Oracle 1z0-1110-25 Oracle Cloud Infrastructure 2025 Data Science Professional Exam Practice Test

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

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

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

Evaluate Options:

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

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

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

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

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

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

Conclusion: B and D are correct.

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

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

Detailed Answer in Step-by-Step Solution:

Objective: 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: 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: 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: 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: Convert fixed to flexible load balancer in OCI.

Understand Load Balancers: Fixed (e.g., 10 Mbps) vs. flexible (dynamic shapes).

Evaluate Options:

A: False—Conversion possible via recreation.

B: Update Shape—For flexible only, not conversion.

C: Delete and recreate—Standard method—correct.

D: Edit Listener—Configures rules, not type.

Reasoning: OCI requires new creation for type change.

Conclusion: C is correct.

OCI documentation states: “To change from a fixed to a flexible load balancer, delete the existing fixed load balancer and create a new flexible one (C)—direct conversion isn’t supported.” A is too absolute, B and D don’t apply—only C matches OCI’s process.

Oracle Cloud Infrastructure Load Balancing Documentation, "Changing Load Balancer Type".

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: Define the role of score.py in OCI model artifacts.

Understand Artifacts: score.py is key for deployment runtime.

Evaluate Options:

A: Infra config—Handled by OCI settings, not score.py.

B: Inference logic—Correct; runs load_model(), predict().

C: Scaling—Set in deployment, not score.py.

D: Dependencies—In runtime.yaml, not score.py.

Reasoning: B aligns with score.py’s execution role.

Conclusion: B is correct.

OCI documentation states: “score.py (B) contains the inference logic, including functions to load the model and predict outputs, executed by the deployment endpoint.” A, C, and D are managed elsewhere—only B matches OCI’s design.

Oracle Cloud Infrastructure Data Science Documentation, "Model Artifact - score.py".

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: Define data science’s main goal.

Evaluate Options:

A: Archiving—Not the focus; too narrow.

B: Analyze for insights/business value—Core purpose—correct.

C: Prep for analytics—Means, not the end goal.

D: Output-focused—Vague, incomplete.

Reasoning: B captures the actionable insight generation central to data science.

Conclusion: B is correct.

OCI documentation defines data science as “mining and analyzing large datasets to uncoveractionable insights for operational improvements and business value.” A is storage-focused, C is preparatory, and D is unclear—only B reflects the principal goal per OCI’s mission.

Oracle Cloud Infrastructure Data Science Documentation, "What is Data Science?".

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: 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: Identify two methods to enhance Object Storage durability.

Understand Durability: Ensures data isn’t lost—focus on redundancy and protection.

Evaluate Options:

A: RAID1—Block volume feature, not Object Storage.

B: Encryption—Secures data, not durability.

C: Versioning—Retains old versions, prevents loss—correct.

D: Limit delete—Prevents accidental deletion—correct.

E: Client encryption—Secures, not durability-focused.

Reasoning: C and D directly protect against data loss—durability-focused.

Conclusion: C and D are correct.

OCI documentation states: “Improve Object Storage durability with Versioning (C) to retain previous object versions and by limiting delete permissions (D) to prevent accidental loss.” A isn’t applicable, B and E focus on security—only C and D enhance durability per OCI’s storage features.

Oracle Cloud Infrastructure Object Storage Documentation, "Data Durability Options".

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: Optimize an embarrassingly parallel job in OCI Data Science Jobs.

Define Embarrassingly Parallel: Tasks are independent, ideal for simultaneous runs.

Evaluate Options:

A: Multiple simultaneous runs—Leverages parallelism—correct.

B: One job per run—Misstates capability; unnecessary complexity.

C: Sequential runs—Inefficient, ignores parallelism.

D: False—Jobs support parallelism.

Reasoning: A maximizes efficiency for parallel tasks.

Conclusion: A is correct.

OCI documentation states: “For embarrassingly parallel workloads, create a single Job and launch multiple simultaneous Job Runs to process data in parallel.” B misinterprets limits, C wastes time, and D denies capability—only A fits OCI’s design.

Oracle Cloud Infrastructure Data Science Documentation, "Parallel Job Runs".

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: 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 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 Notebook Sessions: These are OCI compute instances running JupyterLab.

Deactivation Impact: Deactivating stops the session to save costs.

Evaluate Options:

A: False—Costs decrease as compute stops.

B: False—Boot volume data isn’t preserved; block volume data is.

C: True—The compute instance shuts down, halting billing.

D: False—Block volume persists unless explicitly deleted.

Reasoning: Deactivation stops the instance (C), preserving block volume data separately.

Conclusion: C is correct.

The OCI documentation states: “When a notebook session is deactivated, the underlying compute instance stops, and billing for compute resources ceases. Data on the attached block volume is preserved, but the boot volume is not.” A is backwards, B misattributes preservation, and D overstates deletion—only C aligns with the process.

Oracle Cloud Infrastructure Data Science Documentation, "Notebook Session Lifecycle".

Detailed Answer in Step-by-Step Solution:

Objective: Determine the primary benefit of data sampling.

Define Sampling: Selecting a subset of data to represent the whole—used in ML/statistics.

Evaluate Options:

A: Small batches reduce resources—True but not the main purpose.

B: Reduces storage—Incidental, not the goal.

C: Representative subset for faster, accurate models—Core purpose of sampling.

Reasoning: Sampling speeds up analysis while maintaining accuracy (e.g., training on 10% of data).

Conclusion: C is correct.

OCI documentation states: “Data sampling allows data scientists to use a representative subset of a large dataset to build accurate models more quickly, especially when processing full datasets is impractical.” A focuses on resources (secondary), B on storage (not primary)—only C captures the analytical intent per OCI’s AutoML sampling approach.

Oracle Cloud Infrastructure Data Science Documentation, "Data Sampling Techniques".

Detailed Answer in Step-by-Step Solution:

Objective: Efficiently vary hyperparameters in OCI Jobs.

Evaluate Options:

A: New job per run—Wastes setup time.

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

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

D: New job per run—Redundant effort.

Reasoning: C minimizes job creation, maximizes flexibility.

Conclusion: C is correct.

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

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

Detailed Answer in Step-by-Step Solution:

Objective: Optimize embarrassingly parallel workload in OCI Jobs.

Evaluate Options:

A: Sequential runs—Inefficient for parallel tasks.

B: Simultaneous runs—Maximizes parallelism—correct.

C: False—Jobs support parallelism.

D: One job per run—Misstates capability, wasteful.

Reasoning: B leverages OCI’s parallel run support.

Conclusion: B is correct.

OCI documentation states: “For embarrassingly parallel tasks, create one Job and launch multiple simultaneous Job Runs (B) to process data efficiently.” A is slow, C is incorrect, and Dovercomplicates—B is the best approach.

Oracle Cloud Infrastructure Data Science Documentation, "Parallel Job Execution".

Detailed Answer in Step-by-Step Solution:

Objective: Diagnose a network timeout during pip install in a notebook session.

Understand Notebook Networking: Sessions run in a VCN; internet access requires specific configs.

Analyze Timeout: Indicates failure to reach the public PyPI repository—likely no internet outbound route.

Evaluate Options:

A: NAT Gateway—Provides internet access for private subnets—correct fix.

B: Service Gateway—Accesses OCI services privately, not public internet.

C: FastConnect—Links to on-premises, not public internet.

D: VNIC—Essential but present by default; doesn’t solve internet access.

Reasoning: NAT Gateway enables outbound traffic to public repos like PyPI.

Conclusion: A is correct.

OCI documentation notes: “Notebook sessions in a private subnet require a NAT Gateway to access public internet resources, such as PyPI, for package installation via pip. Without it, network timeouts occur.” Service Gateway (B) is for OCI services, FastConnect (C) is irrelevant, and VNIC (D) is standard—only A resolves the issue.

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

Detailed Answer in Step-by-Step Solution:

Objective: Identify ADS class for dataset access (e.g., scikit-learn).

Evaluate Options:

A: DataLabeling—Not an ADS class.

B: DatasetBrowser—Not real.

C: SecretKeeper—Credentials, not data.

D: DatasetFactory—Loads datasets (e.g., open())—correct.

Reasoning: DatasetFactory simplifies library dataset access.

Conclusion: D is correct.

OCI documentation states: “DatasetFactory (D) in ADS SDK accesses datasets from libraries like scikit-learn (e.g., DatasetFactory.open('sklearn.datasets:load_iris')).” A, B, and C don’t exist or apply—only D fits.

Oracle Cloud Infrastructure ADS SDK Documentation, "DatasetFactory".

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 a widely accepted maxim about data scientists’ time allocation.

Understand Data Science Workflow: Involves data collection, preparation, and analysis—time distribution is key.

Evaluate Options:

A: 80% on finding/preparing, 20% analyzing—Reflects the data wrangling challenge.

B: 80% analyzing, 20% finding/preparing—Inverts the common perception.

C: 80% on failed projects, 20% useful—Pessimistic, not a standard maxim.

Reasoning: Industry consensus (e.g., “80/20 rule”) emphasizes data prep as the bulk of effort due to messy real-world data.

Conclusion: A is correct.

OCI Data Science documentation aligns with industry norms: “Data scientists typically spend 80% of their time finding, cleaning, and preparing data, and 20% on analysis and modeling, due to the complexity of raw data.” B reverses this, and C isn’t supported—only A reflects this widely cited maxim from sources like Forbes and OCI’s practical guidance.

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

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: 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:

Define OCI Secrets: Secrets are sensitive data (e.g., API keys, passwords) managed securely in OCI.

Evaluate Options:

A: Object Storage is for general data, not secure secret management.

B: Vault is OCI’s service for storing and managing secrets securely.

C: Autonomous Data Warehouse is for analytics, not secret storage.

D: Oracle Databases store data, not OCI-specific secrets.

Reasoning: Vault is purpose-built for secrets with encryption and access control.

Conclusion: B is correct.

OCI Vault “provides a secure, centralized service to store and manage secrets, such as passwords and keys, with encryption at rest and fine-grained access policies.” Object Storage (A), Autonomous Data Warehouse (C), and Oracle Databases (D) serve other purposes—only Vault (B) is designed for secrets per OCI’s security architecture.

Oracle Cloud Infrastructure Vault Documentation, "Secrets Management".

Detailed Answer in Step-by-Step Solution:

Objective: Understand parallel execution in OCI Pipelines.

Evaluate Options:

A: False—Pipelines support parallelism.

B: True—DAG allows sequential/parallel steps—correct.

C: False—Not all steps must be parallel.

D: False—Independence enables parallelism.

Reasoning: B reflects OCI’s DAG-based flexibility.

Conclusion: B is correct.

OCI documentation states: “Pipeline steps can run sequentially or in parallel, defined by a directedacyclic graph (DAG) (B), allowing optimization of completion time.” A, C, and D misrepresent this—only B aligns with OCI’s pipeline design.

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

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: 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: Get summary info from an ADSDataset object.

Evaluate Options:

A: Correlation matrix—Specific, not full summary.

B: Converts to XGBoost—Not for summary.

C: Executes computation—Not summary-focused.

D: Displays summary (types, counts, dist)—correct.

Reasoning: show_in_notebook() provides a comprehensive overview.

Conclusion: D is correct.

OCI documentation states: “show_in_notebook() (D) from ADSDataset displays a summary of the dataset, including feature types, observation count, and distributions, in a notebook.” A is partial, B and C are unrelated—only D meets the need per ADS SDK.

Oracle Cloud Infrastructure ADS SDK Documentation, "ADSDataset Methods".

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 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: Optimize compute shape for cost and time.

Evaluate Options:

A: Tuning params—Focuses on model, not shape.

B: Strongest shape—Costly, unbalanced.

C: Scale up when utilized—Balances cost/time—correct.

D: Random start—Unsystematic.

Reasoning: C iteratively optimizes based on utilization.

Conclusion: C is correct.

OCI documentation advises: “Start with a small shape, monitor utilization and time (C); scale up if fully utilized until performance stabilizes—optimizes cost and speed.” A misfocuses, B overspends, D lacks method—only C aligns.

Oracle Cloud Infrastructure Data Science Documentation, "Compute Shape Optimization".

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: 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:

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 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: Locate score.py output in OCI model deployment.

Understand Deployment: Logs are centralized in OCI Logging.

Evaluate Options:

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

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

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

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

Reasoning: B aligns with OCI’s logging integration.

Conclusion: B is correct.

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

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

Detailed Answer in Step-by-Step Solution:

Objective: Identify the interactive coding environment in OCI Data Science.

Evaluate Options:

A: Model Catalog stores models—not for coding.

B: Jobs run predefined tasks—not interactive.

C: Notebook Sessions provide JupyterLab for coding and training—interactive.

D: Projects organize work—not a coding environment.

Reasoning: Notebook Sessions are OCI’s Jupyter-based tool for interactive ML development.

Conclusion: C is correct.

OCI Data Science Notebook Sessions “provide an interactive JupyterLab environment where data scientists can write code, explore data, and train machine learning models.” Model Catalog (A) is for storage, Jobs (B) for automation, and Projects (D) for organization—only C offers interactivity.

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

Detailed Answer in Step-by-Step Solution:

Objective: Identify the non-visualized AutoML stage with small data.

Understand AutoML Pipeline: Includes sampling, feature/algorithm selection, tuning.

Evaluate Options:

A: Feature selection—Visualized (e.g., feature importance).

B: Algorithm selection—Visualized (e.g., algorithm scores).

C: Adaptive sampling—Skipped/visualization absent for <1000 rows.

D: Hyperparameter tuning—Visualized (e.g., trial plots).

Reasoning: Adaptive sampling optimizes large datasets; small data skips it, omitting visuals.

Conclusion: C is correct.

OCI AutoML documentation notes: “Adaptive sampling is applied to large datasets (>1000 rows) to reduce size; for smaller datasets, it’s skipped, and no visualization is generated.” Other stages (A, B,D) produce visuals—only C is absent here.

Oracle Cloud Infrastructure AutoML Documentation, "Pipeline Stages".