100% PASS QUIZ 2025 SNOWFLAKE DAA-C01: TRUSTABLE EXAM SAMPLE SNOWPRO ADVANCED: DATA ANALYST CERTIFICATION EXAM ONLINE

100% Pass Quiz 2025 Snowflake DAA-C01: Trustable Exam Sample SnowPro Advanced: Data Analyst Certification Exam Online

100% Pass Quiz 2025 Snowflake DAA-C01: Trustable Exam Sample SnowPro Advanced: Data Analyst Certification Exam Online

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q253-Q258):

NEW QUESTION # 253
A financial institution needs to categorize transactions as 'High Risk', 'Medium Risk', or 'Low Risk' based on a complex set of rules involving transaction amount, location, merchant type, and customer history. You are using Snowflake to implement this classification. Which of the following approaches would provide the MOST flexible and scalable solution for defining and managing these risk classification rules, and then applying them to incoming transaction data?

  • A. Implementing the classification logic directly within a single, large SQL query using nested 'CASE' statements and complex conditional logic.
  • B. Storing the risk classification rules in a separate Snowflake table and using a dynamic SQL query to generate and execute the classification logic based on the data in the rules table.
  • C. Creating a series of Snowflake Tasks that execute SQL queries to categorize transactions based on individual rules, chaining these tasks together in a specific sequence.
  • D. Using Snowflake's Python User-Defined Functions (UDFs) to encapsulate the risk classification logic, allowing for more complex calculations and the use of external libraries if needed.
  • E. Leveraging Snowflake's external functions to call a rules engine deployed on a separate server (e.g., a Drools rules engine running on AWS EC2) to perform the risk classification.

Answer: D,E

Explanation:
Options C and E are the most flexible and scalable. Option C enables the creation of complex logic with Snowflake UDF, while option E uses external functions for connecting to separate rule engines. Option A would become unwieldy and difficult to maintain as the number of rules increases. Option B may not be suitable for real-time classification and may introduce unnecessary complexity. Option D, while offering some flexibility, can be challenging to implement and optimize for complex rules.


NEW QUESTION # 254
You have a Snowflake table 'CUSTOMER ORDERS with columns 'CUSTOMER ID', 'ORDER DATE, and 'ORDER AMOUNT. You need to calculate the cumulative sum of 'ORDER AMOUNT' for each customer, ordered by 'ORDER DATE. However, due to potential late-arriving data, you also need to implement a windowing function that resets the cumulative sum if there's a gap of more than 30 days between consecutive orders for a customer. Which SQL query best accomplishes this?

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

Answer: D

Explanation:
Option C correctly uses a conditional partitioning approach. UG(ORDER DATE, 1, ORDER DATE) OVER (PARTITION BY CUSTOMER_ID ORDER BY ORDER_DATE)' calculates the previous order date for each customer. (ORDER_DATE - 1, ORDER DATE) OVER (PARTITION BY CUSTOMER ID ORDER BY ORDER DATE) > 30)' creates a boolean expression that is true when the difference between consecutive order dates exceeds 30 days. This boolean expression is then used as a secondary partition key, effectively restarting the cumulative sum whenever a gap of more than 30 days occurs. The primary partition is still 'CUSTOMER ID' , ensuring sums are calculated within each customer's order history. The ordering of 'ORDER_DATE is essential for the cumulative sum to be calculated chronologically.


NEW QUESTION # 255
You have identified a potentially valuable dataset on the Snowflake Marketplace that provides demographic information by zip code. Your company has customer data in a table 'CUSTOMERS' with columns 'CUSTOMER ID' ONT), 'ZIP CODE' (VARCHAR), and 'PURCHASE AMOUNT (NUMBER). Before subscribing to the Marketplace data, you want to preview the data to ensure it contains the necessary information and assess its quality. What is the recommended and MOST efficient way to preview the data from the Snowflake Marketplace listing?

  • A. Utilize the 'Preview Data' tab available directly within the Snowflake Marketplace listing. This tab typically provides a sample of the data, allowing you to inspect the data structure, content, and quality before subscribing.
  • B. Use the 'Get Data' button on the Marketplace listing and specify that you only want a small subset of the data, such as data for a specific geographic region. This will create a smaller, more manageable dataset for previewing.
  • C. Contact the data provider directly and request a sample dataset to be delivered to you as a CSV file. Then upload this CSV file to an internal stage and query it to preview the data.
  • D. Subscribe to the free trial of the Snowflake Marketplace listing, create a database from the shared data, and then sample the demographic data using 'SELECT FROM .. LIMIT 100'.
  • E. Create a stored procedure that dynamically calls the Marketplace data listing API to fetch a JSON sample and parse the JSON data into a temporary table for previewing.

Answer: A

Explanation:
Option D is the most recommended and efficient way. The 'Preview Data' tab within the Snowflake Marketplace listing is designed precisely for this purpose. It allows you to inspect a sample of the data directly within the Marketplace interface, without requiring you to subscribe or incur costs. Option A requires you to subscribe to the listing, even if it's a free trial, which is an unnecessary step for simply previewing the data. Option B is inefficient and requires manual effort to obtain and load the data. Option C is not a standard feature of the Snowflake Marketplace. Option E is overly complex and inefficient for a simple preview task.


NEW QUESTION # 256
You are examining sales data for an e-commerce company in Snowflake. You notice a recurring dip in sales every Wednesday. The 'SALES' table contains columns: 'order_id', 'order date' , 'product_id', 'quantity', 'price'. Which of the following SQL queries and analytical techniques would be the MOST effective for diagnosing the reasons behind this weekly sales dip, assuming no external promotional activity always happens on Wednesdays?

  • A. Investigate shipping times by day of the week to determine if longer shipping times on orders placed earlier in the week are affecting Wednesday sales. Join ' SALES table with 'SHIPPING' table and calculate difference between 'order_date' and 'ship_date' .
  • B. Analyze sales patterns by 'product_id' and day of the week to see if specific product categories are driving the overall dip on Wednesdays. Use 'PIVOT' operator for cross-tabulation.
  • C. Run a query to calculate the average daily sales for each day of the week and visualize the results to confirm the dip on Wednesdays. Use "DAYOFWEEK' function for this.
  • D. Review recent code deployments related to the e-commerce platform and identify if any changes were implemented on Tuesdays or Wednesdays that could be impacting sales.
  • E. Check if there are any scheduled maintenance windows or system outages occurring on Wednesdays that might be affecting order processing. Use 'INFORMATION SCHEMA.LOAD HISTORY' to look for any loads failing on wednesdays

Answer: A,B,C,D

Explanation:
Options A, B, C, and E are the most relevant. Option A helps confirm the dip. Option B identifies product-specific patterns. Option C investigates a potential shipping-related cause. Option E explores potential software changes, while Option D focuses on a possible but less relevant cause of system maintenance.


NEW QUESTION # 257
You are responsible for maintaining a dashboard displaying real-time website traffic data'. The data is ingested into a Snowflake table named 'WEB EVENTS using Snowpipe from cloud storage. The 'WEB EVENTS' table includes 'EVENT TIMESTAMP' , 'PAGE URL' , and 'USER ID columns. The dashboard requires near real-time updates, but you are noticing significant latency. Which of the following actions, performed in isolation, is LEAST likely to improve the dashboard update frequency?

  • A. Refactor dashboard queries to directly query the 'WEB EVENTS' table without any aggregations or transformations.
  • B. Reduce the frequency of micro-batch data loading by increasing the Snowpipe 'AUTO_INGEST schedule to reduce the number of pipe executions per minute.
  • C. Increase the size of the Snowflake virtual warehouse used for Snowpipe data loading.
  • D. Optimize the Snowpipe configuration by adjusting the 'COPY INTO' statement to use file format options appropriate for the source data (e.g., compression, field delimiters).
  • E. Create a materialized view that pre-aggregates the data needed for the dashboard and configure it for automatic refresh.

Answer: B

Explanation:
Reducing the frequency of micro-batch data loading (option E) is LEAST likely to improve dashboard update frequency; in fact, it will decrease it. The dashboard needs near real-time updates, so reducing how often data is loaded will directly conflict with that requirement. All the other options are designed to help with optimization. Optimizing Snowpipe configuration, creating materialized views for pre-aggregation, and increasing warehouse size for Snowpipe can help, too. Reducing the amount of aggregations that dashboard needs will help since less processing is necessary.


NEW QUESTION # 258
......

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