2026 New DEA-C02 Exam Questions Real Snowflake Dumps [Q24-Q45]

Q24. You are tasked with loading data from a set of highly nested JSON files into Snowflake. Some files contain an inconsistent structure where a particular field might be a string in some records and an object in others. You want to avoid data loss and ensure that you capture both string and object representations of the field. What is the most efficient approach to achieve this, minimizing data transformation outside of Snowflake?

Q25. A data warehousing team is experiencing inconsistent query performance on a large fact table C SALES FACT) that is updated daily. Some queries involving complex joins and aggregations take significantly longer to execute than others, even when run with the same virtual warehouse size. You suspect that the query result cache is not being effectively utilized due to variations in query syntax and the dynamic nature of the data’. Which of the following strategies could you implement to maximize the effectiveness of the query result cache and improve query performance consistency? Assume virtual warehouse size is large and the data is skewed across days.

Q26. You have configured a replication group to replicate a database ‘CUSTOMER DATA’ from your primary Snowflake account (AWS us- east-I) to your secondary Snowflake account (Azure eastus). After a recent network outage, the replication process stopped. Upon investigation, you find that some tables in the ‘CUSTOMER DATA’ database in the primary account have been modified (schema changes). You need to resume replication and ensure data consistency in the secondary account, with minimal impact on users querying the secondary database. Which of the following commands or sequence of commands would be the MOST appropriate to refresh the secondary database while minimizing downtime for query users?

Q27. A data engineer is tasked with creating an external table that points to a directory in AWS S3 containing CSV files. The files have a header row and are comma-delimited. The engineer executes the following DDL statement:

Q28. A data engineer is responsible for maintaining a Snowflake data warehouse. They notice a significant slowdown in the performance of a specific query that aggregates data from a table called ‘SALES DATA’, which contains billions of rows. The query is used for generating daily sales reports. The engineer suspects that the issue might be related to clustering. How would you diagnose the effectiveness of the clustering on the ‘SALES DATA’ table and identify potential improvements?

Q29. You are designing a data sharing solution for a multi-tenant application where each tenant’s data must be isolated. You have a ‘sales’ table with a ‘tenant_id’ column. You need to implement row-level security to ensure that each tenant can only access their own data when querying the shared table. Which of the following approaches, considering performance and security, is the MOST suitable for implementing this row-level filtering in Snowflake?

Q30. You are working with a directory table named associated with an external stage containing a large number of small JSON files. You need to process only the files containing specific sensor readings based on a substring match within their filenames (e.g., files containing ‘temperature’ in the filename). You also want to load these files into a Snowflake table ‘sensor_readings. Consider performance and cost-effectiveness. Which of the following approaches is the MOST efficient and cost-effective to achieve this? Choose TWO options.

Q31. A healthcare provider wants to share patient data with a research organization, but must ensure that researchers only have access to records from a specific region (‘REGION A’) and only see anonymized data’. You have a ‘patients’ table with columns ‘patient_id’ , ‘region’, ‘dob’, ‘medical history’, and ‘ssn’. Which of the following steps would be MOST effective and secure for implementing row- level filtering and data masking for this data sharing scenario, minimizing administrative overhead and maximizing query performance?

Q32. A data engineering team is loading a large fact table ‘SALES DATA’ daily, partitioned by ‘SALE DATE. After several months, query performance degrades significantly. An analyst reports that queries filtering on ‘CUSTOMER are slow, despite ‘CUSTOMER ID’ having high cardinality. The table definition is as follows: CREATE TABLE SALES_DATA ( SALE DATE DATE NOT NULL, CUSTOMER_ID NUMBER NOT NULL, PRODUCT ID NUMBER NOT NULL, SALE_AMOUNT … Which of the following actions would BEST improve query performance for queries filtering on ‘CUSTOMER ID’, considering the existing partitioning by ‘SALE DATE’?

Q33. You are designing a Snowpark Python application to process streaming data from a Kafka topic and land it into a Snowflake table ‘STREAMED DATA. Due to the nature of streaming data, you want to achieve the following: 1. Minimize latency between data arrival and data availability in Snowflake. 2. Ensure exactly-once processing semantics to prevent data duplication. 3. Handle potential schema evolution in the Kafka topic without breaking the pipeline. Which combination of Snowpark and Snowflake features, applied with the correct configuration, would BEST satisfy these requirements? Select all that apply.

Q34. You have a table ‘SALES DATA’ in your production environment. You want to create a development environment using cloning, but only want to include data up to a specific point in time to minimize storage costs and potential exposure of recent, sensitive data’. You know there were significant changes to the ‘SALES DATA’ table structure on ‘2024-01-15’. Your goal is to create a clone that only includes the structure as of ‘2024-01-14’. Which Snowflake command is MOST appropriate for this scenario?

Q35. You are monitoring a Snowpipe pipeline that loads data from an external stage into a Snowflake table. You observe the following error messages in the PIPE ERRORS view: ‘Invalid UTF-8 detected in string’. The data files on the stage are encoded in UTF-8. Which of the following actions, taken individually or in combination, are MOST likely to resolve this issue? (Select TWO)

Q36. You’re managing a Snowflake data warehouse and need to create a development environment for testing a complex stored procedure that updates a critical table, ‘SALES DATA’. The procedure is located in the ‘PRODUCTION’ database and you want to ensure minimal impact to the production environment during development. You decide to use cloning and time travel. Which of the following strategies is the MOST efficient and safest approach to achieve this, minimizing downtime and resource consumption in production?

Q37. You are developing a Python script to perform bulk data updates in a Snowflake table. The script needs to update a large number of rows based on values from a Pandas DataFrame. Which of the following approaches is the most efficient and scalable way to achieve this using the Snowflake Python connector, minimizing the number of database operations?

Q38. You have a VARIANT column named ‘raw_data’ in a Snowflake table ‘eventS , containing nested JSON data’. You need to extract specific fields Cevent_id’, ‘timestamp’ , and ‘user.user_id’) and load them into a relational table ‘structured_events’ with columns ‘event_id’ , ‘timestamp’ , and ‘user_id’, respectively. However, some entries may be missing the ‘user’ object. Which of the following SQL statements will achieve this while handling missing ‘user’ objects gracefully and ensuring data integrity, and also efficiently handle potentially large JSON payloads?

Q39. You are developing a data pipeline that extracts data from an on-premise PostgreSQL database, transforms it, and loads it into Snowflake. You want to use the Snowflake Python connector in conjunction with a secure method for accessing the PostgreSQL database. Which of the following approaches provides the MOST secure and manageable way to handle the PostgreSQL connection credentials in your Python script when deploying to a production environment?

Q40. Consider the following scenario: You are ingesting JSON data from an external stage into Snowflake. The JSON data contains an array of objects, where each object represents a product with attributes like ‘product id’, ‘name’, and ‘price’. However, sometimes the ‘price’ field is missing entirely from some product objects. You want to load this data into a Snowflake table with columns ‘product_id’, ‘name’, and ‘price’ (defined as NUMBER). How can you handle the missing ‘price’ field gracefully during the COPY INTO operation, ensuring that missing prices are represented as NULL in the Snowflake table without causing errors?

Q41. You are setting up a Kafka connector to load data from a Kafka topic into a Snowflake table. You want to use Snowflake’s automatic schema evolution feature to handle potential schema changes in the Kafka topic. Which of the following is the correct approach to enable and configure automatic schema evolution using the Kafka Connector for Snowflake?

Q42. A Snowflake data warehouse contains a table named ‘SALES TRANSACTIONS’ with the following columns: ‘TRANSACTION ID’, ‘PRODUCT D’, ‘CUSTOMER D’, ‘TRANSACTION DATE, and ‘SALES AMOUNT’. You need to optimize a query that calculates the total sales amount per product for a given month. The ‘SALES TRANSACTIONS’ table is very large (billions of rows), and queries are slow. Given the following initial query: SELECT PRODUCT ID, SUM(SALES AMOUNT) AS TOTAL SALES FROM SALES TRANSACTIONS WHERE TRANSACTION DATE BETWEEN ‘2023-01-07’ AND ‘2023-01-31’ GäOUP BY PRODUCT ID; Which of the following actions, when combined, would MOST effectively improve the performance of this query?

Q43. You’re designing a Snowpark Scala stored procedure that must execute a series of complex data quality checks on a Snowflake table.
These checks involve multiple steps, including validating data types, checking for null values, and verifying data consistency against external reference data’. You want to ensure that the stored procedure is resilient to errors, provides detailed logging, and can be easily monitored. Which of the following approaches would be the MOST robust and scalable for handling errors and logging within this Snowpark Scala stored procedure?

Q44. A data engineer notices that a daily ETL job loading data into a Snowflake table ‘TRANSACTIONS’ is consistently taking longer than expected. The table is append-only and partitioned by ‘TRANSACTION DATE. The engineer observes high ‘Remote Spill’ during the load process and suspect that micro-partition pruning isn’t working effectively. Which of the following approaches would BEST address the performance issue, assuming you have already considered increasing warehouse size?

Q45. You’re tasked with optimizing a Snowflake data pipeline that transforms and loads data into a target table. The pipeline uses a series of complex SQL queries with multiple joins and aggregations. After analyzing the query execution plans, you identify a few key bottlenecks. Which of the following optimization techniques would MOST directly address common performance bottlenecks in such a data pipeline within Snowflake?