrics, such as ROC curves and confusion matrices, for multiple models on a test set, including baselines.” EvaluationMetrics (A) isn’t a class, ADSExplainer (C) focuses on interpretability, and ADSTuner (D) is for tuning—only B fits the visualization need per OCI’s ADS toolkit.

Oracle Cloud Infrastructure ADS SDK Documentation, "ADSEvaluator Class".

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".

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:

Understand OCI Data Science Jobs: This service automates ML tasks (e.g., training, evaluation) with configurable, repeatable executions.

Key Characteristics: Jobs run on OCI’s infrastructure, managed by Oracle, not the customer or third parties, and are specific to Data Science, not general DevOps.

Evaluate Options:

A: Correct—Jobs are defined by users (e.g., via scripts) and executed on OCI’s fully managed compute resources.

B: Incorrect—Infrastructure is managed by OCI, not the customer.

C: Incorrect—No third-party cloud integration; it’s OCI-specific.

D: Incorrect—Jobs are for Data Science tasks (e.g., ML training), not all DevOps workloads (e.g., CI/CD pipelines).

Reasoning: “Fully managed” means OCI handles provisioning and scaling, aligning with A.

Conclusion: A accurately reflects the service’s purpose and operation.

OCI Data Science Jobs “allow users to define and execute repeatable machine learning tasks, such as model training or batch processing, on fully managed OCI infrastructure.” This eliminates customer management (B), third-party clouds (C), or broad DevOps scope (D). The documentation emphasizes automation and management by OCI, making A the precise description.

Oracle Cloud Infrastructure Data Science Documentation, "Overview of Jobs" section.

Detailed Answer in Step-by-Step Solution:

Objective: Identify the key difference between supervised and unsupervised learning.

Define Types:

Supervised: Uses labeled data (e.g., input-output pairs) to predict outcomes.

Unsupervised: Uses unlabeled data to find patterns (e.g., clustering).

Evaluate Options:

A: Labeled vs. unlabeled—Core distinction, correct.

B: Monitoring—Misleading, not the primary difference.

C: Image recognition—False, supervised applies broadly.

D: Data Engineer—Irrelevant to learning type.

Reasoning: A captures the foundational data difference.

Conclusion: A is correct.

OCI documentation states: “Supervised learning uses labeled data to train models for prediction, while unsupervised learning analyzes unlabeled data to discover patterns.” B, C, and D misrepresent this—only A aligns with OCI’s ML definitions and industry standards.

Oracle Cloud Infrastructure Data Science Documentation, "Machine Learning Types".

Detailed Answer in Step-by-Step Solution:

Objective: Determine minimum log storage duration before archiving in Logging Analytics.

Understand Logging Analytics: Logs are active before archival.

Evaluate Options:

A: 60 days—Too long for minimum.

B: 10 days—Too short.

C: 30 days—Standard minimum—correct.

D: 15 days—Not OCI’s default.

Reasoning: 30 days is OCI’s documented minimum active period.

Conclusion: C is correct.

OCI documentation states: “Logs in Logging Analytics remain active for a minimum of 30 days (C) before archiving, ensuring availability for analysis.” B and D are shorter, A is longer—only C matches OCI’s policy.

Oracle Cloud Infrastructure Logging Analytics Documentation, "Log Retention".

Detailed Answer in Step-by-Step Solution:

Analyze Git Operations: Identify which depend on internet speed.

Evaluate Options:

A. Staging (git add): Local operation—adds files to the index; no network involved.

B. Updating local repo (git pull): Downloads remote changes—requires internet, slowed by poor connectivity.

C. Pushing changes (git push): Uploads local commits to remote—network-dependent, delayed by slow speed.

D. Committing (git commit): Local snapshot—no network needed.

E. Converting to Git repo (git init): Local initialization—no internet required.

Reasoning: Only B and C involve network transfers, directly impacted by slow internet.

Conclusion: B and C are the correct choices.

Git operations like git pull (B) and git push (C) rely on network communication with a remote repository, such as OCI Code Repository, and are documented as “bandwidth-sensitive” in OCI’s guides. Local actions like staging (A), committing (D), and initializing (E) occur on the user’s machine, unaffected by internet speed. This matches standard Git behavior and OCI’s implementation.

Oracle Cloud Infrastructure Data Science Documentation, "Using Git in Notebook Sessions".

Detailed Answer in Step-by-Step Solution:

Understand Model Catalog: The OCI Model Catalog stores trained models and associated details.

Identify Shareable Artifacts: When sharing, all components—model file, metadata (e.g., name, description), hyperparameters (e.g., learning rate), and metrics (e.g., accuracy)—are accessible.

Evaluate Options:

A: Excludes the model itself—incorrect.

B: Excludes metrics—incorrect.

C: Excludes metadata and hyperparameters—incorrect.

D: Includes all components—correct.

Conclusion: D is comprehensive and accurate.

The OCI Model Catalog allows sharing of the model artifact (the trained model), metadata, hyperparameters, and performance metrics, enabling full examination by colleagues. This isdetailed in the official documentation. (Reference: Oracle Cloud Infrastructure Data Science Documentation, "Model Catalog Overview").

Detailed Answer in Step-by-Step Solution:

Objective: Select two metrics for multiclass classification in AutoML.

Understand Multiclass Metrics: Focus on class-specific performance—classification, not regression.

Evaluate Options:

A. Recall: Measures true positives per class—key for multiclass—correct.

B. Mean squared error: Regression metric—incorrect.

C. F1 Score: Balances precision and recall—standard for multiclass—correct.

D. R-Squared: Regression fit—incorrect.

E. Explained variance: Regression metric—incorrect.

Reasoning: A and C assess classification accuracy across multiple classes—fit AutoML’s evaluation.

Conclusion: A and C are correct.

OCI AutoML documentation states: “For multiclass classification, common evaluation metrics include recall (A) for per-class sensitivity and F1 Score (C) for balanced performance.” B, D, and E are regression-focused—only A and C are supported and relevant per OCI’s AutoML metrics suite.

Oracle Cloud Infrastructure AutoML Documentation, "Evaluation Metrics for Classification".

Detailed Answer in Step-by-Step Solution:

Data Access: The first step in any machine learning workflow is accessing the raw data. This involves retrieving the user commute data collected over the past 3 years from the bike-sharing platform’s storage system.

Data Exploration: Once data is accessed, it’s explored to understand its structure, quality, and patterns (e.g., missing values, distributions). This step helps identify what preprocessing is needed.

Feature Engineering: After understanding the data, features are created or transformed (e.g., commute duration, time of day) to improve model performance. This step precedes feature exploration because you need engineered features to analyze further.

Feature Exploration: This involves analyzing the engineered features (e.g., correlation analysis, importance ranking) to refine them or select the most relevant ones for modeling.

Modeling: Finally, the prepared data and features are used to train and evaluate a machine learning model.

Option C (Data Access, Data Exploration, Feature Engineering, Feature Exploration, Modeling) follows this logical sequence, aligning with standard ML workflows.

The correct order reflects the machine learning lifecycle as outlined in Oracle’s OCI Data Science documentation. Data Access is the initial step to retrieve data, followed by Data Exploration to assess it (e.g., using OCI Data Science Notebook Sessions with tools like pandas). Feature Engineering transforms raw data into meaningful inputs, followed by Feature Exploration to analyze feature importance (e.g., using ADS SDK’s correlation tools). Modeling is the final step where the model is built and trained. This sequence is consistent with Oracle’s recommended practices for building ML models in OCI Data Science (Reference: Oracle Cloud Infrastructure Data Science Service Documentation, "Machine Learning Lifecycle").

Detailed Answer in Step-by-Step Solution:

Objective: Identify a step in OCI’s AutoML pipeline.

Understand AutoML: Automates model building—includes preprocessing, selection, and tuning.

Evaluate Options:

A: Feature Extraction (e.g., PCA) isn’t explicitly part of OCI AutoML—too specific.

B: Saving to Model Catalog is post-AutoML, not a pipeline step.

C: Deployment is a separate action after AutoML—incorrect.

D: Feature Selection (e.g., choosing relevant features) is a core AutoML step—correct.

Reasoning: OCI AutoML automates feature selection, algorithm choice, and tuning—D fits.

Conclusion: D is correct.

OCI AutoML’s pipeline includes “feature selection, algorithm selection, adaptive sampling, and hyperparameter tuning,” per the documentation. Extraction (A) isn’t highlighted, while saving (B) and deployment (C) are post-process actions—only Feature Selection (D) is an integral automated step.

Oracle Cloud Infrastructure Data Science Documentation, "AutoML Pipeline".