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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q215-Q220):

NEW QUESTION # 215
You have a CSV file loaded into a Snowflake table named 'raw data'. The file contains customer order data, but some rows have missing values in the 'order date' column. You need to create a new table, 'cleaned data' , that contains only valid records and handles missing 'order date' values by substituting them with the date '1900-01-01'. Which of the following approaches is the MOST efficient and correct way to achieve this using Snowflake features?

Answer: D

Explanation:
Option E is the most efficient and correct. 'COALESCE' efficiently handles NULL replacement, and ensures the replacement value is the correct data type (DATE). It also explicitly selects all other columns. Option A only filters out rows with null order_date. Options B, C and D creates a new column, . It does not also implicitly take all columns, which would make this more appropriate.

NEW QUESTION # 216
You are tasked with building a dashboard that visualizes website traffic data stored in Snowflake. The data includes daily unique visitors, bounce rate, and average session duration. The business stakeholders want to understand the correlation between these metrics. They also want to identify any outliers or anomalies. Which chart type is BEST suited for identifying correlation and outliers in this dataset?

A. A bar chart comparing the average values of each metric.
B. A line chart showing each metric over time.
C. A histogram showing the distribution of individual Metrics.
D. A scatter plot matrix showing the pairwise relationships between all metrics.
E. A pie chart showing the percentage contribution of each metric to the total.

Answer: D

Explanation:
A scatter plot matrix displays the pairwise relationships between multiple variables. This allows for easy identification of correlations (positive, negative, or none) and outliers in the data. Line charts are good for showing trends over time, but not for directly visualizing correlations between different metrics. Bar charts compare average values, and pie charts show proportions. Histograms helps to show single distribution only.

NEW QUESTION # 217
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. Log Transformation
E. Standardization

Answer: B,C,D

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 # 218
You are building a dashboard to monitor website traffic. You have the following requirements: 1. Display the number of unique visitors per day. 2. Allow users to filter the data by device type (desktop, mobile, tablet). 3. Show a trend line of unique visitors over time. 4. The dashboard must refresh every 15 minutes with the latest data,. 5. The dashboard must be performant even with a large volume of dat a. Given the following table definition:

Which of the following approaches would be the MOST efficient and scalable solution in Snowflake? Select all that apply.

A. Create a stored procedure to calculate the number of unique visitors per day and device type. Schedule the stored procedure to run every 15 minutes and update a table. The dashboard queries this table.
B. Use a Snowflake stream to capture changes to the 'website_traffic' table. Create a task to process the stream every 15 minutes and update a summary table with the number of unique visitors per day and device type. The dashboard queries the summary table.
C. Create a standard Snowflake view that calculates the number of unique visitors per day and device type. The dashboard queries the view directly, filtering by device type. No task or stream is used.
D. Create a materialized view to pre-aggregate the number of unique visitors per day and device type. Set up a Snowflake task to refresh the materialized view every 15 minutes. The dashboard queries the materialized view.
E. Use the dashboard tool's built-in data transformation capabilities to calculate the number of unique visitors per day and device type on the fly, directly from the 'website traffic' table.

Answer: B,D

Explanation:
Materialized views (option A) and Streams with tasks (Option B) are the most efficient options for handling large datasets and real- time updates. Materialized views pre-compute the aggregates, which significantly speeds up query performance. A stream and task combination provides an incremental data processing approach, only processing new data every 15 minutes. This prevents full table scans and improves efficiency. A standard view (option C) will perform the calculation every time it's queried, leading to poor performance with large datasets. Using the dashboard tool's transformation capabilities (option D) is generally less efficient than leveraging Snowflake's compute power. Stored procedures (option E) can work but are generally less efficient than materialized views in this scenario.

NEW QUESTION # 219
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.

Answer: A,E

Explanation:
Option B is correct because it uses a regular expression to explicitly check if the PRICE column contains only valid numeric characters (digits and an optional decimal point) before attempting to cast it to a DECIMAL. 'REGEXP LIKE' ensures that non-numeric values are filtered out, preventing errors during casting, and then 'CAST is used since it is validated by the regular expression check. Option D is also correct because it uses 'CASE statement with 'IS DECIMAL' function that is a user defined function (UDF) for checking if a string can be converted to decimal, and if True, it casts the PRICE to DECIMAL; otherwise, it assigns NULL. The AVG function automatically ignores NULL values, ensuring that only valid numeric values are considered. Option A won't work as DECIMAL' function is not standard, so you will need to create user defined function. 'TO_NUMBER and 'TO_DECIMAL' throws error if it cannot parse which is not ideal. Option E, throws error if column PRICE cannot be converted, where as 'TRY TO DECIMAL' will not throw the error and return NULL.

NEW QUESTION # 220
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