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Snowflake SnowPro Advanced: Data Analyst Certification Sample Questions:

1. You are building a sales performance dashboard in Snowflake. You need to incorporate a custom metric called 'Sales Efficiency', which is calculated as (Total Sales Revenue / Number of Sales Representatives) 1000. You want to expose this metric in your dashboard and allow users to filter data based on specific Sales Efficiency ranges. You also want to allow filtering by region and quarter. Which of the following approaches would be MOST performant and maintainable in Snowflake?

A) Create a Snowflake view that calculates 'Sales Efficiency'. The dashboard queries filter this view using standard WHERE clauses for region and quarter. No indexes or partition keys are created.
B) Calculate 'Sales Efficiency' directly in the dashboard tool for each query using its expression engine. Implement region and quarter filtering within the dashboard tool.
C) Create a Snowflake user-defined function (IJDF) in JavaScript to calculate 'Sales Efficiency'. Use this IJDF in the dashboard queries, along with standard WHERE clauses for region and quarter filtering.
D) Create a Snowflake stored procedure that calculates 'Sales Efficiency' based on the selected Region and Quarter. The dashboard calls the stored procedure and displays the returned result set.
E) Create a materialized view in Snowflake that pre-calculates 'Sales Efficiency', region, and quarter. Include appropriate indexes and partition keys on the view. Expose the materialized view to the dashboard.

2. You are analyzing sales data for a retail company. The 'sales' table contains columns 'product id' (INT), 'sale date' (DATE), and 'sale_amount' (NUMBER). You need to calculate the percentage contribution of each product's sales to the total sales on each day. You want the result to include 'sale date', 'product_id', 'sale_amount', 'daily total' , and 'percentage_contribution'. Which of the following Snowflake queries achieves this correctly?

A) Option B
B) Option E
C) Option D
D) Option C
E) Option A

3. You are tasked with creating a data pipeline that ingests data from various sources, including a Snowflake Marketplace data share, and prepares it for analysis. The pipeline involves several transformations and enrichments. Which of the following methods offer the BEST approach to manage data lineage and auditability within this pipeline, considering the shared data from the Marketplace?

A) Use Snowflake's 'SYSTEM$GET_PREDECESSORS' and functions combined with a metadata repository to capture and visualize data lineage.
B) Rely solely on Snowflake's query history and table metadata to track data lineage.
C) Replicate the data share's tables into your own database and track changes on the replicated tables.
D) Implement a custom logging system that records each transformation step and data source, including the data share details.
E) Create a series of temporary tables at each stage of the pipeline to store intermediate results and track data lineage.

4. You have a Snowflake table named 'customer transactionS with columns: 'customer id', 'transaction date', 'transaction amount, and product_categorV. You need to identify customers who have made purchases in more than three different product categories within the last 30 days. Which of the following Snowflake SQL queries is the MOST efficient and accurate way to achieve this, considering the large size of the table?

A) Option B
B) Option E
C) Option D
D) Option C
E) Option A

5. You have a table 'PRODUCTS' with a 'PRICE' column stored as VARCHAR. Some values in this column are valid numerical strings (e.g., '12.99'), while others contain invalid characters (e.g., '12.99USD', 'N/A'). You need to calculate the average price of all valid products. Which of the following approaches ensures that you only consider valid numeric values and handles potential errors effectively? Select all that apply.

A)

B)

C)

D)

E)

Solutions: