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New DSA-C03 Test Pdf & DSA-C03 Test Result

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Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q179-Q184):

NEW QUESTION # 179
You are building a machine learning model using Snowpark for Python and have a feature column called 'TRANSACTION AMOUNT' in your 'transaction_df DataFrame. This column contains some missing values ('NULL). Your model is sensitive to missing data'. You want to impute the missing values using the median "TRANSACTION AMOUNT, but ONLY for specific customer segments (e.g., customers with a 'CUSTOMER TIER of 'Gold' or 'Platinum'). For other customer tiers, you want to impute with the mean. Which of the following Snowpark Python code snippets BEST achieves this selective imputation?

Answer: E

Explanation:
Option B is the most correct. It correctly calculates the median and mean for the specified customer segments using 'agg()' with .alias(y to name the resulting aggregate columns, and then retrieves the values using . This approach correctly handles the aggregation and retrieval of the calculated median and mean values. Option A uses which although technically works, is less readable than the aliased approach. The method provides similar performance benefits to the method with simpler syntax, as you retrieve only the first row of the DataFrame. 'toLocallterator' is a performant way to get local access to the result of an aggregation function when a small number of rows are expected. Option C fails because it attempts to use the aggregate directly without materializing the value. The comparison between using .agg(), .collect(), .first(), and .toLocallterator() demonstrates performance tuning knowledge.

NEW QUESTION # 180
You're building a linear regression model in Snowflake to predict house prices. You have the following features: 'square_footage', 'number of bedrooms', 'location id', and 'year built'. 'location id' is a categorical variable representing different neighborhoods. You suspect that the relationship between 'square footage' and 'price' might differ based on the 'location id'. Which of the following approaches in Snowflake are BEST suited to explore and model this potential interaction effect?

A. Apply a power transformation to 'square_footage' before including it in the linear regression model. This correct, but only to one variable.
B. Create interaction terms by adding 'square_footage' and one-hot encoded columns derived from 'location_id'. Include these interaction terms in the linear regression model.
C. Fit separate linear regression models for each unique 'location_id', using 'square_footage', 'number_of_bedrooms', and 'year_built' as independent variables.
D. Create interaction terms by multiplying 'square_footage' with one-hot encoded columns derived from 'location_id'. Include these interaction terms in the linear regression model.
E. Use the 'QUALIFY clause in Snowflake SQL to filter the data based on 'location_id' before calculating regression coefficients. This is incorrect approach.

Answer: D

Explanation:
Creating interaction terms by multiplying 'square_footage' with one-hot encoded columns from 'location_id' allows the model to estimate different slopes for 'square_footage' for each location. This directly models the interaction effect. Fitting separate models might be computationally expensive and does not allow for sharing of information across locations. The QUALIFY clause is used for filtering and not directly relevant to modeling interactions. A power transformation only affects 'square_footage' and not the interaction effect. Adding instead of multiplying will not create an interaction.

NEW QUESTION # 181
You have deployed a regression model in Snowflake as an external function using AWS Lambda'. The external function takes several numerical features as input and returns a predicted value. You want to continuously monitor the model's performance in production and automatically retrain it when the performance degrades below a predefined threshold. Which of the following methods represent VALID approaches for calculating and monitoring model performance within the Snowflake environment and triggering the retraining process?

A. Create a view that joins the input features with the predicted output and the actual result. Configure model monitoring within the AWS Sagemaker to perform continuous validation of the model.
B. Build a Snowpark Python application deployed on Snowflake which periodically polls the external function's performance by querying the function with a sample data set and comparing results to ground truth stored in Snowflake. Initiate retraining directly from the Snowpark application if performance degrades.
C. Utilize Snowflake's Alerting feature, setting an alert rule based on the output of a SQL query that calculates performance metrics. Configure the alert action to invoke a webhook that triggers a retraining pipeline.
D. Create a Snowflake Task that periodically executes a SQL query to calculate performance metrics (e.g., RMSE) by comparing predicted values from the external function with actual values stored in a separate table. Trigger a Python UDF, deployed as a Snowflake stored procedure, to initiate retraining if the RMSE exceeds the threshold.
E. Implement custom logging within the AWS Lambda function to capture prediction results and actual values. Configure AWS CloudWatch to monitor these logs and trigger an AWS Step Function that initiates a new training job and updates the Snowflake external function with the new model endpoint upon completion.

Answer: C,D,E

Explanation:
Options A, B, and C all represent valid approaches. A uses Snowflake Tasks, SQL queries for metrics, and UDFs/stored procedures for retraining. B uses AWS Lambda logging, CloudWatch, and Step Functions to orchestrate retraining. C leverages Snowflake's Alerting feature and webhooks. D, while technically possible, is not scalable as polling an external function from Snowpark introduces unnecessary latency and overhead. E is partially correct; however Sagemaker can't directly validate data with the actual result in Snowflake. Therefore, we must use alerting or tasks within snowflake.

NEW QUESTION # 182
A data scientist needs to calculate the cumulative moving average of sales for each product in a table. The table contains columns: (INT), (DATE), and (NUMBER). The desired output should include the product_id', 'sale_date', and Which of the following Snowflake SQL statements correctly calculates the cumulative moving average for each product using window functions?

A. SELECT product_id, sale_date, daily_sales, OVER (PARTITION BY product_id ORDER BY sale_date AS cumulative_average FROM sales_by_day;
B. SELECT product_id, sale_date, daily_sales, OVER (PARTITION BY product_id ORDER BY sale_date ASC) / OVER (PARTITION BY product_id ORDER BY sale_date ASC) AS cumulative_average FROM sales_by_day;
C. SELECT product_id, sale_date, daily_sales, OVER (PARTITION BY product_id ORDER BY sale_date ASC ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS cumulative_average FROM
D. SELECT product_id, sale_date, daily_sales, AVG(daily_sales) OVER (PARTITION BY product_id) AS cumulative_average FROM sales_by_day;
E. SELECT product_id, sale_date, daily_sales, AVG(daily_sales) OVER (ORDER BY sale_date ASC) AS cumulative_average FROM sales_by_day;

Answer: B,C

Explanation:
Both options B and D are correct. Option B correctly uses the 'AVG()' window function with the 'PARTITION BY product_id' clause to calculate the average sales for each product independently, and 'ORDER BY sale_date ASC' along with 'ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW' ensures a cumulative average is calculated from the beginning of the product's sales history up to the current date. Option D also calculates the cumulative moving average by summing the and dividing by the current row number, effectively computing the average.

NEW QUESTION # 183
A data science team at a retail company is using Snowflake to store customer transaction data'. They want to segment customers based on their purchasing behavior using K-means clustering. Which of the following approaches is MOST efficient for performing K-means clustering on a very large customer dataset in Snowflake, minimizing data movement and leveraging Snowflake's compute capabilities, and adhering to best practices for data security and governance?

A. Using a Snowflake User-Defined Function (UDF) written in Python that leverages the scikit-learn library within the UDF to perform K-means clustering directly on the data within Snowflake. Ensure the UDF is called with appropriate resource allocation (WAREHOUSE SIZE) and security context.
B. Employing only Snowflake's SQL capabilities to perform approximate nearest neighbor searches without implementing the full K-means algorithm. This compromises the accuracy and effectiveness of the clustering results.
C. Using Snowflake's Snowpark DataFrame API with a Python UDF to preprocess the data and execute the K-means algorithm within the Snowflake environment. This approach allows for scalable processing within Snowflake's compute resources with data kept securely within the governance boundaries.
D. Exporting the entire customer transaction dataset from Snowflake to an external Python environment, performing K-means clustering using scikit-learn, and then importing the cluster assignments back into Snowflake as a new table. This approach involves significant data egress and potential security risks.
E. Implementing K-means clustering using SQL queries with iterative JOINs and aggregations to calculate centroids and assign data points to clusters. This approach is computationally expensive and not recommended for large datasets. Moreover, security considerations are minimal.

Answer: C

Explanation:
Snowpark and Python UDFs provide a way to execute code within the Snowflake environment, leveraging its compute resources and keeping data within Snowflake's security and governance boundaries. This avoids data egress and is more efficient than exporting data or attempting to implement K-means directly in SQL. While B is potentially viable, D leveraging DataFrames provides further optimization. The other options are either inefficient or insecure.

NEW QUESTION # 184
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