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Snowflake DAA-C01 Exam Syllabus Topics:

Section	Objectives
Topic 1: Data Modeling and Performance Optimization	- Performance tuning 1. Clustering and pruning techniques 2. Warehouse sizing and auto-suspend/auto-resume - Modeling approaches in Snowflake 1. Star and snowflake schemas 2. Data normalization vs denormalization
Topic 2: Snowflake Architecture and Data Platform Fundamentals	- Data platform fundamentals 1. Data lifecycle in Snowflake 2. Separation of storage and compute - Snowflake architecture concepts 1. Cloud services layer, compute layer, storage layer 2. Virtual warehouses and scaling
Topic 3: Data Loading and Unloading	- Data ingestion methods 1. COPY INTO and bulk loading 2. Continuous ingestion and Snowpipe concepts - Data export 1. UNLOAD and external stages
Topic 4: Security, Governance, and Data Sharing	- Data sharing and governance 1. Secure data sharing 2. Data masking and policies - Access control and security 1. Authentication and encryption concepts 2. Role-based access control (RBAC)
Topic 5: Data Transformation and Analysis	- SQL-based transformations 1. Joins, aggregations, window functions 2. Semi-structured data (VARIANT, JSON, XML) - Analytical workloads 1. Query optimization for analytics 2. Materialized views and caching

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q59-Q64):

NEW QUESTION # 59
You have a table named 'product_sales' with columns 'product_id', 'sale date' , and 'sales_amount'. You need to calculate a 7-day moving average of the sales amount for each product. However, you also need to handle cases where there might be missing 'sale_date' entries for a product. How can you best achieve this using Snowflake window functions and without creating temporary tables or stored procedures?

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

Answer: A

Explanation:
Option B is the most appropriate. using 'RANGE BETWEEN INTERVAL '6 day' PRECEDING AND CURRENT ROW' will calculate the average of sales amounts for the previous 6 days, regardless of whether there are missing days in the 'sale_date' column. This provides a true 7-day moving average. 'ROWS BETWEEN' (Option A) would count rows, not days, potentially including data from more than 7 days if there are multiple sales on a single day, or less than 7 days if there are missing days. 'EXCLUDE CURRENT ROW' (Option C) would exclude the current row's sales amount from the average. 'RANGE BETWEEN 6 PRECEDING AND CURRENT ROW' (Option D) would not be valid Snowflake syntax for dates. Option E will calculate cumulative average, also that WHERE clause will lead to error.

NEW QUESTION # 60
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. Box-Cox Transformation
C. Winsorization
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 # 61
Consider a table 'sales data' with columns 'product id', 'sale date', and 'revenue'. You need to calculate the cumulative revenue for each product over time, but only for the top 10 products by total revenue. What is the most efficient way to achieve this in Snowflake?

A. Create a temporary table to store the total revenue for each product, then select the top 10 from the temporary table. Join this result with 'sales_data' and apply ' SUM(revenue) OVER (PARTITION BY product_id ORDER BY sale_datey for the cumulative revenue calculation.
B. Use 'SUM(revenue) OVER (PARTITION BY product_id ORDER BY sale_datey to calculate the cumulative revenue for all products, then filter the results to include only the top 10 products based on their final cumulative revenue.
C. Use the aggregation to identify top 10 'product_id' and calculate the cumilative revenue using SUM(revenue) OVER (PARTITION BY product_id ORDER BY sale_date)'.
D. Use a window function to rank products by total revenue, then filter for the top 10 ranks using a 'QUALIFY' clause. Finally, calculate cumulative revenue using 'SUM(revenue) OVER (PARTITION BY product_id ORDER BY sale_datey.
E. Use a subquery to find the top 10 'product_id' based on total revenue, then join this subquery with 'sales_data' and use 'SUM(revenue) OVER (PARTITION BY product_id ORDER BY sale_date)' to calculate the cumulative revenue.

Answer: D

Explanation:
Option B is the most efficient. and 'QUALIFY allow filtering and ranking within a single query, and efficiently calculates the cumulative revenue. Option A requires a join which can be less performant. Option C involves creating a temporary table which adds overhead. Option D calculates cumulative revenue for all products before filtering, which is unnecessary work. Option E could be considered, however APPROX TOP_N is approximate and the requirement asks for an exact calculation.

NEW QUESTION # 62
Your company needs to comply with GDPR regulations, and you're tasked with implementing data masking to protect sensitive customer information. Specifically, you need to mask email addresses in a 'CUSTOMERS' table during data preparation. Which of the following approaches, utilizing Snowflake's system functions and data masking capabilities, offers the MOST secure and flexible solution, while allowing authorized users to potentially unmask the data if necessary?

A. Create a masking policy using the function to redact portions of the email address. Apply this policy to the EMAIL_ADDRESS column in the CUSTOMERS table.
B. Use the 'SHA2()' function with a salt to hash the email addresses. Authorized users can reverse the hash using the correct salt.
C. Create a view that uses 'REGEXP REPLACE()' function to redact portions of the email address. Grant access to the view instead of the table.
D. Create a masking policy that uses 'CASE statement based on IS_ROLE_IN_SESSION() function. When the role is Analyst role it should display original value, if not then it must display null value. Apply this policy to the EMAIL ADDRESS column in the CUSTOMERS table.
E. Use the 'MD5()' function to encrypt the email addresses. Authorized users can decrypt the MD5 hash using the appropriate key.

Answer: D

Explanation:
Option C provides the best combination of security and flexibility. Snowflake's masking policies, combined with functions like , allow for dynamic masking based on user roles. Authorized users (e.g., those in an 'analyst' role) can see the original email address, while others see a masked version. Options A and D are not recommended as hashing/encryption functions like SHA2() and MD5() are not easily reversible and are not the intended use case for data masking policies. Option B offers masking with REGEXP_REPLACE but no mechanism to unmask with proper permissions. Option E creates a view, which is not centrally managed or scalable solution.

NEW QUESTION # 63
How do Stored Procedures contribute to the efficiency of data analysis using SQL?

A. Stored Procedures enable the execution of repetitive tasks, enhancing efficiency.
B. Stored Procedures can't be used in conjunction with User-Defined Functions (UDFs).
C. They solely facilitate basic arithmetic operations.
D. They limit data accessibility, hindering analysis.

Answer: A

Explanation:
Stored Procedures aid in data analysis by enabling the execution of repetitive tasks, thereby enhancing efficiency.

NEW QUESTION # 64
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