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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. Increase the size of the Snowflake virtual warehouse used for Snowpipe data loading.
B. 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).
C. Refactor dashboard queries to directly query the 'WEB EVENTS' table without any aggregations or transformations.
D. Create a materialized view that pre-aggregates the data needed for the dashboard and configure it for automatic refresh.
E. Reduce the frequency of micro-batch data loading by increasing the Snowpipe 'AUTO_INGEST schedule to reduce the number of pipe executions per minute.
Answer: E
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 # 84
When customizing data presentations in dashboards using filtering and editing techniques, what advantages do these methods offer? (Select all that apply)
A. Limited data exploration
B. Simplified data representation
C. Enhanced data accuracy
D. Improved data relevancy
Answer: C,D
Explanation:
Customizing through filtering and editing enhances data accuracy and relevancy in dashboards.
NEW QUESTION # 85
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 C
D. Option A
E. Option E
Answer: C
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 # 86
A data analyst needs to ingest data from various sources into Snowflake, cleanse it, and load it into target tables. Which of the following actions are MOST crucial for ensuring data quality and consistency during the ingestion and preparation phases?
A. Implementing data profiling techniques to identify data quality issues and inconsistencies before loading data into target tables and creating detailed data dictionaries.
B. Enforcing data type constraints on Snowflake table columns.
C. Storing all data in a single large table to avoid data silos.
D. Using Snowflake's data masking policies to protect sensitive data during ingestion.
E. Implementing data validation checks using Snowflake user-defined functions (UDFs) or stored procedures to detect and handle invalid data.
Answer: A,B,E
Explanation:
Options A, B, and E are most crucial for data quality and consistency. Enforcing data type constraints (A) ensures that only data of the correct type is loaded into the tables. Implementing data validation checks (B) using UDFs or stored procedures allows for detecting and handling invalid data, preventing it from corrupting the data warehouse. Data profiling (E) helps to understand data quality issues before the load and guides data cleansing efforts. Storing all data in a single table (C) is generally not recommended as it can lead to performance issues and make it harder to manage data. While data masking (D) is important for data security, it is not directly related to data quality and consistency during ingestion and preparation. Data dictionaries promote data quality and usability.
NEW QUESTION # 87
You have a 'PRODUCT SALES table with columns 'PRODUCT ID, 'SALE DATE, 'UNITS SOLD, and 'PRICE PER UNIT. You need to perform a complex data transformation for reporting purposes. Specifically, you need to calculate the 7-day moving average of the total revenue CUNITS SOLD 'PRICE PER UNIT) for each product. However, you also need to handle cases where data might be missing for some dates. For those missing dates within a 7-day window, you want to impute a zero value for 'UNITS SOLD before calculating the moving average. Which of the following approaches is MOST efficient and accurate in Snowflake to achieve this, assuming you want to minimize code complexity and execution time? (Choose all that apply)
A. Using Snowflake's Time Travel feature to retrieve historical data and fill in the gaps before calculating the moving average.
B. Directly using a window function with the 'AVG()' aggregate function, without handling missing dates.
C. Using a series of Common Table Expressions (CTEs) to generate a date series, left join with the table, impute missing values with zero, and then apply a window function for the moving average calculation.
D. Creating a stored procedure to loop through each product and calculate the moving average with imputation using procedural logic.
E. Using a User-Defined Function (UDF) written in Python to calculate the moving average with imputation.
Answer: C
Explanation:
Option B (using CTEs and a left join) is the most efficient and accurate approach. Here's why: CTEs provide modularity: They break down the complex transformation into logical steps, making the code easier to read and maintain. Date series generation: A CTE can generate a complete date series within the relevant range. This ensures that all dates within the 7-day window are considered. Left Join and Imputation: Left joining the generated date series with the 'PRODUCT _ SALES' table allows you to identify missing dates. Then, you can use 'COALESCE or similar functions to impute zero values for 'UNITS_SOLD' for these missing dates. Window Function: Finally, you can apply the 'AVG()' window function over the revenue (UNITS_SOLD PRICE_PER_UNIT), ordered by date, to calculate the 7-day moving average. Here's why the other options are less suitable: A (UDF): While UDFs are powerful, they can introduce performance overhead compared to native SQL operations. Imputing missing values and calculating a moving average are tasks that can be efficiently handled with SQL. C (Direct Window Function): This is incorrect as this won't handle missing dates and will lead to inaccurate moving averages. D (Time Travel): Time Travel is useful for data recovery and auditing, not for imputing missing data in a moving average calculation. E (Stored Procedure): Stored procedures with procedural logic are generally less efficient than set-based SQL operations, especially in Snowflake's columnar architecture. They also increase code complexity.
NEW QUESTION # 88
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