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[General] Updated DSA-C03 Dumps Authoritative Questions Pool Only at FreeDumps

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【General】 Updated DSA-C03 Dumps Authoritative Questions Pool Only at FreeDumps

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Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q178-Q183):NEW QUESTION # 178
You are building a customer churn prediction model in Snowflake using Snowflake ML. After training, you need to evaluate the model's performance and identify areas for improvement. Given the following table 'PREDICTIONS' contains predicted probabilities and actual churn labels, which SQL query effectively calculates both precision and recall for the churn class (where 'CHURN = 1')?

  • A. Option E
  • B. Option D
  • C. Option A
  • D. Option C
  • E. Option B
Answer: C
Explanation:
Option A correctly calculates precision and recall. Precision is calculated as True Positives / (True Positives + False Positives), and Recall is calculated as True Positives / (True Positives + False Negatives). The query in option A directly implements these formulas, where 'PREDICTED CHURN = 1 AND CHURN = 1' represents True Positives. Option B and E calculates accuracy. Option C calculates correlation. Option D calculates Precision and Recall for the negative class (non-churn).

NEW QUESTION # 179
You are tasked with presenting a business case to stakeholders demonstrating the value of a new machine learning model that predicts customer churn. The model has been trained on data within Snowflake, and you have various metrics such as accuracy, precision, recall, and F I-score. You also have feature importance scores generated using a SHAP (SHapley Additive exPlanations) explainer. Which of the following visualization strategies, when combined, would MOST effectively communicate the model's performance and impact to a non-technical audience, while also providing sufficient detail for technical stakeholders?
  • A. A confusion matrix visualizing the true positives, true negatives, false positives, and false negatives, along with a summary plot of the SHAP values showing the impact of each feature on the model's prediction for a representative sample of customers. A line chart showing cumulative churn rate across different customer segments.
  • B. A scatter plot showing the relationship between two key features identified by SHAP, colored by the model's churn prediction, and a table summarizing the model's performance metrics (accuracy, precision, recall, F I-score). Additionally, include a waterfall plot for a specific customer, illustrating how each feature contributes to the final prediction.
  • C. A ROC curve (Receiver Operating Characteristic) showing the trade-off between true positive rate and false positive rate, paired with a detailed table of all feature importance scores generated by the SHAP explainer. Present statistical summaries, such as mean and standard deviation, of the top 5 feature values, grouped by predicted churn probability.
  • D. A distribution plot (e.g., histogram or KDE) of the predicted churn probabilities, segmented by actual churn status (churned vs. not churned), combined with a SHAP force plot visualizing the feature contributions for a single, randomly selected customer who churned. Add a section on potential cost savings from churn reduction.
  • E. A simple bar chart showing the overall accuracy score of the model alongside a table detailing the precision, recall, and F I-score. Include a word cloud of the most important features from the SHAP values.
Answer: A,B
Explanation:
Options B and D provide a balanced approach for both technical and non-technical audiences- A confusion matrix (Option B) is easily understandable and shows model performance across different prediction outcomes. A summary plot of SHAP values clearly illustrates feature importance and direction of impact. A line chart showing cumulative churn rate across different customer segments highlights the business value-Option D is also highly effective because scatter plots can be easily understood, especially when colored by churn prediction- The table of model metrics provides necessary details. The waterfall plot brings the explanation down to an individual customer level, making the model's behavior more tangible. Options A, C and E have deficits- Option A lacks detailed performance visualization. Option C is technical and might confuse non-technical stakeholders. Option E has too many summary plots.

NEW QUESTION # 180
You are developing a model to predict equipment failure in a factory using sensor data stored in Snowflake. The data is partitioned by 'EQUIPMENT ID' and 'TIMESTAMP. After initial model training and cross-validation using the following code snippet:

You observe significant performance variations across different equipment groups when evaluating on out-of-sample data'. Which of the following strategies could you employ to address this issue within the Snowflake environment to improve the model's generalization ability across all equipment?
  • A. Create seperate models per equipment ID. For each equipment ID, split data into training and testing data. For each equipment ID, use 'SYSTEM$OPTIMIZE MODEL' to perform hyper parameter search individually. Train and Deploy the model at equipement ID Level.
  • B. Retrain the model with additional feature engineering to create interaction terms between 'EQUIPMENT_ID' and other relevant sensor features to capture equipment-specific patterns. For instance, you can one hot encode and add to model and include in 'INPUT DATA'.
  • C. Implement cross-validation at the partition level by splitting 'TRAINING_DATX into train and test sets before creating the model, and then using the 'FIT' command to train on the train set and 'PREDICT to evaluate on the test set, repeating for each partition.
  • D. Implement a hyperparameter search using 'SYSTEM$OPTIMIZE_MODEL' with a wider range of parameters for each 'EQUIPMENT_ID individually, creating a separate model for each 'EQUIPMENT ID.
  • E. Increase the overall size of the "TRAINING_DATR to include more historical data for all equipment, assuming this will balance the representation of each EQUIPMENT ID'
Answer: A,B
Explanation:
Options C and E are the most effective strategies. Option C (Feature Engineering): By creating interaction terms between EQUIPMENT _ ICY and other sensor features, the model can learn equipment-specific patterns. This enables the model to account for the unique characteristics of each equipment group, improving its ability to generalize across all equipment. For example, the optimal temperature threshold for triggering a failure might differ significantly between EQUIPMENT_ID' groups, and this can be captured using interaction terms. Option E (Seperate models per Equipment ID) : Hyperparameter tuning and training separate models per equipment ID enables you to optimize and customize the model specific to each equipment ID. The downsize is that we need to create and manage more models. Options A and D are less effective or may have limitations: Option A (Increase Training Data Size): While increasing the training data size can sometimes improve model performance, it doesn't guarantee that the model will learn to differentiate between the equipment groups effectively, especially if some groups have significantly different data characteristics. This can also consume a lot of resources unnecessarily. Option D (Custom cross Validation) : While it's valid, it is difficult to implement and the built in Snowflake cross validation features is much more performant and easier to use.

NEW QUESTION # 181
You are building a binary classification model in Snowflake to predict customer churn based on historical customer data, including demographics, purchase history, and engagement metrics. You are using the SNOWFLAKE.ML.ANOMALY package. You notice a significant class imbalance, with churn representing only 5% of your dataset. Which of the following techniques is LEAST appropriate to handle this class imbalance effectively within the SNOWFLAKE.ML framework for structured data and to improve the model's performance on the minority (churn) class?
  • A. Adjusting the decision threshold of the trained model to optimize for a specific metric, such as precision or recall, using a validation set. This can be done by examining the probability outputs and choosing a threshold that maximizes the desired balance.
  • B. Using a clustering algorithm (e.g., K-Means) on the features and then training a separate binary classification model for each cluster to capture potentially different patterns of churn within different customer segments.
  • C. Applying a SMOTE (Synthetic Minority Over-sampling Technique) or similar oversampling technique to generate synthetic samples of the minority class before training the model outside of Snowflake, and then loading the augmented data into Snowflake for model training.
  • D. Downsampling the majority class to create a more balanced training dataset within Snowflake using SQL before feeding the data to the modeling function.
  • E. Using the 'sample_weight' parameter in the 'SNOWFLAKE.ML.ANOMALY.FIT function to assign higher weights to the minority class instances during model training.
Answer: B
Explanation:
E is the LEAST appropriate. While clustering and training separate models per cluster can be a useful strategy for improving overall model performance by capturing heterogeneous patterns, it doesn't directly address the class imbalance problem within each cluster's dataset. Applying clustering does nothing about the class imbalance and adds unnecessary complexity. A, B, C, and D are all standard methods for handling class imbalance. A uses weighted training. B and D address resampling of the training set. C addresses the classification threshold.

NEW QUESTION # 182
You are working with a large dataset of sensor readings stored in a Snowflake table. You need to perform several complex feature engineering steps, including calculating rolling statistics (e.g., moving average) over a time window for each sensor. You want to use Snowpark Pandas for this task. However, the dataset is too large to fit into the memory of a single Snowpark Pandas worker. How can you efficiently perform the rolling statistics calculation without exceeding memory limits? Select all options that apply.
  • A. Break the Snowpark DataFrame into smaller chunks using 'sample' and 'unionAll', process each chunk with Snowpark Pandas, and then combine the results.
  • B. Explore using Snowpark's Pandas user-defined functions (UDFs) with vectorization to apply custom rolling statistics logic directly within Snowflake. UDFs allow you to use Pandas within Snowflake without needing to bring the entire dataset client-side.
  • C. Increase the memory allocation for the Snowpark Pandas worker nodes to accommodate the entire dataset.
  • D. Utilize the 'window' function in Snowpark SQL to define a window specification for each sensor and calculate the rolling statistics using SQL aggregate functions within Snowflake. Leverage Snowpark to consume the results of the SQL transformation.
  • E. Use the 'grouped' method in Snowpark DataFrame to group the data by sensor ID, then download each group as a Pandas DataFrame to the client and perform the rolling statistics calculation locally. Then upload back to Snowflake.
Answer: B,D
Explanation:
Explanation:Options B and D are the most appropriate and efficient solutions for handling large datasets when calculating rolling statistics with Snowpark Pandas. Option B uses the 'window' function in Snowpark SQL. Leverage the 'window' function in Snowpark SQL to define a window specification for each sensor and calculate the rolling statistics using SQL aggregate functions within Snowflake. Option D uses Snowpark's Pandas UDFs. Snowpark's Pandas UDFs with vectorization allow you to bring the processing logic to the data within Snowflake, avoiding the need to move the entire dataset to the client-side and bypassing memory limitations. This approach is generally more scalable and performant for large datasets. Option A is inefficient as it retrieves groups of data from Snowflake to client side before creating the calculations before sending back to snowflake. Option C is correct but complex and not optimal. Option E is possible, but it's not a scalable solution and can be costly.

NEW QUESTION # 183
......
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