100% Pass Quiz 2025 Professional Snowflake DAA-C01: SnowPro Advanced: Data Analyst Certification Exam Valid Test Sims

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q109-Q114):

NEW QUESTION # 109
You are tasked with identifying potential fraud in a financial transactions table named 'transactions'. The table includes 'transaction id' , account id', 'transaction date', and 'transaction_amount'. You need to flag transactions where the transaction amount is significantly higher than the average transaction amount for that account within the last 7 days, specifically, three standard deviations higher. Which analytic function or combination of functions would best accomplish this, while optimizing for performance? (Select all that apply)

A. Calculate the average and standard deviation of transaction amounts for each account using 'AVG()' and window functions over a 7-day window, and then use a 'CASE' statement to flag transactions that exceed the threshold (average + 3 standard deviation).
B. Utilize the ' NTILE()' window function to divide transactions into quantiles based on amount within each account's 7-day window. Flag transactions falling into the highest quantile as potentially fraudulent.
C. Calculate the median transaction amount using the 'MEDIAN()' window function over a 7-day window. Flag transactions where the amount exceeds the median by a predefined percentage (e.g., 200%).
D. Use a user-defined function (UDF) to iterate through each account's transactions, calculate the average and standard deviation for the last 7 days, and flag transactions exceeding the threshold.
E. Calculate the average transaction amount for each account over the entire dataset. Flag transactions exceeding three times this global average.

Answer: A

Explanation:
Option A is the best approach for accurately identifying outliers using statistical measures. Calculating both the average ('AVG()') and standard deviation using window functions over a 7-day window allows for a dynamic threshold that adapts to each account's transaction behavior. Combining this with a 'CASE statement allows you to flag those transactions which are above three standard deviations from the average. Option B is less efficient than using Snowflake's built-in analytic functions. Options C, D, and E do not correctly identify outliers based on the specified criteria. The MEDIAN can hide many outliers, and the total dataset average and NTILE are less accurate.

NEW QUESTION # 110
Consider a table 'customer_orderS with columns 'order_id' (INT), 'customer_id' (INT), 'order_date' (DATE), and 'order_total' (NUMBER). The table is partitioned by 'order_date'. You need to create a materialized view that summarizes the total order value per customer, per month. Which of the following materialized view definitions will both achieve the desired summarization and effectively leverage partition pruning for efficient refreshes?

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

Answer: C

Explanation:
Option A is the best choice. It groups by and 'DATE TRUNC('month', order_datey , which creates a monthly aggregation. Since the base table is partitioned by 'order_date' , the materialized view refresh can efficiently use partition pruning based on the monthly date truncations. Option B groups by the full 'order_date' , leading to daily aggregations instead of monthly. Option C uses , which is less efficient and doesn't preserve the year information for partition pruning. Option D uses "CONVERT_TIMEZONE' , which is incorrect for the purpose and will not efficiently leverage partition pruning.Option E using CAST(order_date AS VARCHAR(7)) as it change the date data type.

NEW QUESTION # 111
You have a table named 'sales_data' with a column 'product_details' of type VARIANT containing JSON data for various products. The JSON structure is inconsistent; some products have a 'size' attribute as a string, while others have it as an integer, and some don't have the attribute at all. You need to extract the 'size' as a consistent numeric value (NULL if it's missing) for analysis. Which SQL statement using table functions and data type conversion techniques correctly and efficiently handles this data inconsistency?

Answer: E

Explanation:
TRY_TO_NUMBER() is the most robust and concise way to handle potential data type inconsistencies and missing values when extracting data from VARIANT columns. It attempts to convert the value to a NUMBER and returns NULL if the conversion fails. This elegantly handles both string and integer representations of 'size', as well as cases where 'size' is missing from the JSON. Using Case statements or IS_NUMBER requires more verbose logic and can be less efficient than using the built-in function. NVL replaces NULL with 0, which isn't suitable as the question asks for NULL if missing.

NEW QUESTION # 112
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. Create a materialized view that pre-aggregates the data needed for the dashboard and configure it for automatic refresh.
C. Refactor dashboard queries to directly query the 'WEB EVENTS' table without any aggregations or transformations.
D. Reduce the frequency of micro-batch data loading by increasing the Snowpipe 'AUTO_INGEST schedule to reduce the number of pipe executions per minute.
E. 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).

Answer: D

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 # 113
You are tasked with creating a dashboard to visualize sales performance across different product categories and regions. The data is stored in a Snowflake table named with columns: 'SALE DATE (DATE), 'PRODUCT CATEGORY (VARCHAR), 'REGION' (VARCHAR), 'SALES_AMOUNT (NUMBER). The business stakeholders want to see a trend of monthly sales for the past year, a breakdown of sales by region, and a comparison of sales between product categories. Which of the following approaches would be MOST effective and efficient in Snowflake for generating the data needed for these visualizations, considering the need for dashboard responsiveness and minimal query cost?

A. Create multiple materialized views, each specifically designed for one of the dashboard visualizations (monthly sales trend, sales by region, sales by product category). Refresh these materialized views regularly.
B. Create a data pipeline that transforms the raw 'SALES_DATX into summary tables using tasks and streams. These summary tables are optimized for the specific queries required by the dashboard visualizations.
C. Export the data to an external data warehouse for visualization purpose to avoid overhead of Snowflake visualization tool.
D. Create a single, complex SQL query that joins the 'SALES DATA' table with itself multiple times, using window functions and subqueries to calculate all the required aggregations and breakdowns within the query. The dashboard will directly query this view.
E. Use Snowflake's caching mechanism extensively to cache the results of individual queries within the dashboard, ensuring that subsequent requests for the same data are served from the cache. Rely on the dashboard's internal caching capabilities.

Answer: A,B

Explanation:
Creating multiple materialized views (B) allows for pre-calculated aggregations tailored to each visualization, significantly improving dashboard responsiveness and reducing query costs. Using tasks and streams to transform the raw data into optimized summary tables (D) also pre-processes the data, providing similar benefits to materialized views, but allows for more complex transformations and incremental updates. A single complex query (A) can be slow and resource-intensive. Relying solely on caching (C) might not be sufficient for complex aggregations or large datasets. Exporting data to a completely new system adds overhead and is unnecessary given Snowflake's capabilities.

NEW QUESTION # 114
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100% Pass Quiz 2025 Professional Snowflake DAA-C01: SnowPro Advanced: Data Analyst Certification Exam Valid Test Sims

DAA-C01 Exam Flashcards, Valid Study DAA-C01 Questions

Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q109-Q114):

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