Free Oracle 1z0-1110-25 Practice Exam with Questions & Answers

Detailed Answer in Step-by-Step Solution:

Objective: Identify two OCI Language AI capabilities.

Understand OCI Language: Focuses on text analysis tasks.

Evaluate Options:

A: Table extraction—Vision, not Language—incorrect.

B: Punctuation correction—Not offered—incorrect.

C: Sentence diagramming—Not supported—incorrect.

D: Topic classification—Supported (custom/pretrained)—correct.

E: Sentiment analysis—Supported (pretrained)—correct.

Reasoning: D and E are core text analysis features of OCI Language.

Conclusion: D and E are correct.

OCI documentation states: “OCI Language offers topic classification (D) and sentiment analysis (E) for text analysis, among other features.” A belongs to Vision, B and C aren’t available—only D and E match OCI Language’s capabilities.

Oracle Cloud Infrastructure Language Documentation, "Text Analysis Features".

Detailed Answer in Step-by-Step Solution:

Objective: 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: 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: Identify three stages in OCI AutoML Pipeline.

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

Evaluate Options:

A: Feature Selection—Selects relevant features—correct.

B: Adaptive Sampling—Reduces data size—correct.

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

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

E: Algorithm Selection—Chooses best model—correct.

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

Conclusion: A, B, E are correct.

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

Oracle Cloud Infrastructure AutoML Documentation, "Pipeline Components".

Detailed Answer in Step-by-Step Solution:

Objective: Determine the purpose of score.py in OCI Data Science model deployment.

Understand Model Deployment: When deploying a model in OCI, artifacts include score.py, runtime.yaml, etc.

Evaluate Options:

A: Infrastructure configuration (e.g., compute shape) is handled by deployment settings, not score.py.

B: score.py contains the inference logic (e.g., load_model(), predict())—correct.

C: Conda environment is defined in runtime.yaml or a requirements file—not score.py.

D: Scaling (e.g., instance count) is set in deployment configuration—not score.py.

Reasoning: score.py is the script executed by the deployment endpoint to load the model and make predictions.

Conclusion: B is the correct purpose.

The OCI Data Science documentation states: “The score.py file is a required artifact for model deployment, containing the inference logic—functions like load_model() to load the model and predict() to generate predictions from input data.” Infrastructure (A) and scaling (D) are managed via the OCI Console or SDK, while the environment (C) is specified in runtime.yaml. B is the precise role of score.py in OCI’s deployment workflow.

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

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:

Define Conda: Conda is a widely used tool for managing packages and environments in data science.

Evaluate Options:

A: Partially true—Conda manages dependencies, but it’s broader (an environment system).

B: Incorrect—Kernels (e.g., Jupyter) are separate; Conda manages environments.

C: Correct—Conda is an open-source tool for creating isolated environments with specific packages.

D: Incorrect—Not specific to Oracle AI; it’s a general tool.

Reasoning: C captures Conda’s full scope as an open-source system, beyond just dependency management (A).

Conclusion: C is the most accurate.

OCI documentation describes Conda as “an open-source package and environment management system that allows data scientists to create isolated environments with specific versions of Python and libraries.” A is too narrow, B misaligns with kernel concepts, and D ties it incorrectly to Oracle AI. C aligns with Conda’s official definition and OCI’s usage.

Oracle Cloud Infrastructure Data Science Documentation, "Conda Environments Overview".

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:

Objective: Identify the function that measures the difference between predicted and actual values in machine learning.

Understand ML Functions:

Optimizer function: Adjusts model parameters to minimize error (e.g., gradient descent)—it uses the cost, not defines it.

Fit function: Trains the model by fitting it to data—process-oriented, not a measure.

Update function: Typically updates weights during training—not a standard term for error measurement.

Cost function: Quantifies prediction error (e.g., MSE, cross-entropy)—directly represents the difference.

Evaluate Options:

A: Optimizer minimizes the cost, not the cost itself—incorrect.

B: Fit executes training, not error definition—incorrect.

C: Update is vague and not a standard ML term for this—incorrect.

D: Cost function (e.g., loss) measures prediction vs. target—correct.

Reasoning: The cost function (or loss function) is the mathematical representation of error, guiding optimization.

Conclusion: D is the correct answer.

In OCI Data Science, the documentation explains: “The cost function (or loss function) measures the difference between the model’s predicted values and the actual target values, such as mean squared error for regression or cross-entropy for classification.” Optimizers (A) use this to adjust weights, fit (B) is a training step, and update (C) isn’t a defined function here—only the cost function (D) fits the description. This aligns with standard ML terminology and OCI’s AutoML processes.

Oracle Cloud Infrastructure Data Science Documentation, "Machine Learning Concepts - Cost Functions".

Detailed Answer in Step-by-Step Solution:

Objective: Identify 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".