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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q56-Q61):

NEW QUESTION # 56
A data analyst is performing an exploratory analysis on sales data and observes a highly skewed distribution for the 'sales_amount' column. Which of the following data transformation techniques are appropriate to mitigate the impact of the skewness and make the data more suitable for modeling and analysis? (Select all that apply)

A. Min-Max Scaling
B. Winsorization
C. Box-Cox Transformation
D. Standardization
E. Log Transformation

Answer: B,C,E

Explanation:
Log transformation and Box-Cox transformation are common techniques used to reduce skewness in data. Winsorization helps to reduce the impact of outliers, which can contribute to skewness. Standardization and Min-Max scaling are primarily used for feature scaling and do not directly address skewness.

NEW QUESTION # 57
What role does operationalizing data play in maintaining reports and dashboards for business requirements?

A. It restricts data accessibility, affecting dashboard usability.
B. It limits data updates, affecting dashboard accuracy.
C. Operationalizing data ensures consistent and efficient usage.
D. Operationalizing data complicates dashboard management.

Answer: C

Explanation:
Operationalizing data ensures consistent and efficient usage of reports and dashboards.

NEW QUESTION # 58
You have a Snowflake table named 'LOG DATA' with a "VARIANT' column called 'log_message" storing application logs in JSON format. The 'log_message' structure contains a nested JSON object called 'context' , which may or may not be present in every log entry. The 'context' object itself contains several key-value pairs, including potentially a 'user_id', a 'session_id' , and/or a 'request_id' You need to extract all the unique combination of 'user _ id' , and 'request_id' that exist across the dataset, accounting for instances where the 'context' object or any of the individual IDs are missing (represented as NULL). The goal is to ensure no errors are thrown due to missing fields. Select all the options which would achieve this outcome.

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

Answer: C,E

Explanation:
Options B and E are both valid solutions. Option B Uses 'TRY TO_STRING' function. If value is not present and is NULL, then automatically it will be converted to Null without any failure. Option E Uses 'COALESCE will handle any missing 'context object or missing IDs within the 'context' object without throwing an error. If any of the three values are NULL then it would be converted to string 'NULL'. Why other options are not entirely correct Option A This is not a correct answer, as a result in case NULL values encountered, this will result in failures. Option C & D This is a valid solution, however, since NVL and IFF functions are slower compared to TRY _ TO STRING and COALESCE, this result in slow processing. So, its not entirely correct answer.

NEW QUESTION # 59
You're tasked with building a data model in Snowflake for a retail company. The company has data on products ('PRODUCTS), sales transactions ('SALES), and customer demographics ('CUSTOMERS). You need to design a star schema to support efficient analysis of sales performance by product category and customer segment. Which of the following statements accurately describes the recommended table design and relationships within the star schema for this scenario?

A. Design a fact table CSALES_FACT) with foreign keys to dimension tables for 'PRODUCTS', 'CUSTOMERS, and a DATE' dimension. The 'SALES_FACT table should contain measures such as 'SALES AMOUNT and 'QUANTITY SOLD.
B. Use a snowflake schema design, where the dimension tables (e.g., 'PRODUCTS', 'CUSTOMERS) are further normalized into related tables to reduce data redundancy.
C. create separate fact tables for each product category (e.g., and link them to the 'CUSTOMERS' dimension table.
D. Maintain the original normalized tables ('PRODUCTS', 'SALES', 'CUSTOMERS') and create views that join these tables as needed for reporting purposes.
E. Create a single, denormalized table containing all product, sales, and customer information to maximize query performance.

Answer: A,B

Explanation:
Option B accurately describes a star schema design with a central fact table (SALES FACT) and dimension tables for products and customers. The fact table contains the numerical measures, and the dimension tables provide the context for analysis. Option D correctly describes Snowflake design, which is an extension of Star schema. Option A is generally not recommended because it leads to data redundancy and potential inconsistencies. Option C is not scalable or maintainable as the product range expands. Option E would result in slower query performance compared to a star schema design.

NEW QUESTION # 60
You have a stored procedure written in Python within Snowflake that needs to process a large dataset. The procedure's performance is critical, and you want to optimize it for speed. Which of the following strategies would be MOST effective in improving the performance of your Python stored procedure when dealing with large datasets in Snowflake? (Select TWO)

A. Increasing the warehouse size for the Snowflake account where the stored procedure is running. A larger warehouse provides more compute resources, which can speed up data processing tasks, including Python stored procedures.
B. Using the Snowflake Python Connector's 'execute_stream' method to stream data in chunks instead of loading the entire dataset into memory at once. This reduces memory consumption and improves performance, especially for very large datasets.
C. Replacing all Python loops with recursive SQL queries to improve performance
D. Converting the Python stored procedure to a SQL stored procedure, as SQL is generally faster for data manipulation tasks within Snowflake due to its optimized query engine.
E. Leveraging Pandas DataFrames within the Python stored procedure to perform data manipulation tasks in a vectorized manner. Pandas provides efficient data structures and functions for data processing, which can significantly improve performance compared to using standard Python loops.

Answer: B,E

Explanation:
Options A and C are the most effective strategies. Option A, 'execute_stream' , is ideal for large datasets as it avoids loading everything into memory. Option C, leveraging Pandas DataFrames for vectorized operations, significantly speeds up data manipulation within Python. Option B is partially correct. SQL is often faster, but rewriting a complex Python procedure in SQL can be time-consuming and may not always be feasible or optimal. Option D, increasing the warehouse size, can help, but it's more of a general performance improvement rather than a specific optimization for Python stored procedures. Also it will cost more. E is incorrect as recursive SQL queries, while sometimes useful, aren't a general-purpose replacement for Python loops and can be less efficient for complex logic.

NEW QUESTION # 61
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