SNOWPRO ADVANCED: DATA SCIENTIST CERTIFICATION EXAM SURE PASS DUMPS & DSA-C03 ACTUAL TRAINING PDF

SnowPro Advanced: Data Scientist Certification Exam sure pass dumps & DSA-C03 actual training pdf

SnowPro Advanced: Data Scientist Certification Exam sure pass dumps & DSA-C03 actual training pdf

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Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q136-Q141):

NEW QUESTION # 136
You've deployed a fraud detection model in Snowflake using Snowpark. You are monitoring its performance and notice a significant decrease in recall, while precision remains high. This means the model is missing many fraudulent transactions. The training data was initially balanced, but you suspect that recent changes in user behavior have skewed the distribution of fraudulent vs. non-fraudulent transactions in production. Which of the following actions are MOST appropriate to address this issue and improve the model's performance, considering best practices for model retraining within the Snowflake ecosystem?

  • A. Retrain the model using a dataset that includes recent production data, being sure to re-balance the dataset to maintain a roughly equal number of fraudulent and non-fraudulent transactions. Prioritize transactions from the last month.
  • B. Implement a data drift monitoring system in Snowflake to automatically detect changes in the input features of the model. Trigger an automated retraining pipeline when significant drift is detected. This retraining should include recent production data with updated labels, but only if label data collection can be automated.
  • C. Retrain the model using the original training data. Since the precision is high, the model's fundamental logic is still sound. A larger training dataset isn't necessary.
  • D. Adjust the model's classification threshold to be more sensitive, even if it means accepting a slightly lower precision. This can be done directly within Snowflake using a SQL UDF that transforms the model's output probabilities.
  • E. Immediately shut down the model to prevent further inaccurate classifications. Investigate why the recall is low before any retraining is performed.

Answer: A,B,D

Explanation:
Options B, C, and D are the most appropriate. B addresses the data drift by incorporating recent production data with re-balancing to mitigate the skewed distribution. C directly improves recall by adjusting the classification threshold. D establishes a proactive drift detection and retraining system which is a best practice for long-term model maintenance. A is incorrect because the original data doesn't reflect current trends. E is too drastic initially; adjusting the threshold and retraining are preferred first. Retraining with balanced, recent data is critical, especially if the class distribution has shifted. Monitoring for drift provides an automated approach to maintaining model accuracy in a changing environment. Also a low code retraining pipeline is appropriate considering current model performance with SQL udf transformations.


NEW QUESTION # 137
Consider the following Python UDF intended to train a simple linear regression model using scikit-learn within Snowflake. The UDF takes feature columns and a target column as input and returns the model's coefficients and intercept as a JSON string. You are encountering an error during the CREATE OR REPLACE FUNCTION statement because of the incorrect deployment of the package during runtime. What would be the right way to fix this deployment and execute your model?

  • A. The code works seamlessly without modification as Snowflake automatically resolves all the dependencies and ensures the execution of code within the create or replace function statement.
  • B. The package 'scikit-learn' needs to be included in the import statement and deployed while creation of the 'Create or Replace function' statement, by including parameter. Also the correct code is to ensure the model can be trained and return the coefficients and intercept of the model.
  • C. The package 'scikit-learn' needs to be included in the import statement and deployed while creation of the 'Create or Replace function' statement, by including parameter. Also the correct code is to ensure the model can be trained and return the coefficients and intercept of the model.
  • D. The required packages 'scikit-learn' is not present. The correct way to create UDF is by including the import statement within the function along with the deployment.
  • E. The package 'scikit-learn' needs to be included in the import statement and deployed while creation of the 'Create or Replace function' statement, by including parameter. Also the correct code is to ensure the model can be trained and return the coefficients and intercept of the model.

Answer: E

Explanation:
Option E is the correct option and provides explanation for deploying the packages and ensuring that model executes successfully.


NEW QUESTION # 138
You are a data scientist working for a retail company that stores its transaction data in Snowflake. You need to perform feature engineering on customer purchase history data to build a customer churn prediction model. Which of the following approaches best combines Snowflake's capabilities with a machine learning framework (like scikit-learn) for efficient feature engineering? Assume your data is stored in a table named 'CUSTOMER TRANSACTIONS' with columns like 'CUSTOMER ID, 'TRANSACTION DATE, 'AMOUNT, and 'PRODUCT CATEGORY.

  • A. Use Snowflake's SQL UDFs (User-Defined Functions) written in Python to perform feature engineering directly within Snowflake on smaller aggregated sets of data to optimize compute costs. Integrate these UDFs to query the entire 'CUSTOMER TRANSACTIONS table to build your features.
  • B. Load a small subset of 'CUSTOMER_TRANSACTIONS' into an in-memory database like Redis, perform feature engineering using custom Python scripts interacting with Redis, and periodically sync the results back to Snowflake.
  • C. Develop a custom Spark application to read data from Snowflake, perform feature engineering in Spark, and write the resulting features back to a new table in Snowflake, and avoid use of Snowflake SQL UDFs to minimize complexity.
  • D. Create a Snowflake external function that calls a cloud-based (AWS, Azure, GCP) machine learning service for feature engineering, passing the raw transaction data for each customer and processing the aggregated data into features in Snowflake SQL.
  • E. Extract all the data from 'CUSTOMER_TRANSACTIONS' into a Pandas DataFrame, perform feature engineering using Pandas and scikit-learn, and then load the processed data back into Snowflake.

Answer: A

Explanation:
Snowflake UDFs allow you to execute Python code directly within Snowflake. This is particularly useful for feature engineering, as it allows you to leverage Snowflake's compute power and data locality. Extracting all data to Pandas (Option A) can be inefficient for large datasets. External functions (Option C) introduce latency and complexity. Spark (Option D) adds an external dependency, and leveraging redis (Option E) increases operational overhead. Using UDFs allows you to push down the computation to the data, improving performance and reducing data transfer costs.


NEW QUESTION # 139
You've developed a fraud detection model using Snowflake ML and want to estimate the expected payout (loss or gain) based on the model's predictions. The cost of investigating a potentially fraudulent transaction is $50. If a fraudulent transaction goes undetected, the average loss is $1000. The model's confusion matrix on a validation dataset is: Predicted Fraud Predicted Not Fraud Actual Fraud 150 50 Actual Not Fraud 20 780 Which of the following SQL queries in Snowflake, assuming you have a table 'FRAUD PREDICTIONS' with columns 'TRANSACTION ID', 'ACTUAL FRAUD', and 'PREDICTED FRAUD' (1 for Fraud, O for Not Fraud), provides the most accurate estimate of the expected payout for every 1000 transactions?

  • A. Option A
  • B. Option B
  • C. Option E
  • D. Option D
  • E. Option C

Answer: C

Explanation:
Option E correctly calculates the expected payout by subtracting the cost of false positives (investigating non-fraudulent transactions) from the loss due to false negatives (undetected fraudulent transactions). The confusion matrix data (50 false negatives, 20 false positives) translates to an expected payout of (1000 50) - (50 20) = $49000 loss for every 1000 transactions. The other queries either incorrectly combine the costs and losses, or only calculate one aspect. The other query calculate in correct format or not relevant as per context.


NEW QUESTION # 140
You are tasked with performing data profiling on a large customer dataset in Snowflake to identify potential issues with data quality and discover initial patterns. The dataset contains personally identifiable information (PII). Which of the following Snowpark and SQL techniques would be most appropriate to perform this task while minimizing the risk of exposing sensitive data during the exploratory data analysis phase?

  • A. Utilize Snowpark to create a sampled dataset (e.g., 1% of the original data) and perform all exploratory data analysis on the sample to reduce the data volume and potential exposure of PII.
  • B. Export the entire customer dataset to an external data lake for exploratory analysis using Spark and Python. Apply data masking in Spark before analysis.
  • C. Create a masked view of the customer data using Snowflake's dynamic data masking features. This view masks sensitive PII columns while allowing you to compute aggregate statistics and identify patterns using SQL and Snowpark functions. Columns like 'email' are masked using and columns like are masked using .
  • D. Apply differential privacy techniques using Snowpark to add noise to the summary statistics generated from the customer data, masking the individual contributions of each customer while revealing overall trends.
  • E. Directly query the raw customer data using SQL and Snowpark, computing descriptive statistics like mean, median, and standard deviation for all numeric columns and frequency counts for categorical columns. Store the results in a temporary table for further analysis.

Answer: C,D

Explanation:
Options C and D provide the most secure and effective ways to perform exploratory data analysis while protecting PII. Differential privacy (C) ensures that aggregate statistics do not reveal too much information about individuals. Masked views (D) prevent direct access to sensitive data, replacing it with masked values during the analysis. A is dangerous because it exposes the raw data. B while reduces the volume, still exposes raw data. E is risky because it involves exporting sensitive data outside of Snowflake.


NEW QUESTION # 141
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