Free Snowflake DEA-C02 Exam Dumps Questions & Answers

You are tasked with ingesting data from an external stage into Snowflake. The data is in JSON format and compressed using GZIP. The JSON files contain nested arrays. You need to create a file format object that Snowflake can use to properly parse the dat a. Which of the following options represents the MOST efficient and correct file format definition to achieve this? Assume the stage is already created and accessible.

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

You are using Snowpark Python to perform a complex data transformation involving multiple tables and several intermediate dataframes. During the transformation, an error occurs within one of the Snowpark functions, causing the entire process to halt. To ensure data consistency, you need to implement transaction management. Which of the following Snowpark DataFrameWriter options or session configurations would be MOST appropriate for rolling back the entire transformation in case of an error during the write operation to the final target table?

• A.  Set the session parameter to 'TRUE to ensure all DDL operations are atomic and can be rolled back.
• B.  Set the session parameter to 'TRUE and wrap the entire transformation within a 'try...except block, explicitly calling in the 'excepts block.
• C.  Wrap the entire transformation in a stored procedure and call 'SYSTEM$QUERY within the stored procedure's exception handler.
• D.  Use True)' to automatically rollback the write operation if an error occurs during the write process.
• E.  Use and manually track intermediate dataframes to delete them in case of failure.

You are tasked with creating a system to monitor the data quality of a 'SALES DATA" table. The table is updated daily with new sales records, and you need to ensure that the 'SALE AMOUNT column always contains positive values. You decide to use Snowflake tasks and streams for this purpose. Consider the following Snowflake script. What is the most appropriate way to modify the 'SALES DATA' table so the task has a 'WHEN' clause that runs only if the 'SALE AMOUNT has negative values? Assume the stream 'SALES DATA STREAM' is properly configured on 'SALES DATA'.

• A. 
• B. 
• C. 
• D. 
• E. 

A large e-commerce company is experiencing performance issues with its daily sales report queries. These queries aggregate data from a fact table 'SALES FACT (100 billion rows) and several dimension tables, including 'CUSTOMER DIM', 'PRODUCT DIM', and 'DATE DIM'. The queries are run every morning and are essential for business decision-making. The team has identified that the 'SALES FACT table's primary key is 'SALE ID, but the queries frequently filter and join on 'CUSTOMER and 'PRODUCT ID. You want to use query acceleration service for these reports without changing query logic. Which combination of actions will MOST effectively leverage query acceleration service, assuming sufficient credits?

• A.  Enable clustering on the 'CUSTOMER DIM' and 'PRODUCT DIMS tables.
• B.  Create materialized views that pre-aggregate the sales data based on 'CUSTOMER ID', 'PRODUCT ID, and 'DATE ID, then enable query acceleration on the virtual warehouse.
• C.  Enable search optimization on the columns 'CUSTOMER ID' and 'PRODUCT ID of the 'SALES FACT table, then enable query acceleration on the virtual warehouse. Set the QUERY_ACCELERATION_MAX_SCALE_FACTOR parameter to a reasonable value based on testing.
• D.  Increase the size of the virtual warehouse used for running the reports and enable query acceleration. Set the parameter to a high value.
• E.  Enable Automatic Clustering on the 'SALES FACT table based on 'CUSTOMER ID' and 'PRODUCT ID, then enable query acceleration on the virtual warehouse.

You are configuring a Snowflake Data Clean Room for two healthcare providers, 'ProviderA' and 'ProviderB', to analyze patient overlap without revealing Personally Identifiable Information (PII). Both providers have patient data in their respective Snowflake accounts, including a 'PATIENT ID' column that uniquely identifies each patient. You need to create a secure join that allows the providers to determine the number of shared patients while protecting the raw 'PATIENT ID' values. Which of the following approaches is the most secure and efficient way to achieve this using Snowflake features? Select TWO options.

• A.  Implement tokenization of the 'PATIENT_ID' column in both ProviderA's and ProviderB's accounts. Share the tokenized values through a secure view and perform a JOIN operation on the tokens. Use a third party to deanonymize the tokens afterwards.
• B.  Share the raw 'PATIENT_ID' columns between ProviderA and ProviderB using secure data sharing, and then perform a JOIN operation in either ProviderA's or ProviderB's account.
• C.  Utilize Snowflake's Secure Aggregate functions (e.g., APPROX_COUNT_DISTINCT) on the 'PATIENT_ID' column without sharing the underlying data. Each provider calculates the approximate distinct count of patient IDs, and the results are compared to estimate the overlap.
• D.  Create a hash of the 'PATIENT_ID' column in both ProviderA's and ProviderB's accounts using a consistent hashing algorithm (e.g., SHA256) and a secret salt known only to both providers. Share the hashed values through a secure view and perform a JOIN operation on the hashed values.
• E.  Leverage Snowflake's differential privacy features to add noise to the patient ID data, share the modified dataset and perform a JOIN.