SNOWFLAKE DSA-C03 DETAIL EXPLANATION | DSA-C03 LATEST BRAINDUMPS

Snowflake DSA-C03 Detail Explanation | DSA-C03 Latest Braindumps

Snowflake DSA-C03 Detail Explanation | DSA-C03 Latest Braindumps

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Tags: DSA-C03 Detail Explanation, DSA-C03 Latest Braindumps, DSA-C03 Exam Consultant, Valid DSA-C03 Test Simulator, DSA-C03 Valid Exam Camp Pdf

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Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q204-Q209):

NEW QUESTION # 204
You are tasked with building a predictive model in Snowflake to identify high-value customers based on their transaction history. The 'CUSTOMER_TRANSACTIONS table contains a 'TRANSACTION_AMOUNT column. You need to binarize this column, categorizing transactions as 'High Value' if the amount is above a dynamically calculated threshold (the 90th percentile of transaction amounts) and 'Low Value' otherwise. Which of the following Snowflake SQL queries correctly achieves this binarization, leveraging window functions for threshold calculation and resulting in a 'CUSTOMER SEGMENT column?

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

Answer: A,D,E

Explanation:
Options A, B and C are correct. Option A uses a window function to calculate the 90th percentile directly within the SELECT statement. Option B calculates the percentile using a common table expression (CTE) which can be reusable. Option C Uses IFF (inline if) with percentile_cont that is valid option. Option D and E are incorrect because APPROX PERCENTILE requires the 'WITHIN GROUP' clause and an 'ORDER BY' clause which cannot be applied when used like this.


NEW QUESTION # 205
You are tasked with building a model to predict customer churn. You have a table named in Snowflake with the following relevant columns: 'customer_id', 'login_date', , 'orders_placed', , and 'churned' (binary indicator). You want to engineer features that capture customer engagement over time using Snowpark for Python. Which of the following feature engineering steps, applied sequentially, are MOST effective in creating features indicative of churn risk?

  • A. 1. Calculate the average 'page_views' per week for each customer over the last 3 months using a window function. 2. Calculate the recency of the last order (days since last order) for each customer. 3. Create a feature indicating the change in average daily page views over the last month compared to the previous month. 4. Create a feature showing standard deviation of page_views per customer over the last 90 days.
  • B. 1. Calculate the maximum 'page_views' in a single day for each customer. 2. Calculate the total number of days with no 'login_date' for each customer. 3. Create a feature indicating if a customer has ever placed an order. 4. Use a simple boolean for the 'subscription_type' column.
  • C. 1. Calculate the number of days since the customer's last login, and use nulls instead of negative numbers to indicate inactivity. 2. Calculate the rolling 7-day average of 'orders_placed' using a window function, partitioning by 'customer_id' and ordering by 'login_date'. 3. Calculate the slope of a linear regression of page_views' over time for each customer, indicating the trend in engagement using Snowpark ML. 4. Calculate the percentage of weeks the customer logged in. 5. Create a feature showing standard deviation of page_views per customer over the last 90 days.
  • D. 1. Calculate the average 'page_views' per day for each customer. 2. Calculate the total number of for each customer. 3. Create a feature indicating whether the customer has a premium subscription ('subscription_type' = 'premium').
  • E. 1. Calculate the total 'page_views' and 'orders_placed' for each customer without considering time. 2. Use one-hot encoding for the 'subscription_type' column.

Answer: A,C

Explanation:
Options B and E are the MOST effective because they incorporate time-based features and indicators of engagement trends. Recency (days since last order) captures the time elapsed since the customer's last interaction. Calculating changes in page views, the number of login days and linear regression slope identifies trends in engagement. Rolling averages smooth out daily fluctuations and capture longer-term patterns. Standard deviation of page views indicates a trend in page view variance, and thus overall customer engagement variance. Option A lacks recency and trend information. Option C misses temporal analysis. Option D has less relevance features and can be used however it is more useful to compare how well a customer is engaged with previous activity.


NEW QUESTION # 206
You are exploring a large dataset of website user behavior in Snowflake to identify patterns and potential features for a machine learning model predicting user engagement. You want to create a visualization showing the distribution of 'session_duration' for different 'user_segments'. The 'user_segmentS column contains categorical values like 'New', 'Returning', and 'Power User'. Which Snowflake SQL query and subsequent data visualization technique would be most effective for this task?

  • A. Query: 'SELECT user_segments, APPROX 0.25), APPROX 0.5), APPROX_PERCENTlLE(session_duration, 0.75) FROM user_behavior GROUP BY user_segments;' Visualization: Scatter plot where each point represents a user segment and the x,y coordinates represent session duration at 25th and 75th percentiles respectively.
  • B. Query: 'SELECT user_segments, MEDIAN(session_duration) FROM user_behavior GROUP BY user_segments;' Visualization: Box plot showing the distribution (quartiles, median, outliers) of session duration for each user segment.
  • C. Query: ' SELECT COUNT( ) ,user_segments FROM user_behavior GROUP BY user_segments;' Visualization: Pie chart showing proportion of each segment.
  • D. Query: 'SELECT session_duration FROM user_behavior WHERE user_segments = 'New';- (repeated for each user segment). Visualization: Overlayed histograms showing the distribution of session duration for each user segment on the same axes.
  • E. Query: 'SELECT user_segments, AVG(session_duration) FROM user_behavior GROUP BY Visualization: Bar chart showing average session duration for each user segment.

Answer: B

Explanation:
Using the Median (option B) provides a better central tendency measure than the average (option A) when the data may have outliers. The box plot effectively visualizes the distribution, including quartiles and outliers. Option C involves generating separate queries and histograms, which is less efficient. Calculating quantiles using 'APPROX_PERCENTILE' (Option D) is good for large datasets, but the resulting scatter plot isn't the best way to show distribution. Pie chart does not show distrubution but proportions.


NEW QUESTION # 207
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 can be completed using the 'when' clause with multiple 'otherwise' clauses.
  • B. 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.
  • C. The construct in Snowpark can be used to create the 'amount_category' feature directly within the DataFrame transformation without needing a UDF
  • D. 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.
  • E. You can register SQL UDF to calculate the 'amount_category' using 'CASE WHEN' statement

Answer: A,C,E

Explanation:
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.


NEW QUESTION # 208
You are performing exploratory data analysis on a large sales dataset in Snowflake using Snowpark. The dataset contains columns such as 'order_id', , and 'profit'. You want to identify the top 5 most profitable products for each month. You have already created a Snowpark DataFrame named 'sales_df. Which of the following Snowpark operations, when combined correctly, will efficiently achieve this?

  • A. Group by and 'product_id' , aggregate 'sum(profit)' , then use partitioned by ordered by 'sum(profit) DESC'.
  • B. First, create a temporary table with aggregated monthly profit for each product using SQL. Then, use Snowpark to read the temporary table and apply a window function partitioned by ordered by 'sum(profit) DESC'.
  • C. Use 'rank()' partitioned by ordered by 'sum(profit) DESC' , after grouping by and 'product_id' , and aggregating 'sum(profity.
  • D. Use 'ntile(5)' partitioned by ordered by 'sum(profit) DESC' after grouping by and 'product_id', and aggregating 'sum(profit)'.
  • E. Group by 'product_id', aggregate 'sum(profity, then use partitioned by ordered by 'sum(profit) DESC' within a UDF.

Answer: A

Explanation:
Option A correctly describes the process. First group by month and product to calculate total profit, then use with correct partitioning and ordering to assign a rank within each month based on profit. Options B and C use less efficient ranking functions. Option D groups by product globally, missing the monthly granularity. Option E 'ntile' divides products into 5 buckets which is not what we are looking for.


NEW QUESTION # 209
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