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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q263-Q268):

NEW QUESTION # 263
You are designing a data warehouse in Snowflake for a retail company. The company has two tables: 'Transactions' (TransactionlD, CustomerlD, ProductlD, TransactionDate, Amount) and 'Products' (ProductlD, ProductName, CategorylD, Price). The 'Transactions' table contains millions of rows, and analysts frequently run queries that join these tables to analyze sales by product category. To optimize query performance and reduce data redundancy for these analytical queries, which of the following strategies would be MOST effective, considering Snowflake's architecture and best practices?

A. Create a standard view that joins the 'Transactions' and 'Products' tables, and rely on Snowflake's query optimizer to automatically optimize the join performance.
B. Create a clustered table that includes all columns in transactions and products tables using 'CLUSTER BY' clause to cluster most frequently used column.
C. Denormalize the 'Transactions' table by adding 'ProductName' and 'CategorylD columns directly to the 'Transactions' table using a data transformation pipeline after the initial load.
D. Create a materialized view that joins the 'Transactions' and 'Products' tables and includes relevant columns for analysis, partitioning the view by 'TransactionDates.
E. Create a stored procedure that automatically rebuilds the standard view daily using the latest data from the 'Transactions' and 'Products' tables.

Answer: C,D

Explanation:
Options A and D are correct. Creating a materialized view (option A) is a good approach if the underlying tables don't change often. Snowflake will automatically refresh the materialized view when changes occur in the base tables. In this case, the company will have increased query performance. Partitioning by TransactionDate also improves the query's performance. Materialized views store the pre-computed results, significantly speeding up queries that use the view. Denormalizing (option D) by adding 'ProductName' and 'CategorylD' to the 'Transactions' table avoids the join entirely for queries that only need these columns, improving performance and reducing data redundancy. A standard view (option B) doesn't store pre-computed results. While Snowflake's optimizer is effective, it won't provide the same performance gains as a materialized view or denormalization. Option C is not correct because creating a clustered table does not avoid the need to join the tables. Option E is not correct because using the materialized view, the Snowflake will automatically refresh it when changes occur.

NEW QUESTION # 264
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. 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.
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. 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.
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 # 265
You are analyzing website traffic data in Snowflake and notice a sudden drop in page views from a specific country (Country A) starting last month. You have access to the 'WEBSITE TRAFFIC' table with columns: 'date' , 'country', 'page_viewS, 'device_type'. Which of the following queries and techniques would be MOST effective in identifying the potential cause of this anomaly?

A. Analyze 'page_viewS by 'device_type' for Country A before and after the drop to see if the drop is concentrated in a specific device type (e.g., mobile, desktop). Use 'CASE statement within the 'GROUP to categorize time periods.
B. Run a simple 'SELECT FROM WEBSITE_TRAFFIC WHERE country = 'Country A' AND date DATEADD(month, -3, GROUP BY date ORDER BY date;' to visualize the trend and confirm the drop.
C. Use a statistical anomaly detection function (e.g., moving average) on the 'page_views' for Country A and compare against other countries to identify if the drop is specific to Country A. Consider using 'LAG' function with 'OVER' clause to calculate the moving average.
D. Join the 'WEBSITE_TRAFFIC' table with a table containing marketing campaign data (MARKETING CAMPAIGNS') on 'date' and 'country' to see if any marketing campaigns were paused or modified in Country A around the time of the drop. Consider using 'LEFT JOIN' to not lose traffic data.
E. Execute 'SELECT FROM WEBSITE_TRAFFIC WHERE country = 'Country A' AND date DATEADD(month, -1, CURRENT DATE());' and manually inspect the data for suspicious patterns.

Answer: A,C,D

Explanation:
Options B, C, and D are the most effective. B uses statistical methods to identify the anomaly, C investigates potential external factors (marketing campaigns), and D explores internal segments (device types). Option A is a basic check but doesn't identify causes. Option E is not scalable and inefficient for large datasets. Using a combination of statistical analysis, external data integration, and segmentation provides a comprehensive diagnostic approach.

NEW QUESTION # 266
A data engineer is tasked with auditing changes made to the 'EMPLOYEES' table over the past week. The data retention period for the Snowflake account is set to 7 days. They need to identify all the rows that were updated or deleted during this period. Which of the following approaches, utilizing Snowflake's Time Travel capabilities, will efficiently provide the required audit information, assuming no custom metadata tracking was implemented?

A. Query the 'EMPLOYEES' table using the 'AT(OFFSET => -604800)' clause and compare the results with the current state of the table to identify changes.
B. Query the 'QUERY_HISTORY view in the ACCOUNT _ USAGE schema to identify all UPDATE and DELETE statements executed against the 'EMPLOYEES' table in the past week, then use Time Travel with specific query IDs to retrieve the affected rows.
C. Enable Snowflake's Change Data Capture (CDC) functionality. Use the stream object to obtain all updates to 'EMPLOYEES' table, then use Time Travel for the stream data to access the required data.
D. Create a clone of the 'EMPLOYEES' table from a week ago using Time Travel, and then perform a full outer join between the clone and the current table, identifying differences based on NULL values in either table.
E. Use the 'VALIDATE' table functionality combined with Time Travel to identify modified rows within the last week.

Answer: B

Explanation:
Option C provides the most efficient and comprehensive approach. By querying the 'QUERY HISTORY view, the engineer can identify the specific queries that modified the 'EMPLOYEES table. Then, using Time Travel with the query IDs, they can retrieve the state of the table before those queries were executed, allowing for a precise comparison and identification of the affected rows. Option A is too broad; it provides a snapshot of the table a week ago but doesn't pinpoint specific changes. Option B doesn't allow data extraction of the changes, so incorrect. Option D is computationally expensive and may not be accurate for large tables. Option E is not feasible because the table was already updated, and CDC needs to be setup before the event. Setting up a stream on a historical data point is not possible.

NEW QUESTION # 267
You are analyzing website traffic data in Snowflake and want to identify unusual access patterns using statistical techniques and visualize them. You have a table LOG' with columns (TIMESTAMP_NTZ), 'IP_ADDRESS (VARCHAR), and (VARCHAR). Which of the following approaches, combining Snowflake SQL and visualization techniques, is the MOST effective for detecting and visualizing anomalies in access patterns?

A. Calculate the rate of change of access attempts per IP address over short time intervals (e.g., 5 minutes) using 'LAG' or 'LEAD functions, identify IP addresses with significantly high rates of change compared to the historical average (using statistical measures like standard deviation), and visualize these IP addresses and their rate of change on a scatter plot with alerts for outliers.
B. Calculate the average number of page accesses per IP address per day using a simple 'AVG' aggregate function and display it on a line chart.
C. Use GROUP BY to find the count of accesses per IP address per page and represent that using a heat map.
D. Simply count the total number of accesses per IP address and visualize it in a histogram.
E. Use Snowflake's function to identify the most frequently accessed pages and visualize them in a bar chart, filtering out common pages like the homepage.

Answer: A

Explanation:
Option C is the most effective because it utilizes time series analysis to identify sudden spikes in access attempts, suggesting potential anomalies like bot attacks or brute-force attempts. It incorporates statistical analysis to identify significant deviations from the norm and provides a visual representation for easy identification of outliers. The other options lack the time series and statistical components necessary for robust anomaly detection. Options A, B, D and E, might provide insights into popular pages or IPs, but would not identify anomalous behavior based on rapid changes.

NEW QUESTION # 268
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