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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q19-Q24):

NEW QUESTION # 19
How do materialized views differ from secure views in data analysis?

Answer: B

Explanation:
Secure views offer enhanced data security without precomputing data, distinguishing them from materialized views.


NEW QUESTION # 20
A company stores sensor data, including timestamps (ts), sensor ID (sensor_id), and readings (reading_value), in a Snowflake table named 'sensor_data'. Due to sensor malfunctions, some readings are significantly higher or lower than expected (outliers). Which of the following approaches are suitable in Snowflake to calculate the average reading value for each sensor, EXCLUDING readings that fall outside of two standard deviations from the mean for that sensor?

Answer: A,B,D

Explanation:
Options A, B and C provides valid ways to determine outliers. A is based on direct filtering based on standard deviation on the original table using window function. B uses Sub query approach and filtering. C allows to use QUALIFY clause with window functions for filtering before aggregation. D attempts to filter groups based on range which is not the intent of the original question to filter on a per reading basis if its an outlier or not. Option E, although technically possible, introduces significant complexity and performance overhead with the use of UDF and array manipulation for a task achievable with standard SQL.


NEW QUESTION # 21
A data analyst is tasked with optimizing a query that aggregates data from a table 'ORDERS' containing order details, including columns like 'ORDER ID', 'CUSTOMER ID, 'ORDER DATE, 'PRODUCT ID', and 'QUANTITY. The query calculates the total quantity of products ordered per customer and month. The current query is as follows: SELECT CUSTOMER ID, DATE TRUNC('MONTH', ORDER DATE) AS ORDER MONTH, SUM(QUANTITY) AS TOTAL QUANTITY FROM ORDERS GROUP BY CUSTOMER_ID, ORDER_MONTH ORDER BY CljSTOMER_lD, ORDER_MONTH; Deopite the 'ORDERS' table being relatively small (10 million rows), the query performance is slow. The analyst suspects a poorly chosen warehouse size. Which of the following actions, combined with monitoring query execution, would be MOST beneficial to determine the optimal warehouse size and improve query performance?

Answer: C

Explanation:
The most beneficial approach is to start with the smallest warehouse size and incrementally increase it (B). This allows for observing the impact of warehouse size on query performance and cloud services usage. Increasing until the query time plateaus or cloud services usage increases significantly indicates the point of diminishing returns. Simply using the largest size (A) may be wasteful, and ignoring cloud services usage (C) can lead to cost overruns. Query history (D) may not be relevant if the query is significantly different. Setting a timeout (E) will not optimize the warehouse size.


NEW QUESTION # 22
You are designing a data warehouse for a retail company. The "SALES table stores transaction data and includes columns like 'TRANSACTION ID', 'PRODUCT ID', 'CUSTOMER ID, 'SALE DATE', and 'SALE AMOUNT'. The 'PRODUCT ID' references the 'PRODUCTS table, and 'CUSTOMER references the 'CUSTOMERS' table. Which of the following strategies represent the MOST optimal approach to define primary keys in this scenario, considering Snowflake's best practices and the need for efficient query performance, assuming 'TRANSACTION ID' is globally unique?

Answer: D

Explanation:
While Snowflake does not enforce primary key constraints, defining them provides valuable metadata for the query optimizer. Since TRANSACTION_ID' is unique in 'SALES', it is a suitable primary key. Defining primary keys on 'PRODUCTS' and 'CUSTOMERS' is also appropriate for their respective tables. Creating unique indexes is redundant if primary keys are defined. Skipping primary key definitions entirely can hinder optimization. A composite key in 'SALES' is unnecessary as 'TRANSACTION_ID is already globally unique.


NEW QUESTION # 23
In Snowflake, how does Time Travel feature assist in data retrieval and analysis?

Answer: A

Explanation:
The Time Travel feature allows querying data as of specific timestamps, enabling historical data retrieval and analysis at various points in time.


NEW QUESTION # 24
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