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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q174-Q179):

NEW QUESTION # 174
You have a table 'CUSTOMER DATA' with a column 'phone_number" (VARCHAR) that contains phone numbers in various formats (e.g., '123-456-7890', '1234567890', '+11234567890'). You need to standardize the phone numbers to a format of '1234567890' (no hyphens or country code). Which Snowflake SQL statement, using scalar string functions, will achieve this standardization while gracefully handling potentially invalid phone numbers (e.g., too short or containing letters) by returning NULL for invalid entries?

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

Answer: E

Explanation:
Option C is the correct answer. It first uses 'REGEXP REPLACE(phone_number, '[AO-91+', to remove all non-numeric characters from the phone number. Then, it uses a 'CASE statement to check if the resulting string has a length of 10 (a valid phone number length after standardization). If it does, the standardized phone number is returned; otherwise, NULL is returned. Option A only removes non-numeric characters but doesn't handle invalid lengths. Option B and D is doing same ,using 'IFF and adds unnecessary complexity by using 'IS NUMERIC' which is redundant since 'REGEXP REPLACE ensures only numbers exist. Option E filters on 'WHERE' Clause that reduces the record which are having length of 10, But Question needs to return NULL for invalid entries .

NEW QUESTION # 175
You are analyzing sales data from different regions stored in a Snowflake table named 'sales_data'. The table includes columns: 'transaction_id' (VARCHAR), 'region' (VARCHAR), 'sale_date' (DATE), and 'sale_amount' (NUMBER). You discover the following data quality issues: The 'region' column contains inconsistent entries such as 'North', 'north', 'NOrth ', and ' South'. The 'sale_amount' column has some values that are stored as strings (e.g., '100.50') instead of numbers, causing errors in aggregation. There are duplicate records identified by the same 'transaction id'. Which set of SQL statements, executed in the given order, provides the MOST effective and efficient way to address these data quality issues in Snowflake?

Answer: A

Explanation:
Option E presents the most efficient and effective solution. It combines all three data cleaning steps into a single operation using a CTE. First, standardizes the region name with trim and lowercase. Second, remove duplicate records based on transaction ID. And most important, it correctly handles the 'sale_amount' conversion using TRY_TO_NUMBER inside the CTE to avoid errors and ensures accurate aggregations down stream. This approach minimizes the number of table scans and UPDATE operations, improving performance. Option A fails on how to remove duplicates correctly using TRY_TO_NUMBER to convert the sale amount correctly and data type changes are not possible via ALTER statements if strings are present. Options B, C, and D does not combine all in one single CTE operations and are slower.

NEW QUESTION # 176
You're designing a data pipeline in Snowflake to process order data'. The raw order data, including customer information, is stored in a JSON format within a single 'RAW ORDERS table. Due to privacy regulations, you need to mask the customer's email addresses before loading the data into a 'CLEANED ORDERS' table, while maintaining referential integrity. Furthermore, you want to track the data lineage (which raw order resulted in which cleaned order) in a separate 'ORDER LINEAGE' table. Which of the following approaches achieves these requirements effectively and efficiently? (Select TWO)

A. Implement a stored procedure that reads data from 'RAW ORDERS, masks the email using SHA256, inserts the cleaned data into 'CLEANED ORDERS', and simultaneously inserts lineage information into 'ORDER LINEAGE. Schedule this stored procedure to run periodically.
B. Create a masking policy on the 'email' column in 'CLEANED ORDERS'. Create a stream on RAW ORDERS, then create a task to insert data from 'RAW ORDERS' into 'CLEANED ORDERS' and to INSERT the order to 'ORDER LINEAGE
C. Use a Snowpipe to load data into 'RAW ORDERS. Create a stream on 'RAW ORDERS'. Create a task chained to the stream that reads the new records from the stream, masks the email, inserts the cleaned data into 'CLEANED_ORDERS' table and inserts lineage information into the 'ORDER _ LINEAGE table.
D. Create a view on the 'RAW ORDERS table that masks the email address. Load the raw order data into 'RAW ORDERS table using Snowpipe and a stream. Use a task chained to the stream to ingest the view into 'CLEANED_ORDERS and populate lineage information into 'ORDER_LINEAGE table.
E. Create a masking policy on the 'email' column of the 'RAW_ORDERS' table. Then, create a task to copy all data from 'RAW_ORDERS to 'CLEANED_ORDERS. Finally, create a separate task that runs after the copying task to populate the 'ORDER_LINEAGE table with relevant mappings.

Answer: A,C

Explanation:
Option B: A stored procedure provides the most control over the masking process and lineage tracking, ensuring data is masked during the transfer. It allows you to perform all operations (masking, inserting into 'CLEANED ORDERS, and inserting into 'ORDER LINEAGE) within a single transaction. Option C: Snowpipe loading data to RAW_ORDERS, using streams to capture the data, and scheduling the tasks to read from the stream, while masking and adding lineage information provides automation and efficiency. Option A is incorrect, as it copies all data using a task into 'CLEANED_ORDERS and then masks the email on RAW_ORDERS after data is already in 'CLEANED_ORDERS'. Option D is incorrect as masking policy is on rather than Option E requires a view to mask email address and is not an effective approach to apply a masking policy. Also, its not the right procedure to load view into a table.

NEW QUESTION # 177
A data analyst is investigating a decline in the conversion rate on an e-commerce website. They have access to the following tables in Snowflake: 'sessions': 'session id', 'user id', 'start time', 'end_time' 'page views': 'session id', 'page_urr, 'view time' 'transactions': 'session_id', 'transaction id', 'amount', 'transaction_time' Which of the following approaches, using Snowflake features, would be MOST effective for identifying potential bottlenecks or drop-off points in the user journey?

A. Create a funnel analysis by defining key stages in the user journey (e.g., homepage visit, product page view, add to cart, checkout, purchase). Use window functions to track users as they progress through the funnel, calculating conversion rates between each stage. Visualize the funnel using a BI tool for easy identification of drop-off points.
B. Use Snowflake's 'SHOW TABLES' command to identify the most frequently updated tables. Then, create a dashboard on these tables to monitor the rate of changes.
C. Implement a data lineage tool to trace the flow of data from raw sources to the transaction table. This will reveal any data quality issues that may be impacting conversion rates.
D. Perform a cohort analysis by grouping users based on their sign-up date or initial website visit date. Track their conversion rates over time. Use a data visualization tool to see if any group exhibits an unusual drop in the conversion rate.
E. Use recursive SQL common table expressions (CTEs) to reconstruct the entire user journey for each session, from the entry page to either a successful transaction or session termination. Analyze path completion rates at each step to identify the pages where users are most likely to abandon the session.

Answer: A,D

Explanation:
Options B and C provide useful diagnostic insights. B offers direct information about conversion at each stage of the funnel. Option C enables discovery of unusual drops over time. Option A might be a difficult, resource intensive solution for complex user journeys. Option D is a poor approach as it identifies the rate of change in tables instead of the main objective - bottlenecks or drop-off points. Option E, while helpful for data governance, doesn't directly pinpoint user journey issues.

NEW QUESTION # 178
A ride-sharing company wants to analyze the density of ride requests in different city areas. They have a table 'RIDES' with a 'LOCATION' (GEOGRAPHY) column representing the pickup location of each ride. They want to divide the city into a grid of hexagonal cells and count the number of rides originating in each cell. Which sequence of steps would achieve this, assuming the 'city_boundary' is defined and accessible as a GEOGRAPHY object?

A. 1. Generate a grid of hexagonal GEOGRAPHY objects covering the 'city_boundary' using 'ST_SPHERE_GRID. 2. Use 'ST_CONTAINS' to determine which rides fall within each hexagonal cell. 3. Aggregate the ride counts for each cell.
B. 1. Generate a grid of hexagonal GEOGRAPHY objects covering the 'city_boundary' using 'ST_HEXGRID. 2. Use 'ST_INTERSECTS' to determine which rides intersect with each hexagonal cell. 3. Aggregate the ride counts for each cell.
C. 1. Generate a grid of rectangular GEOGRAPHY objects covering the 'city_boundary' using 'ST GRID. 2. Use 'ST CONTAINS' to determine which rides fall within each rectangular cell. 3. Aggregate the ride counts for each cell.
D. 1. Generate a grid of hexagonal GEOGRAPHY objects covering the 'city_boundary' using 'ST HEXGRI 2. Use 'ST_WITHIN' to determine which hexagonal cell each ride falls within. 3. Aggregate the ride counts for each cell.
E. 1. Generate a grid of hexagonal GEOGRAPHY objects covering the 'city_boundary' using 'ST_HEXGRID. 2. Use 'ST CONTAINS to determine which hexagonal cell contains each ride. 3. Aggregate the ride counts for each cell.

Answer: E

Explanation:
'ST HEXGRID is the correct function for generating a hexagonal grid. is used to check if a hexagonal cell contains a ride's location. Aggregating the ride counts for each cell provides the density information.

NEW QUESTION # 179
......

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