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[General] DAA-C01 Reliable Exam Blueprint & DAA-C01 Latest Test Report

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【General】 DAA-C01 Reliable Exam Blueprint & DAA-C01 Latest Test Report

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q79-Q84):NEW QUESTION # 79
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. 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
  • B. 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' .
  • C. 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.
  • D. 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.
  • E. 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.
Answer: B,C,D,E
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 # 80
When conducting diagnostic analysis, why is it essential to collect related data and demographics?
  • A. To focus solely on isolated anomalies
  • B. It assists in identifying reasons/causes of anomalies in historical data
  • C. To overlook statistical trends in historical data
  • D. To restrict analysis to recent data only
Answer: B
Explanation:
Collecting related data and demographics aids in identifying reasons/causes behind anomalies in historical data.

NEW QUESTION # 81
A key aspect of performing exploratory ad-hoc analyses is:
  • A. Flexibility in querying and data exploration
  • B. Limiting data sources
  • C. Following a strict data model
  • D. Relying solely on predefined hypotheses
Answer: A

NEW QUESTION # 82
You are tasked with analyzing website clickstream data stored in a Snowflake table named 'clickstream_data'. The table contains a 'variant' column named 'event data' that holds JSON data representing various events like 'page view', 'button click', and 'form submission'. You need to extract the 'page_url' from 'page_view' events, the 'button id' from 'button click' events, and the 'form id' from 'form submission' events. Design the most efficient Snowflake SQL query to achieve this, considering the performance implications of variant parsing and filtering. Assume the 'event_type' is directly available within the variant object as well. Which of the following queries would be most efficient?

  • A. Option E
  • B. Option C
  • C. Option D
  • D. Option A
  • E. Option B
Answer: B
Explanation:
Option C is the most efficient. Snowflake's native variant access is generally faster than using 'PARSE_JSON' Also, explicit casting to VARCHAR ensures consistency in the output data type. Option A although correct will produce an output that is of variant type for all fields. Option B is incorrect because if none of the when conditions matches, then it returns NULL. Option D uses PARSE JSON unneccessarily making it slower. Option E uses GET which is used to extract data from an object within VARIANT and not to directly evaluate the VARIANT column.

NEW QUESTION # 83
You are analyzing customer order data in Snowflake. The 'orders' table has columns: and 'order_totar. Your task is to identify the top 5 customers who have consistently placed high-value orders over time. You need to rank customers based on their average order total, but only consider customers who have placed at least 10 orders. Furthermore, you want to account for the recency of orders by applying a weighted average where more recent orders contribute more to the average. Which of the following approaches will efficiently achieve this goal in Snowflake?
  • A. Use a QUALIFY clause in conjunction with window functions to filter customers with at least 10 orders and calculate both average and weighted average. Then use the ranking function over the weighted average.
  • B. Use a weighted average calculation involving a date-based weighting factor (e.g., days since the order date), calculate the average order total with this weighting, filter with COUNT( ) >= 10 using HAVING, and then rank using RANK() OVER (ORDER BY weighted_average_order_total DESC).
  • C. Calculate the average order total using AVG(), filter customers with COUNT( ) >= 10 using HAVING, then rank them using RANK() OVER (ORDER BY average_order_total DESC).
  • D. Create a stored procedure to iterate through each customer, calculate the weighted average order total, and then rank them in the application layer.
  • E. Calculate the average order total and order count for each customer using a subquery, then join the results with a generated series of dates to calculate the weighted average in the outer query, finally ranking the customers using DENSE_RANK().
Answer: A,B
Explanation:
Both options B and E correctly address the problem. B calculates a weighted average, filters based on the minimum order count, and then ranks customers based on the weighted average. E achieves the same result in using QUALIFY which is an important technique to filter. Option A doesn't account for weighting. C is inefficient and does not leverage Snowflake's processing power. D is unnecessarily complex with join, date series and subquery for simple operation that can be achieved using window functions.

NEW QUESTION # 84
......
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