1z0-1110-25 Oracle Cloud Infrastructure 2025 Data Science Professional Questions and Answers

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:

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:

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: Locate artifacts for ADS model deployment.

Understand ADS Deployment: Requires model artifacts (e.g., score.py) stored in OCI.

Evaluate Options:

A: Vault—Stores secrets, not models.

B: Depository—Not an OCI term.

C: Model Catalog—Stores models/artifacts for deployment—correct.

D: Artifactory—Not an OCI service.

Reasoning: Model Catalog is OCI’s model repository for ADS.

Conclusion: C is correct.

OCI documentation states: “ADS SDK deploys models from the Model Catalog, where trainedmodels and artifacts (e.g., score.py) are stored.” Vault (A) is for secrets, B and D aren’t real—only C supports ADS deployment.

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

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: 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:

Objective: Efficiently increase storage for an OCI Job.

Understand Jobs: Storage (block volume) is set at job creation, not dynamically adjustable.

Evaluate Options:

A: False—Jobs can’t edit storage post-creation; it’s fixed.

B: False—No environment variable adjusts storage size.

C: True—Create a new job with larger storage (e.g., 200 GB) and run it.

D: False—Refactoring code is inefficient compared to increasing storage.

Reasoning: C is the standard OCI process for adjusting resources.

Conclusion: C is correct.

OCI documentation states: “Storage size for a Data Science Job is specified during job creation (e.g., block volume size). To increase it, create a new job with a larger storage configuration and initiate a new run.” Editing (A) isn’t supported, variables (B) don’t apply, and refactoring (D) avoids the issue—only C is efficient.

Oracle Cloud Infrastructure Data Science Documentation, "Jobs - Storage Configuration".

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:

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 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: Identify ADS class for accessing datasets (e.g., scikit-learn).

Evaluate Options:

A: DatasetBrowser—Not an ADS class.

B: DatasetFactory—Loads datasets from sources like scikit-learn—correct.

C: ADSTuner—Hyperparameter tuning, not data access.

D: SecretKeeper—Manages credentials, not datasets.

Reasoning: DatasetFactory simplifies dataset loading (e.g., DatasetFactory.open()).

Conclusion: B is correct.

OCI documentation states: “DatasetFactory in ADS SDK provides methods to easily load datasets from libraries like scikit-learn or other sources (e.g., DatasetFactory.open('sklearn.datasets:load_iris')).” A isn’t real, C tunes models, and D handles secrets—only B fits.

Oracle Cloud Infrastructure ADS SDK Documentation, "DatasetFactory".

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: Identify the WAF component that controls request actions based on criteria.

Understand WAF Components:

Protection Rules: Define conditions and actions (e.g., allow, block, log).

Bot Management: Handles bot traffic, not general request rules.

Origin: Backend server endpoint, not rule-based.

WAF Policy: Umbrella config, but rules specify actions.

Evaluate Options:

A: Protection rules—Set specific criteria and actions—correct.

B: Bot Management—Bot-specific, not general requests.

C: Origin—Defines source, not actions.

D: WAF policy—Broad config, not the granular rules.

Reasoning: Protection rules directly manage request behavior—fit the requirement.

Conclusion: A is correct.

OCI documentation states: “Protection rules (A) in WAF define conditions (e.g., IP, URL) and actions (allow, block, log) for incoming requests.” Bot Management (B) targets bots, Origin (C) is a target server, and WAF Policy (D) encompasses rules but isn’t the action specifier—only A aligns with OCI’s WAF configuration.

Oracle Cloud Infrastructure WAF Documentation, "Protection Rules".

Detailed Answer in Step-by-Step Solution:

Objective: Identify a non-compliance document in OCI context.

Understand Compliance Docs: Formal attestations of adherence (e.g., SOC, ISO).

Evaluate Options:

A: Certificate—Proof of compliance (e.g., ISO)—compliance doc.

B: Pen test report—Security test result, not formal compliance—correct.

C: Attestation—Statement of compliance—compliance doc.

D: Bridge letter—Links audit periods—compliance doc.

Reasoning: B is operational, not a compliance artifact.

Conclusion: B is correct.

OCI documentation lists “compliance documents like certificates (A), attestations (C), and bridge letters (D) for standards like SOC or ISO; penetration test reports (B) are security assessments, not formal compliance docs.” Only B stands apart per OCI’s compliance terminology.

Oracle Cloud Infrastructure Compliance Documentation, "Compliance Artifacts".

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: Find optimal compute shape balancing cost and time.

Approach: Iterative testing with metrics (e.g., CPU/memory usage, runtime).

Evaluate Options:

A: Tuning parameters when underutilized—focuses on model, not shape optimization.

B: Strongest shape—Costly, ignores balance; overkill likely.

C: Scale up from small shape when fully utilized—Balances cost/time effectively.

D: Random start with pre-tests—Unsystematic and inefficient.

Reasoning: C incrementally increases resources based on utilization, optimizing both factors.

Conclusion: C is correct.

OCI documentation advises: “To optimize compute shape for Jobs, start with a small shape, monitor utilization (e.g., CPU, memory) and runtime via OCI Monitoring. If fully utilized, scale up until performance plateaus—balancing cost and speed.” A misfocuses on model tuning, B wastes cost, and D lacks structure—only C aligns with this method.

Oracle Cloud Infrastructure Data Science Documentation, "Optimizing ComputeShapes for Jobs".

Detailed Answer in Step-by-Step Solution:

Objective: Locate sources for ADS SDK in OCI.

Understand ADS SDK: A Python library for Data Science tasks (e.g., EDA).

Evaluate Options:

A: Big Data Service—Spark-focused, not ADS source.

B: Machine Learning—Separate service, not ADS-related.

C: Conda in OCI Data Science—Preinstalled ADS in notebook sessions.

D: PyPI—Public source to install ADS (pip install oracle-ads).

E: ADW—Database, not an SDK source.

Reasoning: C (preinstalled) and D (installable) are practical access points.

Conclusion: C and D are correct.

OCI documentation states: “The ADS SDK is available in OCI Data Science notebook sessions via preinstalled conda environments (C) and can be installed from PyPI (D) using pip install oracle-ads.” Big Data (A), Machine Learning (B), and ADW (E) don’t host ADS—only C and D apply.

Oracle Cloud Infrastructure Data Science Documentation, "ADS SDK Installation".

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 three data types for OCI Data Labeling (question likely incomplete—assuming B, C, D, E options).

Understand Data Labeling: Annotates data for ML—focuses on specific types.

Evaluate Options (Assuming Typical Set):

A: Audio—Not supported—incorrect.

B: Text Document—Supported (e.g., NER)—correct.

C: Images—Supported (e.g., object detection)—correct.

D: Graphs—Not a standard type—incorrect.

Assumed E: Videos—Supported but missing—adjust to fit.

Reasoning: OCI supports text, images, and videos—question lists only four, so B and C are definite.

Conclusion: B, C (third likely video, missing).

OCI documentation states: “Data Labeling supports text documents (B), images (C), and videos for annotation—audio (A) and graphs (D) are not included.” Question likely meant three from a larger set; B and C are confirmed per OCI’s Data Labeling capabilities.

Oracle Cloud Infrastructure Data Labeling Documentation, "Supported Data Types".

Detailed Answer in Step-by-Step Solution:

Objective: Identify a true statement about OCI Data Science Jobs.

Understand OCI Jobs: Jobs automate ML tasks (e.g., training) on managed infrastructure.

Evaluate Options:

A: True—Jobs provision OCI compute resources on-demand for task execution.

B: False—Users define custom tasks (e.g., Python scripts), not limited to standard ones.

C: False—Infrastructure is fully managed by OCI, not user-managed.

D: False—Multiple artifacts (e.g., ZIP with dependencies) can be used, not just one file.

Reasoning: A reflects OCI’s managed, on-demand provisioning model for Jobs.

Conclusion: A is correct.

The OCI Data Science documentation states: “Jobs provision compute infrastructure on-demand to execute user-defined tasks, such as model training or data processing, on fully managed OCI resources.” B is incorrect (customization is allowed), C contradicts the managed nature, and D misstates artifact flexibility—only A accurately describes Jobs.

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

Detailed Answer in Step-by-Step Solution:

Objective: Assess required skills for data scientists.

Analyze Skills:

Technical: Coding, stats, ML.

Nontechnical: Communication, business acumen.

Traits: Curiosity, problem-solving.

Reasoning: Success requires this mix—e.g., explaining models to stakeholders.

Conclusion: A (True) is correct.

OCI documentation states: “Effective data scientists combine technical skills (e.g., Python), nontechnical skills (e.g., storytelling), and traits like analytical thinking.” This holistic requirement is true (A), not false (B).

Oracle Cloud Infrastructure Data Science Documentation, "Data Scientist Skills".

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: 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: 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: Identify interfaces to save a 2 GB model to the Model Catalog.

Evaluate Options:

A: OCI CLI—Supports Data Science tasks—possible but not primary.

B: ADS SDK—Designed for model catalog ops—correct.

C: ODSC CLI—Not standard; likely typo for OCI CLI.

D: Console—GUI for catalog uploads—correct.

E: OCI Python SDK—Programmatic catalog access—correct.

F: Git CLI—Version control, not catalog-related.

Reasoning: B, D, E are OCI’s primary interfaces; A is valid but less emphasized.

Conclusion: B, D, E are correct (A plausible but not top-tier).

OCI documentation lists “ADS SDK (B), OCI Console (D), and OCI Python SDK (E) as primary methods to save models to the Model Catalog.” OCI CLI (A) works but isn’t highlighted, C isn’t real, and F is unrelated—B, D, E are the standard trio.

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

Detailed Answer in Step-by-Step Solution:

Analyze OCI Open Data: OCI Open Data is a free service providing access to public datasets for AI/ML use cases.

Evaluate Statements:

A: True—Open Data includes text and image datasets (e.g., geospatial images).

B: False—Video and other formats may be available depending on the dataset; no strict exclusion exists.

C: False—Datasets may include metadata, but code/tooling examples aren’t guaranteed.

D: True—It’s designed for data scientists and analysts who work with datasets.

E: False—It’s not a user-contributed repository; it’s curated by Oracle.

F: False—Open Data is free and public, not subscription-based.

Select Two: A and D align with the service’s purpose and offerings.

OCI Open Data provides access to datasets like text and images (A) for AI/ML, aimed at data professionals (D). It’s a free, curated service, not user-contributed (E) or paid (F), and while it focuses on certain formats, it doesn’t explicitly exclude audio/video (B). (Reference: Oracle Cloud Infrastructure Open Data Documentation, "Overview of Open Data").

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 the Data Safe feature that reduces personal data exposure.

Understand Data Safe: Secures sensitive data in OCI databases.

Evaluate Options:

A: Encryption—Protects data, doesn’t minimize it.

B: Assessment—Identifies risks, doesn’t alter data.

C: Masking—Obfuscates personal data (e.g., SSNs)—correct.

D: Discovery—Locates sensitive data, doesn’t reduce it.

E: Auditing—Tracks access, doesn’t minimize data.

Reasoning: Masking replaces sensitive data, reducing risk for teams—fits goal.

Conclusion: C is correct.

OCI documentation states: “Data masking (C) in Data Safe transforms sensitive data into anonymized versions, minimizing exposure for test, dev, and analytics use.” A protects, B assesses, D finds, E audits—only C reduces data per OCI’s Data Safe features.

Oracle Cloud Infrastructure Data Safe Documentation, "Data Masking Overview".

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: Spot the invalid OCI IAM policy.

Understand Policies: Use verbs (e.g., use, manage) and resource types.

Evaluate Options:

A: Valid—virtual-network-family is a resource type.

B: Invalid—No data-science-model-sessions resource type; likely meant data-science-models.

C: Valid—data-science-projects is correct.

D: Valid—data-science-family aggregates Data Science resources.

Reasoning: B uses a nonexistent resource type—error in syntax.

Conclusion: B is incorrect.

OCI IAM documentation lists valid resource types: “data-science-models, data-science-projects, data-science-family—but not data-science-model-sessions (B).” A, C, D are correct—B is invalid due to this typo/misunderstanding.

Oracle Cloud Infrastructure IAM Documentation, "Data Science Resource Types".

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: 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".

Detailed Answer in Step-by-Step Solution:

Objective: Calculate optimal bandwidth for model deployment.

Given Data:

Payload size = 1024 KB = 1024 * 8 = 8192 Kb (kilobits).

Requests/sec = 120.

Buffer = 20% = 0.2.

Calculate Base Bandwidth:

Bits/sec = Payload * Requests = 8192 Kb * 120 = 983,040 Kb/s = 983.04 Mbps.

Add Buffer:

Total = Base * (1 + Buffer) = 983.04 * 1.2 = 1179.648 Mbps.

Evaluate Options: Closest to 1179.648 Mbps is 1152 Mbps (D)—realistic rounding.

Conclusion: D is correct.

OCI documentation advises: “Calculate bandwidth as payload size (in bits) * requests/sec, then add a buffer (e.g., 20%) for peak loads.” Here, 1024 KB = 8192 Kb, * 120 = 983.04 Mbps, * 1.2 = 1179.648 Mbps. D (1152 Mbps) is the closest practical option—452 (A) and 52 (B) are too low, 7052 (C) excessive.

Oracle Cloud Infrastructure Data Science Documentation, "Model Deployment - Load Balancing".

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: Identify the OCI service for scalable Spark applications.

Evaluate Options:

A: Data Science—ML platform, not Spark-focused.

B: Anomaly Detection—Specific ML service, not general Spark.

C: Data Labeling—Annotation tool, not Spark-related.

D: Data Flow—Managed Spark service for big data.

Reasoning: Data Flow is OCI’s Spark execution engine.

Conclusion: D is correct.

OCI Data Flow “provides a fully managed environment to run Apache Spark applications at scale, ideal for data processing and ML tasks.” Data Science (A) supports Spark in notebooks, but Data Flow (D) is the dedicated, scalable solution—B and C are unrelated.

Oracle Cloud Infrastructure Data Flow Documentation, "Overview".

Detailed Answer in Step-by-Step Solution:

Objective: Identify which activity isn’t part of the ML lifecycle.

Define ML Lifecycle: Includes data access, preparation, modeling, evaluation, deployment, and monitoring.

Evaluate Options:

A: Database Management (e.g., DBA tasks) is IT-related, not specific to ML workflows.

B: Model Deployment (e.g., serving predictions) is a key ML phase—correctly included.

C: Modeling (e.g., training) is the core of ML—correctly included.

D: Data Access (e.g., retrieving data) is the first ML step—correctly included.

Reasoning: Database management supports infrastructure, not the ML process directly.

Conclusion: A is the outlier.

The OCI Data Science lifecycle includes “data access, exploration, feature engineering, modeling, deployment, and monitoring,” per the documentation. Database Management (A) is a general ITtask (e.g., optimizing Oracle DB), not an ML-specific activity, unlike B, C, and D, which are integral to OCI’s ML pipeline.

Oracle Cloud Infrastructure Data Science Documentation, "Machine Learning Lifecycle Overview".

Detailed Answer in Step-by-Step Solution:

Objective: Identify a step unique to MLOps vs. DevOps.

Compare MLOps and DevOps:

DevOps: Focuses on software deployment (CI/CD).

MLOps: Extends DevOps to ML, adding model-specific steps.

Evaluate Options:

A: Continuous deployment—Common to both (software/models).

B: Continuous integration—Common to both (code merging).

C: Continuous delivery—Common to both (releasing updates).

D: Continuous training—Unique to MLOps (retraining models with new data).

Reasoning: Only D addresses ML-specific needs (model retraining).

Conclusion: D is correct.

OCI documentation notes: “MLOps extends DevOps with continuous training, a process unique to machine learning where models are retrained with new data to maintain performance.” CI (B), CD (A), and delivery (C) are shared with DevOps—only continuous training (D) is MLOps-specific.

Oracle Cloud Infrastructure Data Science Documentation, "MLOps Concepts".

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: Choose an interface for a Python script to manage projects.

Evaluate Options:

A: OCI SDK—Python-based, scriptable—correct.

B: Console—GUI, not scriptable.

C: CLI—Command-based, not Python-native.

D: Mobile App—Not for scripting.

Reasoning: A enables programmatic project creation.

Conclusion: A is correct.

OCI documentation states: “Use the OCI Python SDK (A) to programmatically manage Data Science resources, like creating projects, via Python scripts.” B, C, and D don’t support Python scripting—only A fits.

Oracle Cloud Infrastructure SDK Documentation, "Data Science API".

Detailed Answer in Step-by-Step Solution:

Objective: Identify the initial data science step.

Define Process: Starts with problem definition, then data and modeling.

Evaluate Options:

A: Data collection—Second step after problem definition.

B: Modeling—Later stage.

C: Hypothesis—Sets the goal, first step—correct.

D: Data owners—Collaboration, not the start.

Reasoning: Hypothesis drives the process (e.g., “Can we predict churn?”).

Conclusion: C is correct.

OCI documentation states: “The data science process begins with defining an analytical hypothesis to address a business problem, followed by data collection and analysis.” C precedes A, B, and D—aligning with OCI’s structured approach.

Oracle Cloud Infrastructure Data Science Documentation, "Data Science Process".

Detailed Answer in Step-by-Step Solution:

Objective: Determine truth about WAF origin management.

Understand WAF: Protects apps by routing traffic via origins.

Evaluate Statements:

A: Multiple origins—True; WAF supports this.

B: Single active origin—True; only one is active per policy.

Evaluate Options:

C: B only—False; A is true.

D: Both false—Incorrect.

E: Both true—Correct per OCI WAF.

F: A only—False; B is true.

Conclusion: E is correct.

OCI documentation states: “WAF allows defining multiple origins (A), but only one origin is active per WAF policy at a time (B)—both are true (E).” C, D, and F misalign—E matches OCI’s WAF origin management.

Oracle Cloud Infrastructure WAF Documentation, "Origin Management".

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 Apache Spark’s ML library.

Understand Spark: A big data framework with specialized libraries.

Evaluate Options:

A: MLib (correctly MLlib)—Spark’s machine learning library.

B: GraphX—Graph processing, not ML.

C: Structured Streaming—Streaming data, not ML.

D: HadoopML—Not a Spark library (Hadoop-related).

Reasoning: MLlib is Spark’s official ML toolkit (e.g., regression, clustering).

Conclusion: A is correct (noting “MLib” should be “MLlib”).

OCI Data Science supports Spark via Data Flow, where “MLlib (Machine Learning library) provides scalable ML algorithms.” GraphX (B) and Structured Streaming (C) serve other purposes, and HadoopML (D) isn’t real—MLlib (A) is the standard, despite the typo.

Oracle Cloud Infrastructure Data Flow Documentation, "Apache Spark MLlib".

Detailed Answer in Step-by-Step Solution:

Objective: Identify encryption standard for OCI Data Science.

Understand OCI Encryption: Applies to data at rest and in transit.

Evaluate Options:

A: AES-256—Industry-standard, OCI default—correct.

B: DES—Outdated, weak—incorrect.

C: TDES—Older, less secure—incorrect.

D: Twofish—Not OCI standard—incorrect.

E: RSA—Asymmetric, not primary for data at rest—incorrect.

Reasoning: AES-256 is OCI’s go-to for Data Science resources.

Conclusion: A is correct.

OCI documentation states: “Data Science services encrypt data at rest using AES-256 (A), ensuring high security for notebooks, jobs, and models.” B, C, D, and E are either outdated or not used—only A matches OCI’s encryption policy.

Oracle Cloud Infrastructure Data Science Documentation, "Data Encryption".