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DSA-C03 Schulungsangebot - DSA-C03 Fragen&Antworten

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Snowflake SnowPro Advanced: Data Scientist Certification Exam DSA-C03 Prüfungsfragen mit Lösungen (Q255-Q260):

255. Frage
You're building a customer segmentation model and need to aggregate data from various tables. You have the following tables in Snowflake: 'customer demographics' (customer id, age, city, income) 'customer transactionS (transaction_id, customer id, transaction_date, amount) 'product_details' (product_id, category) 'transaction_products' (transaction_id, product_id) Your goal is to create a single Snowpark DataFrame containing customer demographics along with the total amount spent by each customer on products within the 'Electronics' category in the last year. However, ensure that only customers with income greater than 50000 are considered and handle cases where customers have no transaction records, assigning a value of 0 to the 'total_electronics_spending' column for those customers. How can we achieve this using snowpark? Choose the correct options

A. Create a Python UDF that performs the joins and aggregations. This offers flexibility and good performance when dealing with complex data transformations.
B. Use a combination of LEFT JOINs and filtering. Start with 'customer_demographics' (filtered for income > 50000) as the base table and LEFT JOIN to subsequent tables. Use the 'coalesce' function to handle customers without transaction data.
C. Create a temporary view to store total electronics expenditure of each customer and left join with customer demographics table.
D. Create a complex SQL query within Snowpark using 'session.sql()' to perform all the joins, filtering, and aggregation in a single step. This will be the most efficient approach.
E. Use a series of INNER JOINs to connect the tables and filter data, followed by grouping and aggregation. This approach guarantees accurate results with good performance.

Antwort: B,C,D

Begründung:
Option B, C and D are correct. Option B is correct because using LEFT JOINs starting with 'customer_demographics' (after filtering for income) ensures all eligible customers are included. 'coalesce' is crucial for handling customers with no transactions, assigning a 0 value. Option C is also correct as using a temporary view is a valid solution to have electronics expenditure for each customer. Option D is correct as pushing down all operations to SQL within Snowpark can be highly performant, as it allows Snowflake to optimize the query execution. However, query readability and maintainability should also be considered. Option A is incorrect because it states that INNER JOINs should be used, but inner joins would exclude customers with no transaction data which is opposite to what is stated in the question. Option E is incorrect as UDFs can introduce performance overhead compared to native Snowpark DataFrame operations or direct SQL queries, especially for large datasets. Avoid UDF when the same output can be achieved without it.

256. Frage
You are analyzing customer transaction data in Snowflake to identify fraudulent activities. The 'TRANSACTION AMOUNT' column exhibits a right-skewed distribution. Which of the following Snowflake queries is MOST effective in identifying outliers based on the Interquartile Range (IQR) method, specifically targeting unusually large transaction amounts? Assume IQR is already calculated as variable and QI as and Q3 as in snowflake session.

A. SELECT TRANSACTION ID FROM TRANSACTIONS WHERE TRANSACTION AMOUNT > q3 + (1.5 iqr);
B. SELECT TRANSACTION ID FROM TRANSACTIONS WHERE TRANSACTION_AMOUNT > (SELECT + 3 FROM TRANSACTIONS);
C. SELECT TRANSACTION ID FROM TRANSACTIONS WHERE TRANSACTION_AMOUNT < qi - (1.5 iqr);
D. SELECT TRANSACTION ID FROM TRANSACTIONS WHERE TRANSACTION_AMOUNT > (SELECT MEDIAN(TRANSACTION AMOUNT) FROM TRANSACTIONS);
E. SELECT TRANSACTION ID FROM TRANSACTIONS WHERE TRANSACTION_AMOUNT > (SELECT WITHIN GROUP (ORDER BY TRANSACTION_AMOUNT) FROM TRANSACTIONS);

Antwort: A

Begründung:
Option B correctly implements the IQR method for identifying outliers. The formula 'Q3 + (1.5 identifies values significantly higher than the third quartile, which is appropriate for detecting outliers in a right-skewed distribution. Options A uses standard deviation, which is less robust to outliers. Option C finds the 95th percentile but might not isolate extreme outliers. Option D looks for lower bound outliers, which is not the case. Option E uses the median which not effective to find out outlier values.

257. Frage
You are using Snowpark Pandas to prepare data for a machine learning model. You have a Snowpark DataFrame named 'transactions df that contains transaction data, including 'transaction id', 'product id', 'customer id', and 'transaction_amount'. You want to create a new feature that represents the average transaction amount per customer. However, you are concerned about potential skewness in the 'transaction_amount' and want to apply a log transformation to reduce its impact before calculating the average. Which of the following steps using Snowpark Pandas would achieve this transformation and calculation most efficiently within Snowflake?

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

Antwort: C

Begründung:
Option B is the most efficient solution because it performs both the log transformation and the average calculation entirely within Snowflake using Snowpark functions. This avoids the overhead of transferring the data to the client side. It uses F.logl p() to apply the log transformation to the 'transaction_amount' column, handling potential zero values gracefully. It groups by 'customer_id' and uses F.mean() to calculate the average of the transformed transaction amounts.

258. Frage
You are building a multi-class classification model in Snowflake to predict the category of customer support tickets (e.g., 'Billing', 'Technical Support', 'Sales Inquiry', 'Account Management', 'Feature Request') based on the ticket's text content. The initial model evaluation shows an overall accuracy of 75%, but the 'Feature Request' category has a significantly lower precision and recall compared to other categories. Which of the following strategies would be MOST effective in addressing this issue, considering the limitations and advantages of Snowflake's data processing capabilities and typical machine learning practices?

A. Apply a cost-sensitive learning approach during model training, assigning a higher misclassification cost to errors involving the 'Feature Request' category. This encourages the model to prioritize correctly classifying feature requests.
B. Engineer new features specifically designed to improve the model's ability to distinguish 'Feature Request' tickets from other categories. This could involve creating sentiment scores for 'innovation' or using topic modeling to identify key themes related to feature requests.
C. Increase the threshold for classifying a ticket as 'Feature Request' to improve precision, even if it further reduces recall. This prioritizes accurate identification of feature requests over capturing all of them.
D. All of the above.
E. Oversample the 'Feature Request' category in the training dataset before training the model. This involves creating synthetic data points or duplicating existing data to balance the class distribution. This can be done using SQL and Snowflake's internal stage for storing temporary data before training.

Antwort: D

Begründung:
All options are potentially beneficial. Increasing the threshold (A) improves precision. Oversampling (B) addresses class imbalance. Cost-sensitive learning (C) penalizes misclassification. Feature engineering (D) improves discrimination. Therefore, the optimal solution may involve combining these strategies. Oversampling can be implemented using SQL and INSERT INTO statements in Snowflake, storing the oversampled data in a temporary table. Cost-sensitive learning might involve adjusting model weights or using a custom loss function (depending on the chosen model framework, potentially requiring integration with external ML tools).

259. Frage
You are working on a fraud detection model and need to prepare transaction data'. You have two tables: 'transactions' (transaction_id, customer_id, transaction_date, amount, merchant_id) and (merchant_id, city, state). You need to perform the following data cleaning and feature engineering steps using Snowpark: 1. Remove duplicate transactions based on 'transaction_id'. 2.
Join the 'transactions' table with the 'merchant_locations table to add city and state information to each transaction. 3. Create a new feature called 'amount_category' based on the transaction amount, categorized as 'Low', 'Medium', or 'High'. 4. The categorization thresholds are defined as follows: 'LoW: amount < 50 'Medium': 50 amount < 200 'High': amount >= 200 Which of the following statements about performing these operations using Snowpark are accurate?

A. Removing duplicate transactions can be efficiently done using the method on the Snowpark DataFrame, specifying 'transaction_id' as the subset. Creating the amount categories requires use of a User-Defined Function (UDF) as the logic can't be efficiently embedded in a single 'when' clause.
B. Removing duplicate transactions can be efficiently done using the method on the Snowpark DataFrame, specifying 'transaction_id' as the subset. Creating the amount categories can be completed using the 'when' clause with multiple 'otherwise' clauses.
C. You can register SQL UDF to calculate the 'amount_category' using 'CASE WHEN' statement
D. The construct in Snowpark can be used to create the 'amount_category' feature directly within the DataFrame transformation without needing a UDF
E. A LEFT JOIN should be used to join the 'transactions' and 'merchant_location' tables to ensure that all transactions are included, even if some merchant IDs are not present in the 'merchant_location' table.

Antwort: B,C,D

Begründung:
Options C, D and E are correct. Option C is correct because Snowpark's construct allows creating new features based on conditional logic directly within DataFrame transformations, avoiding the need for a UDF for simple categorizations like this. Option D is correct because SQL UDF can be used to create a function that returns Option E is also correct because the method efficiently removes duplicates, and the 'when' clauses enables easy categorization of in snowflake. Option A is incorrect, the categorization doesn't necessarily require UDF. Option B is incorrect since a RIGHT or INNER join is valid as well.

260. Frage
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