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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q247-Q252):

NEW QUESTION # 247
A data analyst is tasked with optimizing a daily ETL pipeline that loads data from several external sources into a Snowflake data warehouse. One of the key transformations involves joining two large tables, 'ORDERS' (millions of rows) and 'CUSTOMERS' (hundreds of thousands of rows), based on 'CUSTOMER ID. The pipeline currently uses a standard 'JOIN' operation, but the transformation step is taking longer than expected. The analyst has explored various optimization techniques, including increasing virtual warehouse size, but the performance improvement is minimal. Assuming that the 'CUSTOMER ID column is appropriately indexed (or clustered, if applicable) in both tables, and you want to minimise data movement. Which of the following approaches would yield the MOST significant performance improvement for this transformation step, considering metadata caching and data distribution?

A. Implement a 'MAP JOIN' by setting = FALSE at session Level, which caches the smaller table ('CUSTOMERS') in memory on each node of the virtual warehouse.
B. Use a CTAS (CREATE TABLE AS SELECT) statement with the = HASH' clause on the 'CUSTOMER_ID column to redistribute the 'CUSTOMERS table before the join operation.
C. Pre-sort both tables by 'CUSTOMER_ID before the join operation using 'ORDER BY clauses in subqueries, ensuring that the data is co-located for faster processing.
D. Ensure that the 'CUSTOMER_ID column in both 'ORDERS' and 'CUSTOMERS' tables have the same data type and collation. Then, leverage Snowflake's automatic optimization capabilities without explicit hints or redistribution.
E. Use a 'BROADCAST JOIN' hint to force Snowflake to distribute the smaller 'CUSTOMERS' table to all nodes in the virtual warehouse, regardless of the table sizes and statistics.

Answer: D

Explanation:
Ensuring consistent data types and collations (D) is crucial for optimal join performance in Snowflake. When data types and collations match, Snowflake can leverage its internal optimizations more effectively, including metadata caching and efficient data access patterns. Using a 'BROADCAST JOIN' hint without considering data distribution (A) might lead to unnecessary data movement and performance degradation. 'MAP JOIN' is unavailable in Snowflake (B). Redistributing data using 'DISTRIBUTION_TYPE = HASH' (C) is generally less efficient than leveraging Snowflake's automatic optimizations. Pre-sorting data (E) is unnecessary in Snowflake as it does not guarantee data colocation in the same way that other distributed systems might.

NEW QUESTION # 248
You are tasked with preparing and loading data from multiple CSV files stored in a cloud storage location into a Snowflake table named 'customer data'. The files have the following issues: Inconsistent delimiters (some use commas, others use pipes). Missing values represented by different strings (e.g., 'NULL', 'N/A', empty strings). Different date formats. Duplicate records across files. Which of the following strategies can you implement to address these issues efficiently during the data loading process? (Select all that apply)

A. Utilize the 'ON ERROR = 'SKIP FILE" option in the 'COPY INTO command to skip files with errors and process the rest.
B. Use the 'VALIDATE' function with the 'LOAD ONLY' option to identify files with invalid data before loading.
C. Employ a data transformation pipeline using Snowflake streams and tasks to standardize date formats, handle missing values, and deduplicate records.
D. Implement error handling using 'ON ERROR = 'CONTINUE'S in the COPY INTO' command along with a 'VALIDATE function to capture errors and store them in a separate error table
E. Create a file format object with 'FILE_FORMAT = (TYPE = CSV, FIELD_DELIMITER = AUTO)' to automatically detect delimiters.

Answer: C,E

Explanation:
Option A addresses the inconsistent delimiter issue directly. The FIELD DELIMITER = AUTO' option automatically detects the delimiter. Option D is crucial for standardizing the date formats, handling missing values, and deduplicating records, ensuring data quality. Option B while useful for validation, is not helpful at scale and doesn't solve all problems listed. Option C is too aggressive; skipping entire files might lead to data loss. Option E while somewhat useful, is not as efficient as the other options.

NEW QUESTION # 249
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.

A. None of the above.
B.
C.
D.
E.

Answer: C

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 # 250
You are building a dashboard in Power BI that connects to Snowflake. The dashboard needs to display the trend of daily active users (DAU) for the past year. The 'USER_ACTIVITY table in Snowflake contains columns: 'USER ONT), 'ACTIVITY DATE (DATE), and 'ACTIVITY TYPE (VARCHAR). Due to the large size of the 'USER ACTIVITY table, query performance is critical. Which of the following strategies will BEST optimize the query executed by Power BI against Snowflake to calculate DAU?

A. Directly query the 'USER_ACTIVITY table from Power BI using a DAX measure to calculate distinct user counts per day. Rely on Power BI'S query folding capabilities to optimize the query sent to Snowflake.
B. Create a materialized view in Snowflake that pre-calculates the DAU for each day, then connect Power BI to this materialized view.
C. Create a Snowflake view that calculates the DAU for each day using 'COUNT(DISTINCT USER_ID)' and 'GROUP BY ACTIVITY_DATE' , then connect Power BI to this view.
D. Using a scheduled task in Snowflake, regularly create a summary table containing daily active users, then connect Power BI to that summary table.
E. Import the entire 'USER ACTIVITY table into Power BI using Power BI Desktop's data import functionality and calculate DAU within Power BI'S data model.

Answer: B

Explanation:
Creating a materialized view that pre-calculates the DAU for each day is the most effective approach for optimizing query performance. Materialized views store the results of the query, so Power BI only needs to retrieve the pre-calculated DAU values, avoiding the expensive 'COUNT(DISTINCTY operation on the entire 'USER_ACTIVITY table. Option A might not fold completely and still execute poorly. Option B is better than A but does not provide performance like a Materialized View. Option D brings all the data into Power 31, which is not scalable or efficient. Option E is a valid option but has additional management overhead compared to materialized views.

NEW QUESTION # 251
You are performing an UPDATE operation on a large table 'CUSTOMER ORDERS with millions of rows. The update logic involves complex calculations based on data from another table 'PRODUCT PRICES. To minimize the impact on concurrent queries and ensure data consistency, which of the following strategies should you implement?

A. Execute the UPDATE statement directly without any special considerations, relying on Snowflake's default concurrency control.
B. Create a new version of table with updated data and switch to new version.
C. Break the UPDATE operation into smaller batches using a WHERE clause with a limiting condition based on a primary key or date range and commit changes after each batch.
D. Use a MERGE statement to update the 'CUSTOMER ORDERS' table based on the data from the 'PRODUCT PRICES' table.
E. Create a temporary table with the updated values, drop the original table, and rename the temporary table to the original table name.

Answer: C,D

Explanation:
Breaking the UPDATE into smaller batches minimizes lock contention and reduces the duration of the update transaction, thereby reducing the impact on concurrent queries. A MERGE statement is an efficient way to perform updates based on data from another table, optimizing the process compared to a simple UPDATE statement. Option A is not recommended for large tables due to potential lock contention. Option C is disruptive and might cause issues if concurrent queries are running against the table. Option E is not a standard approach.

NEW QUESTION # 252
......

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Snowflake DAA-C01 Books PDF & DAA-C01 New Braindumps Files

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