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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q120-Q125):

NEW QUESTION # 120
You are tasked with designing a Snowflake data model for a system that tracks changes to product information. You need to store both the current and historical states of the 'PRODUCTS' table. Which of the following strategies, regarding primary keys and table structures, is the MOST appropriate for efficiently querying the current product state and historical changes, considering best practices for Snowflake and minimizing storage costs? Select all that apply.

A. Create two tables: with 'PRODUCT_ID as the primary key, storing the current product information, and 'PRODUCTS_HISTORY' with 'PRODUCT ID', 'EFFECTIVE DATE, and 'END DATES columns, where the combination of 'PRODUCT and 'EFFECTIVE DATE' serves as a composite key. Use a scheduled task to copy data from 'PRODUCTS_CURRENT to 'PRODUCTS_HISTORY whenever a product is updated.
B. Create a single table, 'PRODUCTS', with columns 'PRODUCT_ID (primary key), 'PRODUCT NAME, 'PRODUCT DESCRIPTION', 'EFFECTIVE DATE and 'END_DATE. Implement clustering on 'PRODUCT_ID' and 'EFFECTIVE_DATE'.
C. Create a single table, 'PRODUCTS', with columns 'PRODUCT ID, 'PRODUCT NAME', 'PRODUCT DESCRIPTION', 'EFFECTIVE DATE, and 'END_DAT Add a sequence to act as a surrogate key and define this sequence as the primary key.
D. Create a single table, 'PRODUCTS', with 'PRODUCT_ID as the primary key and a 'JSON' column to store the historical product data. Use a stored procedure to update the ' JSON' column whenever a product attribute changes.
E. Create a single table, 'PRODUCTS', with columns 'PRODUCT_ID (primary key), 'PRODUCT_NAME', 'PRODUCT_DESCRIPTION', 'EFFECTIVE_DATE', and 'END_DATE. Whenever a product attribute changes, insert a new row with the updated attribute values and the new 'EFFECTIVE_DATE , setting the 'END_DATE' of the previous record to the ' EFFECTIVE_DATE of the new record.

Answer: B,E

Explanation:
Option B correctly describes a type 2 slowly changing dimension (SCD2) approach. This is a common and effective way to track historical changes. Each row represents a specific state of a product over a defined period (between 'EFFECTIVE_DATE and 'END_DATE). While Snowflake doesn't enforce primary keys, defining 'PRODUCT ID' as the primary key provides metadata to the optimizer. Option D suggests clustering on "PRODUCT_ID and which is crucial for optimizing queries that retrieve product information for specific time periods. This clustering will significantly improve query performance. Option A is less efficient, the scheduled task copies data which leads to higher compute cost. JSON isn't scalable since querying JSON efficiently requires specific functions. Option E introduces surrogate keys that can be useful, clustering helps more here.

NEW QUESTION # 121
You are tasked with creating a data model for a global e-commerce company in Snowflake. They have data on customers, products, orders, and website events. They need to support complex analytical queries such as 'What are the top 10 products purchased by customers in the US who have visited the website more than 5 times in the last month?' The data volumes are very large, and query performance is critical. Which of the following data modeling techniques and Snowflake features, used in combination, would be MOST effective?

A. A data vault model, combined with Snowflake's search optimization service on the hub tables.
B. A star schema with fact and dimension tables, combined with materialized views to pre-aggregate data and clustering on dimension keys in the fact table.
C. A wide, denormalized table containing all customer, product, order, and event data, combined with Snowflake's zero-copy cloning for data backups.
D. A fully normalized relational model with primary and foreign key constraints, combined with Snowflake's automatic query optimization.
E. A star schema with fact and dimension tables, combined with clustering the fact table on a composite key of customer ID and product ID.

Answer: B,E

Explanation:
Options B and E are the most effective. A star schema (B and E) is well-suited for analytical workloads. Clustering the fact table on customer and product IDs (B) helps improve query performance when filtering on those dimensions. Materialized views (E) provide pre-aggregated data for common queries, further boosting performance. Normalization (A) can lead to too many joins. Data Vault (C) is complex and may not be necessary. A wide, denormalized table (D) can be difficult to manage and maintain, and zero-copy cloning is for backup, not performance. Clustering on dimension keys in the fact table works best when coupled with a star schema and the keys are frequently used as a filter.

NEW QUESTION # 122
You have a Snowpipe configured to load CSV files from an AWS S3 bucket into a Snowflake table. The CSV files are compressed using GZIP. You've noticed that Snowpipe is occasionally failing with the error 'Incorrect number of columns in file'. This issue is intermittent and affects different files. Your team has confirmed that the source data schema should be consistent. What combination of actions provides the most likely and efficient solution to address this intermittent column count mismatch issue?

A. Recreate the Snowflake table with a 'VARIANT column to store the entire CSV row as a single field. Then, use SQL to parse the 'VARIANT* data into the desired columns.
B. Check for carriage return characters within the CSV data fields. These characters can be misinterpreted as row delimiters, leading to incorrect column counts. Use the and 'RECORD_DELIMITER parameters in the file format to correctly parse the CSV data.
C. Set the 'SKIP_HEADER parameter in the file format to 1 and ensure that a header row is consistently present in all CSV files. Also implement a task that validates that the header of all CSV files are correct.
D. Adjust the parameter in the file format to FALSE. This will allow Snowpipe to load the data, skipping rows with incorrect column counts. Implement a separate process to identify and handle skipped rows.
E. Investigate the compression level of the GZIP files. Some compression levels might lead to data corruption during decompression, causing incorrect column counts. Lowering the compression might help.

Answer: B,D

Explanation:
Setting *ERROR ON COLUMN COUNT MISMATCH' to FALSE allows the pipe to continue without halting on such errors. However, this approach will leave behind bad records. Carriage return issues can occur, which affect the column count when ingesting data. If there are carriage return characters inside the CSV fields, this will be misinterpreted as delimiters. Option A might help if headers are present and consistent, but is less likely the root cause of an intermittent column count mismatch. Option C is unlikely to be a primary cause of column count issues as GZIP decompression is generally reliable. Option E is a workaround, but less efficient than correctly configuring the CSV parsing.

NEW QUESTION # 123
You are tasked with loading JSON files into Snowflake using Snowsight. The JSON files are semi-structured and contain nested arrays and objects. You want to flatten the JSON structure during the load process to facilitate easier querying. Which of the following Snowsight-integrated features or approaches are suitable for flattening the JSON data during the load process? Select all that apply.

A. Load the JSON data into a staging table as raw JSON strings. Then, write a custom Python User-Defined Function (UDF) that parses the JSON strings and returns a flattened representation of the data. Use this UDF in a 'CREATE TABLE AS SELECT statement to load the flattened data into a new table.
B. Use Snowsight's 'Load Data' wizard along with Snowflake's built-in functions like 'GET_PATH' and operator within the computed column expressions during the load process to extract and load specific elements from the JSON data into individual columns.
C. Load the JSON data into a VARIANT column in Snowflake. Then, use SQL with 'LATERAL FLATTEN' to query and extract data from the nested structures, creating a view to represent the flattened data.
D. Create a Snowpipe that automatically loads new JSON files from a stage into a Snowflake table. Within the Snowpipe definition, define a transformation that uses 'LATERAL FLATTEN' to flatten the JSON data before loading it into the target table.
E. Use Snowsight's 'Load Data' wizard to load the JSON files directly into relational tables by defining mappings between JSON elements and table columns. Snowsight automatically flattens the JSON structure based on the defined mappings.

Answer: B,C,D

Explanation:
Options A, C, and E are all suitable approaches for flattening JSON data. Option A leverages 'LATERAL FLATTEN' in SQL, which is a common and efficient way to flatten JSON structures after loading into a VARIANT column. Option C utilizes Snowpipe with a transformation including 'LATERAL FLATTEN' , enabling automatic flattening during ingestion. Option E uses computed columns in Snowsight to extract JSON elements during loading using 'GET_PATH' or the operator. Option B is incorrect; Snowsight's 'Load Data' wizard doesn't automatically flatten JSON during the initial load. It mainly handles the basic load of the JSON file into a VARIANT column, not automatic flattening. Option D is a valid but less performant alternative; UDFs can be slower than native Snowflake SQL functions for large datasets.

NEW QUESTION # 124
You are analyzing website traffic data stored in a Snowflake table named 'page views'. The table has columns 'user id', 'page_url', and 'timestamp'. You need to identify users who visited a specific sequence of pages ('/home', '[products', '/cart', '[checkout') within a 5- minute window. Which analytic function and additional Snowflake features would be MOST efficient and accurate to achieve this?

A. Use LAST VALUE and FIRST VALUE along with CASE statements, ordering by timestamp for each user, followed by joining the table to itself four times to find the page sequence.
B. Use to check previous page visits, ordered by 'timestamp' within each 'user_id' , and then filter based on the sequence. Create a user-defined function (UDF) in Python to handle the 5-minute window logic.
C. Create a stored procedure that iterates through each user's page views, sorted by timestamp, checking for the page sequence within the 5-minute window.
D. Use 'SESSIONIZE to define user sessions based on a 5-minute inactivity gap. Then, use window functions to identify users who visited all four specified pages within the same session. This requires Snowflake Enterprise Edition.
E. Use 'LEAD and 'LAG' functions to check the next and previous page visits, ordered by 'timestamp' within each , then use a 'QUALIFY' clause with conditional statements to identify the correct sequence and timeframe.

Answer: E

Explanation:
Option B is the most efficient and accurate. Using LEAD and LAG allows direct comparison of adjacent events in time, and the QUALIFY clause provides a concise way to filter based on complex conditions. Sessionize is also valid in Enterprise Edition. While UDFs (A) and stored procedures (D) are possible, they are generally less performant than SQL analytic functions. Option E, self-joining the table multiple times, would be inefficient and difficult to maintain.

NEW QUESTION # 125
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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q120-Q125):

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