100% PASS USEFUL DAA-C01 - TEST SNOWPRO ADVANCED: DATA ANALYST CERTIFICATION EXAM QUESTIONS VCE

100% Pass Useful DAA-C01 - Test SnowPro Advanced: Data Analyst Certification Exam Questions Vce

100% Pass Useful DAA-C01 - Test SnowPro Advanced: Data Analyst Certification Exam Questions Vce

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q291-Q296):

NEW QUESTION # 291
A financial institution uses Snowflake to store transaction data'. They observe an unexpected spike in fraudulent transactions during a specific weekend. The 'TRANSACTIONS table contains columns like 'transaction id', 'transaction date', 'amount', 'merchant_category', 'user_id', and 'is_fraudulent. Which of the following approaches, leveraging Snowflake's capabilities, would be MOST effective in diagnosing the potential causes of this fraud spike?

  • A. Join the 'TRANSACTIONS table with a table containing external threat intelligence data (e.g., IP address reputation, known fraudulent merchants) to identify transactions originating from suspicious sources.
  • B. Analyze transaction patterns by 'merchant_category' and 'user_id' before, during, and after the spike to identify any unusual concentrations or emerging fraudulent schemes. Consider using 'QUALIFY' clause to filter top N merchant_category by transaction amount.
  • C. Create cohorts of users based on transaction behavior before the spike and compare their transaction patterns during the spike to identify anomalies within specific user groups. Use window functions like 'FIRST_VALUE to define cohorts.
  • D. Use a machine learning model (deployed as a Snowflake user-defined function) to re-score all transactions from the affected weekend based on historical patterns and identify potentially misclassified transactions.
  • E. Only check the transaction details, like amount and the users involved, and assume one of the users triggered those Fraud Trasanctions.

Answer: A,B,C,D

Explanation:
Options A, B, C, and D provide a robust diagnostic approach. A uses machine learning for anomaly detection, B analyzes patterns by merchant category and user, C incorporates external threat intelligence, and D examines cohort behavior. Only checking specific transaction is not effective.


NEW QUESTION # 292
You are designing a data warehouse in Snowflake for a retail company. The company has two tables: 'Transactions' (TransactionlD, CustomerlD, ProductlD, TransactionDate, Amount) and 'Products' (ProductlD, ProductName, CategorylD, Price). The 'Transactions' table contains millions of rows, and analysts frequently run queries that join these tables to analyze sales by product category. To optimize query performance and reduce data redundancy for these analytical queries, which of the following strategies would be MOST effective, considering Snowflake's architecture and best practices?

  • A. Create a materialized view that joins the 'Transactions' and 'Products' tables and includes relevant columns for analysis, partitioning the view by 'TransactionDates.
  • B. Create a clustered table that includes all columns in transactions and products tables using 'CLUSTER BY' clause to cluster most frequently used column.
  • C. Denormalize the 'Transactions' table by adding 'ProductName' and 'CategorylD columns directly to the 'Transactions' table using a data transformation pipeline after the initial load.
  • D. Create a stored procedure that automatically rebuilds the standard view daily using the latest data from the 'Transactions' and 'Products' tables.
  • E. Create a standard view that joins the 'Transactions' and 'Products' tables, and rely on Snowflake's query optimizer to automatically optimize the join performance.

Answer: A,C

Explanation:
Options A and D are correct. Creating a materialized view (option A) is a good approach if the underlying tables don't change often. Snowflake will automatically refresh the materialized view when changes occur in the base tables. In this case, the company will have increased query performance. Partitioning by TransactionDate also improves the query's performance. Materialized views store the pre-computed results, significantly speeding up queries that use the view. Denormalizing (option D) by adding 'ProductName' and 'CategorylD' to the 'Transactions' table avoids the join entirely for queries that only need these columns, improving performance and reducing data redundancy. A standard view (option B) doesn't store pre-computed results. While Snowflake's optimizer is effective, it won't provide the same performance gains as a materialized view or denormalization. Option C is not correct because creating a clustered table does not avoid the need to join the tables. Option E is not correct because using the materialized view, the Snowflake will automatically refresh it when changes occur.


NEW QUESTION # 293
You're building a Snowflake forecasting model to predict website traffic. Your dataset contains 'VISIT DATE (DATE), 'PAGE VIEWS (NUMBER), and 'PROMOTION FLAG' (BOOLEAN, indicating whether a promotion was active that day). You suspect that promotional periods significantly impact traffic, but need to account for days after a promotion that show residual impact. Which of the following strategies can you employ to improve your forecasting model to handle promotion and their lagging effects. Select two correct options.

  • A. Remove the 'PROMOTION FLAG' column entirely, as promotions introduce too much noise in the data and make accurate forecasting impossible.
  • B. Use the 'HOLIDAY_DETECTION' parameter in the model creation statement. Snowflake will automatically detect promotions as holidays and incorporate them into the forecast.
  • C. Create multiple lagged features for 'PROMOTION FLAG'. For example, 'PROMOTION FLAG LAGI' would be the 'PROMOTION FLAG' value from the previous day, from two days ago, and so on. Include these lagged features in the model's INPUT.
  • D. Use a simple moving average on the 'PAGE VIEWS' column over a 7-day period, ignoring the 'PROMOTION FLAG' entirely, as Snowflake's forecasting will automatically learn the promotional effects through the averaged data.
  • E. Create a new feature called 'DAYS SINCE PROMOTION' that calculates the number of days since the last promotion. Include this feature in the model's INPUT.

Answer: C,E

Explanation:
Options A and C are correct. Option A helps the model directly capture the time elapsed since a promotion, allowing it to learn the decaying effect. Option C captures the lagged effects of promotions by including ' PROMOTION_FLAG' values from previous days as separate features. Option B is incorrect because simple moving average is a bad approach that may not be able to learn complex patterns of promotion effects on forecasting data, moreover promotional periods will be ignored. Option D is incorrect as promotions are valuable signals, not noise. Option E is incorrect because Snowflake's 'HOLIDAY DETECTION' feature automatically deals with typical public holidays, not self defined promotional campaigns.


NEW QUESTION # 294
You're using Snowsight to build a dashboard for monitoring website performance. The data is in a table called 'WEB EVENTS' with columns: 'EVENT _ TIME' (TIMESTAMP_NTZ), 'EVENT _ TYPE' (VARCHAR, e.g., 'page_view', 'button_click'), 'USER_ID' (VARCHAR), and 'PAGE URL' (VARCHAR). You want to create a tile that shows the average time between consecutive 'page_view' events for each user over the last 7 days. This will help you understand how users are navigating the site. Assume that for a single user, page_view events are ordered by EVENT TIME. Which of the following SQL queries, when used as the basis for a Snowsight tile, will correctly calculate this average time difference in seconds?

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

Answer: D

Explanation:
It uses the window function to get the previous event time for each user, then calculates the difference between consecutive event times in seconds using 'TIMESTAMP_DIFF. The outer query then averages these differences for each user. The 'WHERE PREVIOUS_EVENT_TIME IS NOT NULL' clause is important to exclude the first event for each user, which would have a null previous event time. Option B attempts to subtract timestamps directly, which is not the correct way to get the difference in seconds in Snowflake. Option C uses 'DATEDIFF which has the parameters in the wrong order compared to the logic of the question. Option D incorrectly uses FIRST _ VALUE. Option E omits the subquery necessary to correctly use the LAG function.


NEW QUESTION # 295
You are working with a dataset containing customer orders and their associated products. The data is stored in two tables: 'ORDERS' (order_id, customer_id, order_date) and 'ORDER ITEMS' (order_id, product_id, quantity, price). The Bl team needs to analyze the top- selling products and the average order value per customer. However, due to data quality issues, some 'order _ id' values in the 'ORDER ITEMS table do not exist in the 'ORDERS' table, leading to data inconsistencies. What is the most robust and efficient way to handle this data inconsistency issue while ensuring accurate reporting?

  • A. Use a UNION ALL statement to combine the two tables, creating a single table with all order and item information. Handle inconsistencies during data extraction and loading (ETL) processes.
  • B. Create a flattened view that combines 'ORDERS' and 'ORDER_ITEMS' using a FULL OUTER JOIN. Handle missing values in the reporting layer.
  • C. Use an INNER JOIN between 'ORDERS' and 'ORDER ITEMS'. Before joining, use a subquery or CTE to filter 'ORDER ITEMS' to include only 'order_id' values that exist in 'ORDERS'. After joining apply a filter for valid prices(Price > 0). This ensures correct analysis and avoids invalid orders. Select all the rows with valid prices (Price > 0).
  • D. Use a RIGHT JOIN from 'ORDERS' to 'ORDER ITEMS' and filter out rows where order details are missing.
  • E. Use a LEFT JOIN from 'ORDERS' to 'ORDER ITEMS to include all orders, even if they don't have corresponding items. Filter out rows with NULL 'product_id' in the final result.

Answer: C

Explanation:
Option B provides the most robust and efficient solution. By using an INNER JOIN after filtering the 'ORDER_ITEMS' table to include only valid 'order_id' values (those present in the 'ORDERS' table), you ensure that the analysis is based on consistent and reliable data. The subquery/CTE effectively cleans the data before joining, preventing inaccurate calculations. Filtering for valid prices ensures data quality. Option A would include orders without item details, which is not desired for this analysis. Options C and D might result in inaccurate results due to the incomplete relationship between orders and products. Option E is not suitable since it mixes the distinct column sets, resulting in inaccurate analyses. Always ensure referential integrity where business logic dictates.


NEW QUESTION # 296
......

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