1z0-1110-25 Exam Dumps - Oracle Cloud Infrastructure Questions and Answers

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: Transcribe Spanish video audio and identify profanity.

Evaluate Options:

A: Anomaly Detection—Not for transcription or text analysis.

B: Speech—Converts audio to text (e.g., Spanish), base for further analysis—correct.

C: Translation—Translates text, not transcription.

D: Language—Analyzes text (e.g., profanity), but needs transcribed input.

Reasoning: Speech (B) transcribes video audio; Language could follow for profanity.

Conclusion: B is correct for transcription.

OCI Speech “transcribes audio from video or audio files into text, supporting languages like Spanish.” Post-transcription, OCI Language could detect profanity, but B is the starting point—Anomaly (A) and Translation (C) don’t fit.

Oracle Cloud Infrastructure Speech Documentation, "Transcription Features".

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

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

Evaluate Options:

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

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

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

D: Random—Defeats tuning purpose.

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

Conclusion: A is correct.

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

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

Detailed Answer in Step-by-Step Solution:

Objective: 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: 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: 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: 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:

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

Evaluate Options:

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

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

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

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

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

Conclusion: D is correct.

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

Oracle Cloud Infrastructure Language Documentation, "Language Detection".