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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q164-Q169):
NEW QUESTION # 164
You're analyzing a sales dataset in Snowflake. The dataset contains 'customer id', 'product id', 'sale date', and 'sale amount'. You've noticed that some 'sale_amount' values are negative, which is illogical. You also suspect duplicate records, indicated by identical 'customer_id', 'product_id', and 'sale_date'. Additionally, a business rule dictates that sale amounts must be less than $10,000. Which combination of Snowflake SQL statements is the MOST efficient and accurate way to address these data quality issues?
Answer: B
Explanation:
Option E correctly handles all specified data quality issues. It replaces negative sale amounts with 0, enforces the business rule (sale_amount < 10000), and removes duplicate records based on 'customer_id', 'product_id', and 'sale_date' using the QUALIFY clause and ROW_NUMBER() window function, selecting only the first occurrence of each duplicate set. Options A and D do not handle negative sales amount correctly, Option B uses IFF(sale_amount < O, O, sale_amount) but do not handle boundary values of sale_amount < 10000, Option C considers sale amount between 0 and 10000.
NEW QUESTION # 165
You are tasked with creating a stored procedure in Snowflake to perform data cleansing on a table named 'CUSTOMER DATA'. The procedure should: 1) Remove rows where the 'EMAIL' column is NULL or empty. 2) Standardize the 'PHONE NUMBER' column by removing all non-numeric characters and ensuring it's exactly 10 digits long. 3) Return the number of rows removed due to invalid emails and the number of rows modified due to phone number standardization. Assume the table already exists and contains columns 'CUSTOMER (INT), (VARCHAR), and 'PHONE NUMBER (VARCHAR). Which of the following code snippets correctly implements this stored procedure? The procedure should use exception handling to gracefully handle errors, returning -1 for both counts if any error occurs.
Answer: D
Explanation:
Option A is correct because it uses SQL to perform the data cleansing tasks, correctly utilizes 'SQLROWCOUNT' to capture the number of affected rows, and returns the results as a VARIANT OBJECT. It also includes proper exception handling. Options B, C, and D have errors in syntax or logic regarding return types, variable declaration, or how to retrieve row counts. Specifically, using Javascript or returning an ARRAY/TABLE when VARIANT is more flexible in this scenario.
NEW QUESTION # 166
You are responsible for collecting server log data from multiple geographically distributed data centers. The logs are generated at a high velocity and variety of formats (JSON, CSV, plain text). The requirement is to ensure minimal data loss and efficient ingestion into Snowflake, while also handling potential schema variations across different log sources. Which of the following is the MOST robust and scalable solution, considering potential schema drift and data volume?
Answer: B
Explanation:
A distributed log aggregation system (Fluentd/Logstash) is the best choice here. These systems are designed for handling high- velocity, varied log formats, and schema variations. They can buffer data to prevent data loss and transform the data into a consistent format before ingestion into Snowflake. Snowpipe provides efficient data loading from cloud storage. This combination provides scalability, reliability, and flexibility. Message queues require more configuration overhead and external tables can be slower for querying. Custom scripts are less scalable and harder to maintain. Direct streaming using JDBC is not recommended for high-volume data.
NEW QUESTION # 167
You're building a Snowflake forecasting model to predict website traffic. Your dataset contains 'VISIT DATE (DATE), 'PAGE VIEWS (NUMBER), and 'PROMOTION FLAG' (BOOLEAN, indicating whether a promotion was active that day). You suspect that promotional periods significantly impact traffic, but need to account for days after a promotion that show residual impact. Which of the following strategies can you employ to improve your forecasting model to handle promotion and their lagging effects. Select two correct options.
Answer: A,E
Explanation:
Options A and C are correct. Option A helps the model directly capture the time elapsed since a promotion, allowing it to learn the decaying effect. Option C captures the lagged effects of promotions by including ' PROMOTION_FLAG' values from previous days as separate features. Option B is incorrect because simple moving average is a bad approach that may not be able to learn complex patterns of promotion effects on forecasting data, moreover promotional periods will be ignored. Option D is incorrect as promotions are valuable signals, not noise. Option E is incorrect because Snowflake's 'HOLIDAY DETECTION' feature automatically deals with typical public holidays, not self defined promotional campaigns.
NEW QUESTION # 168
A data analyst is working with JSON data representing product reviews. The JSON structure is complex, containing nested arrays and objects. The analyst needs to extract all the reviewer names (reviewer _ name) who gave a rating greater than 4, along with the product ID (product_id) and the overall average rating of that specific product. The table 'RAW REVIEWS contains a single VARIANT column named holding the JSON data'. Choose the most efficient and correct Snowflake SQL query to achieve this. Assume the JSON structure is consistent across all rows.
Answer: B
Explanation:
Option C correctly uses LATERAL FLATTEN to unnest the 'reviews' array within the JSON. It accurately extracts 'reviewer_name' and 'rating' from the flattened array 'r'. Critically, it correctly retrieves directly from the 'raw_json' column as the product ID is not within the nested 'reviews' array, but at the root level. The average rating is correctly calculated using a window function partitioned by the Option A incorrectly references the product_id in the flatten table.Option B is inefficient and incorrect because it assumes only one review exists ([0]) and uses GET PATH, which is less performant than direct path access. Options D is also incorrect for average rating as raw_json:reviews[0].rating will pick only the first rating in JSON to derive avg_rating.
NEW QUESTION # 169
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