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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q185-Q190):

NEW QUESTION # 185
You are using Snowpipe to continuously load data from an AWS S3 bucket into a Snowflake table called 'ORDERS. The data is in JSON format. You observe that Snowpipe is occasionally missing records, even though the S3 event notifications are being correctly sent to the Snowflake-managed SQS queue. Upon investigation, you discover that some JSON records are larger than the maximum size supported by Snowpipe for a single record (16MB). You need to implement a solution to handle these oversized JSON records without losing data,. Which of the following approaches is the most efficient and reliable?

A. Increase the 'MAX FILE SIZE parameter of the Snowpipe configuration to accommodate the larger JSON records. Snowflake automatically handles oversized records by splitting them internally.
B. Configure the S3 bucket to automatically split oversized JSON files into smaller files before they are sent to the SQS queue. Snowpipe will then process these smaller files independently.
C. Use the 'VALIDATE function in Snowflake to identify oversized JSON records. Then, manually extract and split those records into smaller files and load them separately.
D. Implement a pre-processing step using an AWS Lambda function triggered by S3 events to split the oversized JSON records into smaller, valid-sized chunks before they are ingested by Snowpipe. Update the Snowpipe COPY statement to handle the new chunked data format.
E. Disable Snowpipe and switch to a batch loading approach using the COPY INTO command with automatic data compression. The COPY INTO command handles larger files more efficiently than Snowpipe.

Answer: D

Explanation:
The correct answer is B. Snowpipe has a limitation of 16MB per record. The most reliable solution is to pre-process the oversized records before they reach Snowpipe. Using an AWS Lambda function is a serverless and scalable way to split these records. Option A is incorrect because 'MAX FILE_SIZE pertains to the size of the files, not individual records within those files. Option C is not feasible as S3 doesn't natively split JSON files. Option D is inefficient as it involves manual intervention. Option E defeats the purpose of continuous data loading with Snowpipe. By splitting oversized records before Snowpipe ingests them, you ensure that no data is lost, and Snowpipe can continue to operate as designed.

NEW QUESTION # 186
You are building a sales performance dashboard in Snowflake. You need to incorporate a custom metric called 'Sales Efficiency', which is calculated as (Total Sales Revenue / Number of Sales Representatives) 1000. You want to expose this metric in your dashboard and allow users to filter data based on specific Sales Efficiency ranges. You also want to allow filtering by region and quarter. Which of the following approaches would be MOST performant and maintainable in Snowflake?

A. Create a materialized view in Snowflake that pre-calculates 'Sales Efficiency', region, and quarter. Include appropriate indexes and partition keys on the view. Expose the materialized view to the dashboard.
B. Calculate 'Sales Efficiency' directly in the dashboard tool for each query using its expression engine. Implement region and quarter filtering within the dashboard tool.
C. Create a Snowflake user-defined function (IJDF) in JavaScript to calculate 'Sales Efficiency'. Use this IJDF in the dashboard queries, along with standard WHERE clauses for region and quarter filtering.
D. Create a Snowflake stored procedure that calculates 'Sales Efficiency' based on the selected Region and Quarter. The dashboard calls the stored procedure and displays the returned result set.
E. Create a Snowflake view that calculates 'Sales Efficiency'. The dashboard queries filter this view using standard WHERE clauses for region and quarter. No indexes or partition keys are created.

Answer: A

Explanation:
A materialized view (option C) is the most performant option. It pre-calculates the 'Sales Efficiency' metric and stores the results, allowing for fast retrieval. Proper indexing and partitioning further optimize query performance. Performing the calculation in the dashboard tool (option A) is inefficient for large datasets. IJDFs (option B) can be slower than native SQL. A regular view (option D) does not store the pre- calculated data, so the calculation is performed every time the view is queried. Stored procedures are typically less efficient for dashboard queries than materialized views, as they need to be executed each time.

NEW QUESTION # 187
When working with Snowsight dashboards to summarize large data sets, what key advantage do they offer in exploratory analyses?

A. They only support basic data summarization.
B. Snowsight dashboards facilitate quick, visual comprehension of complex data.
C. Snowsight dashboards can't handle large data sets efficiently.
D. They are limited to presenting static data sets.

Answer: B

Explanation:
Snowsight dashboards aid in exploratory analysis by providing visually accessible insights into complex data, aiding quick comprehension.

NEW QUESTION # 188
A company ingests sensor data into a Snowflake table named READINGS with columns (VARCHAR), 'reading_time' (TIMESTAMP NTZ), and 'raw_value' (VARCHAR). The 'raw_value' column contains numeric data represented as strings, but sometimes includes non-numeric characters (e.g., '123.45', 'N/A', '500'). You need to calculate the average of the numeric raw_value' readings for each within the last hour, excluding invalid readings. Which of the following Snowflake SQL statements will correctly accomplish this, handling potential conversion errors and filtering for valid data?

A. SELECT sensor_id, raw_value, NULL))) FROM SENSOR_READINGS WHERE reading_time DATEADD(hour, -1, CURRENT TIMESTAMP()) GROUP BY sensor_id;
B. SELECT sensor_id, AVG(CASE WHEN THEN ELSE NULL END) FROM SENSOR_READINGS WHERE reading_time DATEADD(hour, -1, CURRENT TIMESTAMP()) GROUP BY sensor_id;
C. SELECT sensor_id, 'N/A'))) FROM SENSOR_READINGS WHERE reading_time DATEADD(hour, -1 , CURRENT TIMESTAMP()) GROUP BY sensor_id;
D. SELECT sensor_id, FROM SENSOR_READINGS WHERE reading_time DATEADD(hour, -1, CURRENT _ TIMESTAMP()) GROUP BY sensor id;
E. SELECT sensor_id, FROM SENSOR_READINGS WHERE reading_time DATEADD(hour, -1 , AND TRY_TO IS NOT NULL GROUP BY sensor_id;

Answer: E

Explanation:
Option B is the correct answer because 'TRY TO NUMBER attempts to convert the 'raw_value' to a number, returning NULL if the conversion fails. The 'AND TRY_TO_NUMBER(raw_value) IS NOT NULL' clause then filters out these NULL values, ensuring only valid numeric readings are included in the average calculation. Option A will throw an error if it encounters a non-numeric value. Option C, while functionally correct, utilizes which can be less reliable for specific locale formats compared to Option D is unnecessarily complex and less readable. Option E only handles 'N/A', not other potential invalid values.

NEW QUESTION # 189
A company stores web analytics data in a Snowflake table named 'WEB EVENTS. This table includes a 'USER ID column, a 'TIMESTAMP' column indicating when the event occurred, and a 'EVENT TYPE column that captures the type of event (e.g., 'page_view', 'add_to_cart', 'purchase'). The data analysts want to enrich this data to identify the first and last event times for each user. Which Snowflake features or functions would be MOST appropriate and efficient for achieving this enrichment?

A. Creating a stored procedure that iterates through each user ID and finds the minimum and maximum timestamp using separate queries.
B. Using a correlated subquery to find the minimum and maximum timestamp for each user in the 'WEB EVENTS' table.
C. Using a lateral view combined with a table function to find the first and last event times.
D. Using a simple GROUP BY clause on 'USER ID to find the minimum and maximum timestamp.
E. Using window functions such as FIRST _ VALUE and 'LAST_VALUE partitioned by 'USER_ID and ordered by 'TIMESTAMP' to find the first and last event times.

Answer: E

Explanation:
Window functions are the most efficient approach for calculating aggregate values (like minimum and maximum) within partitions (in this case, per user) without requiring self-joins or subqueries. Correlated subqueries can be inefficient for large datasets. Stored procedures with iteration are generally slower than set-based operations. Lateral views are more suitable for exploding array structures, not for finding min/max values. A simple GROUP BY would provide the overall minimum and maximum, not per user.

NEW QUESTION # 190
......

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