Free Oracle Cloud Infrastructure 2025 Data Science Professional 1Z0-1110-25 Exam Questions

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: 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:

Objective: Identify custom model types for OCI Language.

Understand OCI Language: Focuses on text analysis.

Evaluate Options:

A: Image classification---Not text-based, incorrect.

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

C: Sentiment, NER---Sentiment is pretrained, not custom.

D: Object detection---Image-based, incorrect.

Reasoning: B aligns with OCI Language's text custom models.

Conclusion: B is correct.

OCI Language documentation states: ''Custom models can be trained for text classification and Named Entity Recognition (NER) using your data.'' Image tasks (A, D) are for Vision, and sentiment (C) is pretrained---only B fits OCI Language's scope.

: Oracle Cloud Infrastructure Language Documentation, 'Custom Model Training'.

Detailed Answer in Step-by-Step Solution:

Objective: Determine collaboration method in OCI Data Science (Notebook Sessions).

Evaluate Options:

A: Access control---Possible but not primary collaboration.

B: Version control (e.g., Git)---Standard for code sharing---correct.

C: Shared instance---Not supported; sessions are single-user.

D: Chat/video---Not a feature of OCI Data Science.

Reasoning: B leverages Git for team collaboration---OCI's recommended method.

Conclusion: B is correct.

OCI documentation states: ''Collaborate in Data Science by integrating version control systems like Git (B) with notebook sessions to share code and notebooks.'' A is limited, C isn't feasible, and D isn't available---only B matches OCI's collaboration approach.

: Oracle Cloud Infrastructure Data Science Documentation, 'Collaboration with Git'.

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: Locate sources for ADS SDK in OCI.

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

Evaluate Options:

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

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

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

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

E: ADW---Database, not an SDK source.

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

Conclusion: C and D are correct.

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

: Oracle Cloud Infrastructure Data Science Documentation, 'ADS SDK Installation'.

Detailed Answer in Step-by-Step Solution:

Objective: Find a true statement about ML models.

Evaluate Options:

A: True---Data drift (changes in data distribution) degrades performance over time.

B: False---Static predictions don't improve without retraining.

C: False---Models need updates as data changes, unlike static software.

D: False---Even high-quality models require retraining with new data.

Reasoning: A reflects the reality of data drift, a common ML challenge.

Conclusion: A is correct.

OCI documentation notes: ''Model performance can degrade over time due to data drift, where the underlying data distribution changes, necessitating monitoring and retraining.'' B, C, and D contradict this---static predictions don't improve (B), models aren't static (C), and retraining is needed (D). A is the accurate aspect.

: Oracle Cloud Infrastructure Data Science Documentation, 'Model Monitoring and Drift'.

Detailed Answer in Step-by-Step Solution:

Objective: Minimize false alarms in OCI Anomaly Detection.

Understand FAP: False Alarm Probability---lower FAP means fewer false positives.

Evaluate Options:

A: High FAP---Increases false alarms---incorrect.

B: Low FAP---Reduces false alarms---correct.

C: Zero FAP---Unrealistic; risks missing true anomalies.

D: Function---Vague, not a direct setting.

Reasoning: Low FAP balances sensitivity and false positives--- aligns with goal.

Conclusion: B is correct.

OCI Anomaly Detection documentation states: ''Set a low False Alarm Probability (FAP) to minimize false positives, though too low (e.g., zero) may miss anomalies.'' B fits the goal---high (A) increases errors, zero (C) is impractical, and function (D) isn't specified.

: Oracle Cloud Infrastructure Anomaly Detection Documentation, 'Configuring FAP'.

Detailed Answer in Step-by-Step Solution:

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

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

Evaluate Options:

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

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

C: Compartment---Required for resource scope---correct.

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

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

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

Conclusion: C, D, E are correct.

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

: Oracle Cloud Infrastructure Data Flow Documentation, 'Creating Applications'.

Detailed Answer in Step-by-Step Solution:

Prepare Model: Build and train the time series model using historical data.

Verify: Validate the model's accuracy (e.g., using metrics like MAE or RMSE).

Save: Store the trained model (e.g., in the OCI Model Catalog).

Deploy: Make the model available for predictions (e.g., via OCI Model Deployment).

Predict: Generate revenue forecasts for the upcoming quarter.

Evaluate Options: D follows this logical flow; others (e.g., A starts with ''verify'' before preparation) don't.

In OCI Data Science, the workflow for time series forecasting involves preparing the model (training), verifying its performance, saving it to the catalog, deploying it, and then predicting. This sequence is standard for ML deployment in OCI, as per the documentation. (Reference: Oracle Cloud Infrastructure Data Science Documentation, 'Time Series Forecasting Workflow').

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: Determine truth about WAF origin management.

Understand WAF: Protects apps by routing traffic via origins.

Evaluate Statements:

A: Multiple origins---True; WAF supports this.

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

Evaluate Options:

C: B only---False; A is true.

D: Both false---Incorrect.

E: Both true---Correct per OCI WAF.

F: A only---False; B is true.

Conclusion: E is correct.

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

: Oracle Cloud Infrastructure WAF Documentation, 'Origin Management'.

Detailed Answer in Step-by-Step Solution:

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: Efficiently increase storage for an OCI Job.

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

Evaluate Options:

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

B: False---No environment variable adjusts storage size.

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

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

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

Conclusion: C is correct.

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

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

Detailed Answer in Step-by-Step Solution:

Objective: Determine where OCI budgets are set.

Understand Budgets: Track spending across OCI resources.

Evaluate Options:

A: Compartments---Scoped within tenancy, not budget root.

B: Instances---Specific resources, not budget scope.

C: Tags---Filter costs, not budget setting.

D: Tenancy---Top-level scope for budgets---correct.

Reasoning: Budgets apply at tenancy, optionally filtered (e.g., by compartment).

Conclusion: D is correct.

OCI documentation states: ''Budgets are set at the tenancy level (D), with optional filters like compartments or tags to monitor spending.'' A, B, and C are sub-elements---only D is the primary scope per OCI's cost management.

: Oracle Cloud Infrastructure Cost Management Documentation, 'Setting Budgets'.