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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q14-Q19):

NEW QUESTION # 14
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. 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.
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. 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.
D. 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.
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: C

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 # 15
How does leveraging window functions in Snowflake differ from using table functions for data manipulation?

A. Window functions operate on entire datasets
B. Window functions modify table structures directly
C. Table functions are limited to specific data types only
D. Table functions generate tables as output

Answer: A

Explanation:
Window functions process data within specified partitions or frames, while table functions generate tables as their output, differing in their scope and operation.

NEW QUESTION # 16
A financial institution uses Snowflake to store customer transaction data'. They need to create a dashboard that visualizes daily transaction volume and average transaction amount for fraud detection purposes. This dashboard needs to be automatically updated every hour. The current dashboard query performance is slow, especially during peak hours. Given that the 'TRANSACTIONS table contains billions of rows, which of the following strategies would BEST optimize both the query performance and the automated update process?

A. Create a temporary table that stores daily transaction summaries. Truncate and reload the temporary table hourly using a scheduled Snowflake task.
B. Increase the warehouse size to X-Large and rely on Snowflake's query optimization engine without any changes to the data model or update process.
C. Create a materialized view that pre-aggregates the daily transaction volume and average transaction amount. Schedule a Snowflake task to refresh the materialized view hourly.
D. Implement caching within the dashboard application to store the query results and only refresh the data once a day to avoid performance issues
E. Create a regular view that calculates daily transaction volume and average transaction amount. Use a Snowflake stored procedure to execute the query and update a separate reporting table hourly.

Answer: C

Explanation:
Materialized views are designed for pre-computation of aggregations, providing significant performance improvements. Scheduling a task to refresh the materialized view ensures automatic updates. Regular views are calculated at query time and would not improve performance. Increasing warehouse size (C) might help, but it's not the most efficient solution. Temporary tables are not persistent and truncation/reload is inefficient. Dashboard caching (E) does not solve underlying Snowflake performance issues.

NEW QUESTION # 17
You are preparing to load data from an internal stage that contains multiple CSV files into a Snowflake table. You need to ensure that only files matching a specific pattern are loaded (e.g., files starting with 'sales_'). You also need to transform the date column (located in the third position of each CSV file) from 'YYYYMMDD' to 'YYYY-MM-DD' format during the load. The target table 'SALES has columns 'product_id', and 'region'. Which combination of options correctly filters the files and transforms the date format during the load process?

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

Answer: C

Explanation:
Option D is correct. It uses the 'PATTERN' parameter to filter files starting with 'sales_' and ending with '.csv'. It then uses the TRANSFORM AS SELECT clause to select and transform the data. Inside the select clause, it correctly uses 'substr' to reformat the date and 'TO_DATE to convert the re-formatted string into a DATE datatype. The 'ON_ERROR = 'SKIP_FILE" is a good practice for handling potential errors. Option A has an invalid parameter declaration within the COPY INTO command and does not use PATTERN, making the filter inaccurate. Option B attempts to define date format at file format level, but date transformatinon is required and FILES filter declaration is invalid. Option C uses 'TRANSFORMATION' instead of 'TRANSFORM AS SELECT , which is incorrect and will cause an error. Option E attempts to define DATE FORMAT with the wrong TYPE and is missing concatination of DATE string.

NEW QUESTION # 18
A retail company has data about their products, sales, and inventory. They need a dashboard to visualize key metrics, including total sales, average order value, inventory levels, and product performance across different regions. The data is stored in the following tables: 'PRODUCTS (PRODUCT ID, PRODUCT NAME, CATEGORY, PRICE) 'SALES' (SALE_ID, PRODUCT_ID, SALE_DATE, QUANTITY, REGION) 'INVENTORY (PRODUCT ID, REGION, QUANTITY ON_HAND) Which of the following strategies will result in an efficient dashboard that allows users to quickly filter and drill down into the data by region, product category, and time period while minimizing query execution time? (Select all that apply.)

A. Implement dynamic data masking policies to filter out sensitive data from the base tables, ensuring data governance.
B. Utilize Snowflake's search optimization service on relevant columns (e.g., PRODUCT ID, REGION) in the base tables and use standard JOINs and aggregations within views used by the dashboard.
C. Create materialized views that pre-aggregate sales data by region, product category, and time period (e.g., daily, weekly, monthly). Join these materialized views with product and inventory data in the dashboard queries.
D. Create separate views for sales, inventory, and product information, then use the dashboard tool to join these views and perform aggregations.
E. Create a single, wide denormalized table containing all the necessary data from the 'PRODUCTS, 'SALES, and 'INVENTORY tables using JOINs. Build the dashboard directly on this table.

Answer: B,C

Explanation:
Search optimization (C) can significantly speed up queries on large tables by creating a search index on frequently used filter columns. Materialized views (D) are also beneficial because they pre-aggregate the data, reducing the amount of computation required at query time. Creating a single, wide denormalized table (A) can lead to data redundancy and increased storage costs. Joining separate views in the dashboard tool (B) can be inefficient, as the joins are performed at query time. Data masking policies (E) are important for security but don't directly optimize query performance for dashboards.

NEW QUESTION # 19
......

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Carl Ford Carl Ford

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