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

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:

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: 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 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: Identify three annotation methods in OCI Data Labeling for images.

Understand Data Labeling: Supports image annotations for ML.

Evaluate Options:

A: Semantic segmentation with boxes—Incorrect; segmentation is pixel-based, not boxes.

B: Single label (classification)—Supported—correct.

C: No bounding boxes—False; boxes are supported.

D: Object detection with boxes—Supported—correct.

E: Multiple labels (multi-label)—Supported—correct.

Reasoning: B (classification), D (detection), E (multi-label) match OCI capabilities.

Conclusion: B, D, E are correct.

OCI documentation states: “Data Labeling supports image annotations via single-label classification (B), object detection with bounding boxes (D), and multi-label classification (E).” A misdefines segmentation, C contradicts support—only B, D, E are valid per OCI’s Data Labeling features.

Oracle Cloud Infrastructure Data Labeling Documentation, "Image Annotation Types".

Detailed Answer in Step-by-Step Solution:

Objective: Select an auth method for third-party APIs lacking OCI signature support.

Understand OCI Auth: Typically uses API keys, but alternatives exist for non-standard APIs.

Evaluate Options:

A: Username/password—Not API-friendly, insecure.

B: API Signing Key—Requires signature-based auth, unsupported here.

C: SSH Key—For instance access, not APIs.

D: Auth Token—Simple token for API calls—correct.

Reasoning: Auth Token provides a bearer token for APIs without signature complexity.

Conclusion: D is correct.

OCI documentation states: “For third-party APIs not supporting signature-based authentication, use an Auth Token (D), a secure, revocable token for accessing OCI resources via REST APIs.” A, B, and C don’t fit non-signature scenarios—only D ensures compatibility per OCI’s IAM options.

Oracle Cloud Infrastructure IAM Documentation, "Auth Tokens for API Access".

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: Sequence steps for a PySpark app in OCI Data Science.

Evaluate Steps:

Launch notebook: First—provides the environment.

Install PySpark conda: Second—sets up Spark libraries.

Configure core-site.xml: Third—connects to data (e.g., Object Storage).

Develop app: Fourth—writes the PySpark code.

Data Flow: Fifth—optional scaling, post-development.

Check Options: D (1, 2, 3, 4, 5) matches this logical flow.

Reasoning: Notebook first, then setup, coding, and scaling.

Conclusion: D is correct.

OCI documentation recommends: “1) Launch a notebook session, 2) install a PySpark conda environment, 3) configure core-site.xml for data access, 4) develop your PySpark application, and 5) optionally use Data Flow for scale.” D follows this—others (A, B, C) misorder critical steps like launching the notebook.

Oracle Cloud Infrastructure Data Science Documentation, "PySpark in Notebooks".

Detailed Answer in Step-by-Step Solution:

Understand Published Conda Environments: In OCI Data Science, these are custom conda environments shared across users via Object Storage.

Evaluate Options:

A: Vague—All conda environments can address use cases; not unique to “published.”

B: Incorrect—Availability on reactivation applies to session persistence, not publishing.

C: Correct—Publishing saves the environment to Object Storage for sharing/reuse.

D: Incorrect—Block volumes store session data, not published environments.

Reasoning: The unique aspect of “published” environments is their storage in Object Storage (via odsc conda publish), enabling team access.

Conclusion: C is the distinctive feature.

The OCI Data Science documentation highlights that “published conda environments are saved to an OCI Object Storage Bucket, allowing them to be shared across notebook sessions and users.” This distinguishes C from A (generic), B (session-related), and D (block volume is for session state, not publishing). Publishing to Object Storage is the defining trait per Oracle’s design.

Oracle Cloud Infrastructure Data Science Documentation, "Managing Conda Environments - Publishing" section.

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 an open model format for cross-platform ML model execution.

Evaluate Options:

A. PySpark: A big data framework, not a model format.

B. PyTorch: An ML framework with its own format, not inherently cross-platform without conversion.

C. TensorFlow: An ML framework with its SavedModel format, not universally open across platforms.

D. ONNX: Open Neural Network Exchange, an open-source format for model interoperability across frameworks.

Reasoning: ONNX is designed for portability (e.g., convert PyTorch to ONNX, run in TensorFlow), unlike framework-specific options.

Conclusion: D is the correct choice.

ONNX (D) is “an open-source model format that enables interoperability between ML frameworks like PyTorch and TensorFlow,” per OCI documentation. PySpark (A) is a processing tool, while PyTorch (B) and TensorFlow (C) are frameworks with native formats—only ONNX ensures cross-platform compatibility.

Oracle Cloud Infrastructure Data Science Documentation, "Supported Model Formats".

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: Determine where to specify the conda environment for an OCI model deployment.

Understand Model Deployment: Requires artifacts like score.py and runtime.yaml to define runtime settings.

Evaluate Options:

A. score.py: Contains inference logic (e.g., load_model(), predict())—not for environment specs.

B. runtime.yaml: Defines deployment runtime, including conda environment path—correct.

C. requirements.txt: Lists pip dependencies—not used in OCI for conda environments.

D. model_artifact_validate.py: Not a standard artifact; doesn’t exist in OCI deployment.

Reasoning: runtime.yaml specifies the conda env (e.g., slug: pyspark30_p37_cpu_v2)—failure to set this causes deployment errors.

Conclusion: B is correct.

OCI documentation states: “The runtime.yaml file in a model artifact specifies the runtime environment, including the conda environment path (e.g., ENVIRONMENT_SLUG: pyspark30_p37_cpu_v2), ensuring the deployed model uses the correct dependencies.” score.py (A) handles inference, requirements.txt (C) is for pip (not conda in OCI), and D isn’t valid—only B addresses the conda issue per OCI’s deployment process.

Oracle Cloud Infrastructure Data Science Documentation, "Model Deployment - runtime.yaml".

Detailed Answer in Step-by-Step Solution:

Objective: Find the stage where data imbalance (e.g., skewed classes) is identified.

Understand Stages:

Data Modeling: Training models—assumes data is prepared.

Data Monitoring: Post-deployment tracking—not for initial analysis.

Data Exploration: Analyzing data properties (e.g., distributions)—key for imbalance.

Data Access: Retrieving data—no analysis yet.

Evaluate Options:

A: Modeling uses data, doesn’t detect imbalance—incorrect.

B: Monitoring tracks performance, not initial data issues—incorrect.

C: Exploration (e.g., via pandas) reveals imbalances—correct.

D: Access is just retrieval—incorrect.

Reasoning: Imbalance is assessed during exploration (e.g., class counts).

Conclusion: C is correct.

OCI documentation notes: “Data Exploration involves analyzing the dataset to understand its characteristics, such as identifying class imbalances or missing values, using tools like ADS SDK or Jupyter notebooks.” Modeling (A) and Monitoring (B) occur later, while Access (D) is pre-analysis—only Exploration (C) fits this role.

Oracle Cloud Infrastructure Data Science Documentation, "Data Exploration Stage".

Detailed Answer in Step-by-Step Solution:

Objective: Understand FAP’s effect in OCI Anomaly Detection.

Evaluate Options:

A: Disable reporting—Incorrect; FAP sets threshold.

B: Sensitivity—Correct; lower FAP reduces false positives.

C: Error message—Incorrect; not a count mechanism.

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

Reasoning: FAP adjusts detection threshold—key to sensitivity.

Conclusion: B is correct.

OCI documentation states: “False Alarm Probability (FAP) controls the model’s sensitivity—lower values reduce false positives, higher values increase detection.” B aligns—others misrepresent FAP’s role.

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

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: 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 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:

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 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: Identify the required configuration for OCI Logging Analytics to collect logs from an instance.

Understand Logging Analytics: Collects and analyzes logs from OCI resources via Management Agents.

Key Concepts:

Entity: Represents the instance (e.g., Linux VM).

Source: Defines log locations (e.g., file paths).

Log Group: Organizes logs for analysis.

Evaluate Options:

A: Log-Log Group—Groups logs, not collection setup.

B: Entity-Log—Links instance to logs, but not source-specific.

C: Source-Entity—Maps log sources to the instance—correct.

D: Log Group-Source—Post-collection organization, not ingestion.

Reasoning: C establishes the link between the instance and its log sources—key for ingestion.

Conclusion: C is correct.

OCI documentation states: “To collect logs using Logging Analytics, configure a Source-Entity Association (C) to link the Management Agent on the instance (entity) to specific log sources (e.g., file paths).” A and D organize logs post-collection, B is less specific—only C is required for ingestion per OCI’s Logging Analytics setup.

Oracle Cloud Infrastructure Logging Analytics Documentation, "Configuring Log Collection".

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:

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: 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: 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:

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 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:

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

Evaluate Metrics:

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

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

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

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

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

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

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

Detailed Answer in Step-by-Step Solution:

Objective: Identify a true statement about standards in an OCI context (likely governance/security).

Understand Standards: Rules or benchmarks, often compliance-related.

Evaluate Options:

A: Auditable—True; standards are checked for adherence.

B: Result of requirements—Partially true, but not always.

C: Methods/instructions—More procedural, not defining standards.

D: Foundation of governance—Broad, not specific to standards.

Reasoning: A is universally true—standards face audits (e.g., SOC, ISO).

Conclusion: A is correct.

OCI documentation notes: “Standards (e.g., security standards) may be audited (A) to ensure compliance with OCI policies or external regulations.” B is a source, C describes procedures, D is too vague—only A is consistently true per OCI’s compliance framework.

Oracle Cloud Infrastructure Security Documentation, "Compliance and Standards".

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:

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: 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:

Define Resource Principals: They allow OCI resources (e.g., notebook sessions) to authenticate to other OCI services without user credentials.

Evaluate Options:

A: False—Resource principals eliminate manual credential management.

B: False—They’re secure, leveraging IAM policies, not less secure than API keys.

C: False—Data Science supports resource principals for accessing resources (e.g., Object Storage).

D: True—Resource principals are an IAM feature authorizing resources as actors.

Reasoning: D captures the essence of resource principals as an IAM mechanism.

Conclusion: D is correct.

OCI documentation states: “A resource principal is an IAM feature that enables OCI resources, such as compute instances or notebook sessions, to act as principal actors and authenticate to other OCI services using policies.” This refutes A (no credentials needed), B (secure method), and C (supported in Data Science), making D the accurate statement.

Oracle Cloud Infrastructure IAM Documentation, "Resource Principals".

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: Efficiently vary hyperparameters in OCI Jobs.

Evaluate Options:

A: New job per run—Wastes setup time.

B: Code changes per job—Inefficient, error-prone.

C: Flexible params per run—Efficient, reusable—correct.

D: New job per run—Redundant effort.

Reasoning: C minimizes job creation, maximizes flexibility.

Conclusion: C is correct.

OCI documentation states: “For varying hyperparameters, configure a single Job with code accepting environment variables or command-line arguments (C), set per run—most efficient.” A and D over-create jobs, B ties params to code—only C optimizes.

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

Detailed Answer in Step-by-Step Solution:

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

Evaluate Options:

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

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

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

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

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

Conclusion: D is correct.

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

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

Detailed Answer in Step-by-Step Solution:

Objective: 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: 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 ADSTuner strategy to minimize cost and assess model quality.

Understand ADSTuner: Optimizes hyperparameters with configurable search spaces.

Evaluate Options:

A: Detailed—Exhaustive, high cost—incorrect.

B: No search space—False; tuning requires a space.

C: Perfunctory—Quick, low-cost assessment—correct.

D: Dictionary—Defines space but not a strategy.

Reasoning: Perfunctory balances cost and initial quality check.

Conclusion: C is correct.

OCI documentation states: “ADSTuner’s perfunctory strategy (C) performs a quick, low-cost search to assess model quality, ideal for initial tuning.” Detailed (A) is costly, B misstates requirements, and D is a method, not a strategy—only C fits the goal.

Oracle Cloud Infrastructure ADS SDK Documentation, "ADSTuner Search Strategies".

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:

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

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

Evaluate Options:

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

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

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

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

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

Conclusion: D is the best approach for detailed investigation.

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

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

Detailed Answer in Step-by-Step Solution:

Objective: 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: 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".