A Data Engineer enables a result cache at the session level with the following command: ALTER SESSION SET USE CACHED RESULT = TRUE;
The Engineer then runs the following select query twice without delay:

The underlying table does not change between executions What are the results of both runs?

1. A. The first and second run returned the same results because sample is deterministic
2. B. The first and second run returned the same results, because the specific SEEDvalue was provided.
3. C. The first and second run returned different results because the query is evaluated each time it is run.
4. D. The first and second run returned differentresults because the query uses *instead of an explicit column list

Correct Answer: B
The result cache is enabled at the session level, which means that repeated queries will return cached results if there is no change in the underlying data or session parameters. However, in this case, the result cache is not relevant because the query uses a specific SEED value for sampling, which makes it deterministic. Therefore, both runs will return the same results regardless of caching.

Which methods can be used to create a DataFrame object in Snowpark? (Select THREE)

1. A. session.jdbc_connection()
2. B. session.read.json{)
3. C. session,table()
4. D. DataFraas.writeO
5. E. session.builder()
6. F. session.sql()

Correct Answer: BCF
The methods that can be used to create a DataFrame object in Snowpark are session.read.json(), session.table(), and session.sql(). These methods can create a DataFrame from different sources, such as JSON files, Snowflake tables, or SQL queries.
The other options are not methods that can create a DataFrame object in Snowpark. Option A, session.jdbc_connection(), is a method that can create a JDBC connection object to connect to a database. Option D, DataFrame.write(), is a method that can write a DataFrame to a destination, such as a file or a table. Option E, session.builder(), is a method that can create a SessionBuilder object to configure and build a Snowpark session.

Which functions will compute a 'fingerprint' over an entire table, query result, or window to quickly detect changes to table contents or query results? (Select TWO).

1. A. HASH (*)
2. B. HASH_AGG(*)
3. C. HASH_AGG(<expr>, <expr>)
4. D. HASH_AGG_COMPARE (*)
5. E. HASH COMPARE(*)

Correct Answer: BC
The functions that will compute a ‘fingerprint’ over an entire table, query result, or window to quickly detect changes to table contents or query results are:
✑ HASH_AGG(*): This function computes a hash value over all columns and rows in
a table, query result, or window. The function returns a single value for each group defined by a GROUP BY clause, or a single value for the entire input if no GROUP BY clause is specified.
✑ HASH_AGG(<expr>, <expr>): This function computes a hash value over two
expressions in a table, query result, or window. The function returns a single value for each group defined by a GROUP BY clause, or a single value for the entire input if no GROUP BY clause is specified. The other functions are not correct because:
✑ HASH (*): This function computes a hash value over all columns in a single row.
The function returns one value per row, not one value per table, query result, or window.
✑ HASH_AGG_COMPARE (): This function compares two hash values computed by
HASH_AGG() over two tables or query results and returns true if they are equal or false if they are different. The function does not compute a hash value itself, but rather compares two existing hash values.
✑ HASH COMPARE(): This function compares two hash values computed by
HASH() over two rows and returns true if they are equal or false if they are different. The function does not compute a hash value itself, but rather compares two existing hash values.

A company built a sales reporting system with Python, connecting to Snowflake using the Python Connector. Based on the user's selections, the system generates the SQL queries needed to fetch the data for the report First it gets the customers that meet the given query parameters (on average 1000 customer records for each report run) and then it loops the customer records sequentially Inside that loop it runs the generated SQL clause for the current customer to get the detailed data for that customer number from the sales data table
When the Data Engineer tested the individual SQL clauses they were fast enough (1 second to get the customers 0 5 second to get the sales data for one customer) but the total runtime of the report is too long
How can this situation be improved?

1. A. Increase the size of the virtual warehouse
2. B. Increase the number of maximum clusters of the virtual warehouse
3. C. Define a clustering key for the sales data table
4. D. Rewrite the report to eliminate the use of the loop construct

Correct Answer: D
This option is the best way to improve the situation, as using a loop construct to run SQL queries for each customer is very inefficient and slow. Instead, the report should be rewritten to use a single SQL query that joins the customer and sales data tables and applies the query parameters as filters. This way, the report can leverage Snowflake’s parallel processing and optimization capabilities and reduce the network overhead and latency.

What kind of Snowflake integration is required when defining an external function in Snowflake?

1. A. API integration
2. B. HTTP integration
3. C. Notification integration
4. D. Security integration

Correct Answer: A
An API integration is required when defining an external function in Snowflake. An API integration is a Snowflake object that defines how Snowflake communicates with an externalservice via HTTPS requests and responses. An API integration specifies parameters such as URL, authentication method, encryption settings, request headers, and timeout values. An API integration is used to create an external function object that invokes the external service from within SQL queries.