1z0-1110-25 Practice Exam Questions with Answers Oracle Cloud Infrastructure 2025 Data Science Professional Certification

Detailed Answer in Step-by-Step Solution:

Objective: Select an OCI AI service to sort images by language.

Evaluate Options:

A: Digital Assistant—Chatbots, not image/language processing.

B: Vision—Image analysis (e.g., object detection), not language sorting.

C: Speech—Audio-to-text, not image-based.

D: Language—Text analysis (e.g., language detection) after OCR—correct.

Reasoning: Images need OCR (Vision) then language detection (Language)—D fits the sorting task.

Conclusion: D is correct.

OCI Language “detects and classifies languages in text,” often paired with OCI Vision’s OCR to process document images. Vision (B) extracts text, but Language (D) sorts by language—Digital Assistant (A) and Speech (C) don’t apply. Documentation supports this workflow.

Oracle Cloud Infrastructure Language Documentation, "Language Detection".

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: Find a true statement about OCI Anomaly Detection.

Understand Service: Detects anomalies in multivariate data (e.g., time series).

Evaluate Options:

A: False—Accepted types are CSV/JSON, not SQL/Python.

B: Partially true—Focuses on numerical data (e.g., sensors), not text broadly.

C: True—Used for fraud, intrusions, and sensor anomalies (key use cases).

D: False—Trained on customer data only, not general datasets.

Reasoning: C aligns with documented applications; others misalign.

Conclusion: C is correct.

OCI Anomaly Detection documentation states: “The service is designed to detect anomalies in time series data, making it valuable for fraud detection, network intrusion analysis, and sensor discrepancies.” A is incorrect (file formats), B overgeneralizes (numerical focus), and D misstates training data—only C matches the service’s purpose.

Oracle Cloud Infrastructure Anomaly Detection Documentation, "Use Cases".

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: Securely authenticate a recurring OCI Job.

Evaluate Options:

A: PAR—Limited duration, insecure for recurring jobs.

B: Resource principal—Secure, managed auth for Jobs—correct.

C: Personal config—Unscalable, security risk.

D: Vault with personal keys—Complex, still uses user creds.

Reasoning: B uses OCI’s native, secure resource principal mechanism.

Conclusion: B is correct.

OCI documentation states: “For Jobs accessing sensitive data, use resource principals with a dynamic group (e.g., resource.type = 'datasciencejobrun') and policies granting access to Object Storage and Model Catalog—secure and scalable.” A is temporary, C and D risk credential exposure—B is best practice.

Oracle Cloud Infrastructure Data Science Documentation, "Job Authentication".

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:

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 two reasons for key rotation in OCI Vault.

Understand Key Rotation: Enhances security by updating keys.

Evaluate Options:

A: Five-key limit—False, no such restriction.

B: Efficiency—False, not the purpose.

C: Reuse—False, rotation prevents reuse.

D: Reduces risk—True, limits exposure—correct.

E: Limits data—True, reduces breach scope—correct.

Reasoning: D and E are security-focused—key Vault benefits.

Conclusion: D and E are correct.

OCI documentation states: “Key rotation in Vault (D) reduces risk if a key is compromised and (E) limits the data encrypted by a single key version, enhancing security.” A, B, and C misrepresent rotation’s purpose—only D and E align with OCI’s Vault best practices.

Oracle Cloud Infrastructure Vault Documentation, "Key Rotation Benefits".

Detailed Answer in Step-by-Step Solution:

Objective: Identify a true statement about OCI Jobs logging.

Understand Logging: Jobs can log stdout/stderr to OCI Logging service.

Evaluate Options:

A: False—Each run has its own log, not a single job log.

B: False—Logging is optional, not mandatory.

C: True—When enabled, stdout/stderr are auto-captured.

D: False—Logs persist unless explicitly deleted.

Reasoning: C matches OCI’s automatic logging feature.

Conclusion: C is correct.

OCI documentation states: “When automatic log creation is enabled for Data Science Jobs, all stdout and stderr outputs are captured and stored in the OCI Logging service.” A is incorrect (per-run logs), B is optional, and D contradicts log retention—only C is accurate.

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

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 top languages for data science.

Evaluate Options:

A: C/C++—Low-level, less common for data tasks.

B: Python (ML, libraries), R (stats), SQL (data)—Industry standards.

C: Java (enterprise), JavaScript (web)—Not data-focused.

Reasoning: B aligns with data science tools (e.g., pandas, ggplot).

Conclusion: B is correct.

OCI documentation highlights “Python, R, and SQL as the most widely used languages in Data Science for modeling, analysis, and data querying.” C/C++ (A) and Java/JS (C) are less prevalent—B matches OCI’s notebook support and industry trends.

Oracle Cloud Infrastructure Data Science Documentation, "Supported Languages".

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 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:

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: Address declining prediction accuracy and verify model performance.

Analyze Problem: Degradation over time suggests data drift or model staleness—common ML issues.

Evaluate Options:

A. Retrain the model: Uses new data to update the model—fixes accuracy—correct.

B. Validate with recent data: Tests performance but doesn’t fix—diagnostic only.

C. Drift monitoring: Detects data distribution shifts—verifies cause—correct.

D. Redeploy the model: Repeats deployment, doesn’t address root cause.

E. Operational monitoring: Tracks infra (e.g., latency), not prediction accuracy.

Reasoning: C identifies drift (why accuracy dropped), A corrects it—best pair for verification and improvement.

Conclusion: A and C are correct.

OCI documentation states: “Drift monitoring (C) detects changes in data distribution that impact accuracy, while retraining (A) with new data restores model performance.” Validation (B) checks but doesn’t fix, redeployment (D) is redundant, and operational monitoring (E) is infra-focused—only A and C 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: Publish a conda env to Object Storage.

Process: Initialize bucket config, then publish env.

Evaluate Options:

A: Publishes env with slug—correct final step.

B: Lists envs—unrelated to publishing.

C: Sets bucket details—required setup—correct.

D: Creates env—not publishing.

E: Activates env—not for sharing.

Reasoning: C sets up, A executes—standard workflow.

Conclusion: A and C are correct.

OCI documentation states: “To publish a conda environment, first run odsc conda init (C) with bucket namespace and name, then odsc conda publish (A) with a slug to upload to Object Storage.” B, D, and E serve other purposes—only A and C are required per OCI’s process.

Oracle Cloud Infrastructure Data Science CLI Reference, "Publishing Conda Environments".

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:

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: Assess if bias is a common issue in data science.

Define Bias: Systematic errors in data/models (e.g., skewed training data).

Evaluate Statement:

Bias arises from unrepresentative data, poor feature selection, or algorithmic flaws—widely recognized in ML.

Examples: Gender bias in hiring models, racial bias in facial recognition.

Reasoning: Literature and practice (e.g., fairness in AI) confirm bias as prevalent.

Conclusion: A (True) is correct.

OCI documentation notes: “Bias is a common challenge in data science, stemming from imbalanced datasets or flawed assumptions, requiring techniques like re-weighting or fairness checks.” This aligns with industry standards—bias is a well-documented issue, making A true.

Oracle Cloud Infrastructure Data Science Documentation, "Addressing Bias in Models".

Detailed Answer in Step-by-Step Solution:

Objective: Identify custom model types for OCI Language service.

Understand OCI Language: Focuses on text analysis, not images.

Evaluate Options:

A: Image classification—Incorrect; Language is text-based.

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

C: Sentiment—Pretrained, not custom; NER ok—incorrect.

D: Object detection—Image-based, incorrect.

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

Conclusion: B is correct.

OCI documentation states: “OCI Language supports training custom models for text classification and Named Entity Recognition (NER) (B) using user data.” A and D involve images, C includes pretrained sentiment—only B matches OCI Language’s custom model scope.

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

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: 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 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: 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: 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: 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:

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:

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

Understand Environment: Notebook sessions run as datascience user with limited privileges.

Evaluate Options:

A: False—Yum isn’t available; pip is the primary tool.

B: True—Custom repos work with proper network config.

C: False—No root access; managed environment.

D: True—PyPI packages installable with internet (NAT Gateway).

E: False—Youcaninstall beyond preinstalled; likely meant opposite.

Reasoning: B and D are true; E’s intent seems reversed (common exam error)—corrected to B, D.

Conclusion: B, D (assuming E typo).

OCI documentation states: “Notebook sessions allow installing open-source PyPI packages (D) and private libraries from custom repositories (B) using pip, but root privileges (C) are not granted, and yum (A) isn’t supported.” E contradicts capability—corrected, B and D are accurate.

Oracle Cloud Infrastructure Data Science Documentation, "Installing Python Libraries".

Detailed Answer in Step-by-Step Solution:

Objective: Identify three stages in OCI AutoML Pipeline.

Understand Pipeline: Automates ML steps from data to model training.

Evaluate Options:

A: Feature Selection—Selects relevant features—correct.

B: Adaptive Sampling—Reduces data size—correct.

C: Model Deployment—Post-pipeline step—incorrect.

D: Feature Extraction—Not explicit in OCI AutoML—incorrect.

E: Algorithm Selection—Chooses best model—correct.

Reasoning: A, B, E are core automated stages; C and D are separate.

Conclusion: A, B, E are correct.

OCI documentation lists “AutoML Pipeline stages as adaptive sampling (B), feature selection (A), algorithm selection (E), and hyperparameter tuning.” Deployment (C) is post-pipeline, and extraction (D) isn’t highlighted—only A, B, E are included per OCI’s design.

Oracle Cloud Infrastructure AutoML Documentation, "Pipeline Components".

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: Locate the lifecycle stage for transforming timestamps into categories.

Understand Lifecycle:

Model Type Selection: Choosing algorithms—post-data prep.

Model Validation: Evaluating performance—post-training.

Data Access: Retrieving raw data—pre-transformation.

Feature Engineering: Creating new features—correct.

Evaluate Options:

A: Too late—incorrect.

B: Post-model—incorrect.

C: Pre-transformation—incorrect.

D: Feature creation—correct.

Reasoning: Converting timestamps to time periods is feature engineering.

Conclusion: D is correct.

OCI documentation states: “Feature engineering (D) transforms raw data, such as timestamps into categorical variables (e.g., morning/afternoon), to enhance model inputs.” A, B, and C occur at different stages—only D fits OCI’s lifecycle for this task.

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

Detailed Answer in Step-by-Step Solution:

Objective: Understand the effect of False Alarm Probability (FAP) in OCI Anomaly Detection.

Understand FAP: Controls false positive rate—threshold for anomaly flagging.

Evaluate Options:

A: Disable reporting—Incorrect; FAP sets sensitivity, not on/off.

B: Changes sensitivity—Correct; lower FAP = fewer false positives—correct.

C: Count-based error—Incorrect; not a counter.

D: Score per signal—Incorrect; FAP is a global setting.

Reasoning: FAP adjusts detection threshold—direct impact on sensitivity.

Conclusion: B is correct.

OCI documentation states: “False Alarm Probability (FAP) (B) adjusts the model’s sensitivity in Anomaly Detection—lower values increase specificity, reducing false positives.” A, C, and D misinterpret FAP’s role—only B aligns with OCI’s anomaly detection tuning.

Oracle Cloud Infrastructure Anomaly Detection Documentation, "FAP Settings".

Detailed Answer in Step-by-Step Solution:

Understand ORM Template: It automates OCI Data Science setup with predefined configurations.

Evaluate Components:

A: User groups are created for role-based access—automated.

B: Dynamic groups (e.g., for notebook sessions) are included—automated.

C: Individual users require manual creation via IAM—not automated.

D: Basic policies (e.g., access to Data Science resources) are included—automated.

Reasoning: ORM focuses on infrastructure and permissions, not user accounts.

Conclusion: C is the exception.

The OCI Resource Manager template for Data Science “automatically provisions user groups, dynamic groups, and policies for basic use cases,” but “individual users must be created separately in IAM and assigned to groups.” C is the only item not handled by the template, per the documentation.

Oracle Cloud Infrastructure Resource Manager Documentation, "Data Science Template".

Detailed Answer in Step-by-Step Solution:

Objective: Scale up a notebook session without losing work.

Understand Persistence: Block volume stores session data (e.g., /home/datascience).

Evaluate Options:

A: Recreating work—inefficient, risks loss.

B: Local download/upload—cumbersome, unnecessary.

C: Use block volume persistence, scale up—efficient, preserves work—correct.

D: Object Storage—extra steps, not needed with block volume.

Reasoning: C leverages OCI’s built-in persistence for seamless scaling.

Conclusion: C is correct.

OCI documentation states: “Files in /home/datascience are stored on the block volume. To scale up, deactivate the session, provision a new one with a larger shape, and the block volume persists your work.” A loses data, B and D add complexity—only C is optimal.

Oracle Cloud Infrastructure Data Science Documentation, "Scaling Notebook Sessions".

Detailed Answer in Step-by-Step Solution:

Objective: Predict AutoML’s output when time runs out during tuning.

Understand AutoML Tuning: Iteratively tests hyperparameters, tracks best results.

Evaluate Options:

A: Best-known config—Logical, reflects optimization goal—correct.

B: Last config—Ignores prior better results—incorrect.

C: Minimum learning rate—Arbitrary, not performance-based.

D: Random—Defeats tuning purpose.

Reasoning: AutoML prioritizes the best config found within the budget.

Conclusion: A is correct.

OCI AutoML documentation states: “If the time budget expires, AutoML returns the best hyperparameter configuration (A) identified during tuning based on performance metrics.” Last (B), minimum (C), or random (D) configs aren’t selected—only A aligns with OCI’s optimization strategy.

Oracle Cloud Infrastructure AutoML Documentation, "Hyperparameter Tuning - Time Budget".

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: Fix network timeout for pip install in a notebook.

Evaluate Options:

A: FastConnect—On-premises link, not public internet.

B: VNIC—Default, not the issue.

C: NAT Gateway—Grants internet access—correct.

D: Service Gateway—OCI services, not PyPI.

Reasoning: C enables outbound traffic to public repos.

Conclusion: C is correct.

OCI documentation states: “A NAT Gateway (C) is required for notebook sessions in private subnets to access public internet repositories like PyPI.” A, B, and D don’t provide this—only C resolves the timeout.

Oracle Cloud Infrastructure Data Science Documentation, "Notebook Networking".

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: Spot the invalid Security Zone policy.

Understand Security Zones: Enforce strict security—resources can’t leave easily.

Evaluate Options:

A: Boot volume move—Not allowed; violates zone rules—invalid.

B: Compute no-move—True, enforces security.

C: No public access—True, standard policy.

D: Auto-backup—True, required for compliance.

Reasoning: A contradicts Security Zone immutability.

Conclusion: A is incorrect.

OCI documentation states: “Resources like boot volumes (A) cannot be moved from a Security Zone to a standard compartment—B, C, and D are enforced policies.” A violates OCI’s Security Zone restrictions—only A is not valid.

Oracle Cloud Infrastructure Security Zones Documentation, "Policy Restrictions".

Detailed Answer in Step-by-Step Solution:

Objective: Sequence steps for PySpark app development.

Steps:

Launch notebook: First—sets up environment.

Install PySpark conda: Second—adds Spark libraries.

Configure core-site.xml: Third—connects to data.

Develop app: Fourth—writes code.

Data Flow: Fifth—scales (optional).

Evaluate: D (1, 2, 3, 4, 5) matches this logical order.

Reasoning: Notebook first, then setup and coding.

Conclusion: D is correct.

OCI documentation states: “1) Launch a notebook session, 2) install a PySpark conda env, 3) configure core-site.xml, 4) develop your PySpark app, 5) optionally use Data Flow (D).” Other orders (A, B, C) misplace notebook launch or config—D is correct.

Oracle Cloud Infrastructure Data Science Documentation, "PySpark Development".