You want to make your model more parsimonious to reduce the cost of collecting and processing data. You plan to do this by removing features that are highly correlated. You would like to create a heatmap that displays the correlation so that you can identify candidate features to remove. Which Accelerated Data Science (ADS) SDK method would be appropriate to display the correlation between Continuous and Categorical features?
Correct Answer: B
Detailed Answer in Step-by-Step Solution: * Objective: Visualize correlation between continuous and categorical features using ADS SDK. * Understand Correlation Types: * Continuous vs. Continuous: Pearson correlation. * Categorical vs. Categorical: Cramer's V. * Continuous vs. Categorical: Correlation ratio (eta). * Evaluate Options: * A. corr(): General correlation (Pearson), not suited for mixed types-incorrect. * B. correlation_ratio_plot(): Plots correlation ratio for continuous-categorical-correct. * C. pearson_plot(): Not an ADS method; Pearson is continuous-only-incorrect. * D. cramersv_plot(): Cramer's V for categorical-categorical-incorrect. * Reasoning: Correlation ratio measures association between continuous and categorical variables-ideal for heatmap in this mixed scenario. * Conclusion: B is correct. OCI documentation states: "The correlation_ratio_plot() method (B) in ADS SDK generates a heatmap displaying the correlation ratio between continuous and categorical features, suitable for identifying highly correlated features for removal." corr() (A) defaults to Pearson, pearson_plot() (C) isn't real, and cramersv_plot() (D) is for categorical pairs-only B aligns with OCI's ADS capabilities for this use case. Oracle Cloud Infrastructure ADS SDK Documentation, "Correlation Visualization Methods".
1z0-1110-25 Exam Question 57
Which function's objective is to represent the difference between the predictive value and the target value?
Correct Answer: D
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".
1z0-1110-25 Exam Question 58
You want to build a multistep machine learning workflow by using the Oracle Cloud Infrastructure (OCI) Data Science Pipeline feature. How would you configure the conda environment to run a pipeline step?
Correct Answer: D
Detailed Answer in Step-by-Step Solution: * Objective: Configure conda env for a pipeline step. * Evaluate Options: * A: Shape-Infra, not env config. * B: Volume-Storage, not env. * C: Command-line-Step args, not env. * D: Env variables-Sets conda path-correct. * Reasoning: D specifies runtime env (e.g., CONDA_ENV_SLUG). * Conclusion: D is correct. OCI documentation states: "Configure a pipeline step's conda environment using environment variables (D), such as CONDA_ENV_SLUG, in the step definition." A, B, and C address other aspects-only D fits env config. Oracle Cloud Infrastructure Data Science Documentation, "Pipeline Step Configuration".
1z0-1110-25 Exam Question 59
As a data scientist, you are working on a global health dataset that has data from more than 50 countries. You want to encode three features, such as 'countries', 'race', and 'body organ' as categories. Which option would you use to encode the categorical feature?
Correct Answer: C
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".
1z0-1110-25 Exam Question 60
Which statement about Oracle Cloud Infrastructure Anomaly Detection is true?
Correct Answer: C
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".
