Pass the actual test with the help of DEA-C02 study guide
Last Updated: Aug 19, 2026
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| Section | Objectives |
|---|---|
| Topic 1: Security and Data Governance | - Role-based access control (RBAC) - Data masking and encryption - Secure data sharing |
| Topic 2: Data Ingestion and Integration | - Snowpipe usage and automation - Staging data and loading mechanisms - Batch and streaming ingestion approaches |
| Topic 3: Data Transformation and Processing | - Handling semi-structured data (JSON, Avro, Parquet) - Streams and Tasks for ELT pipelines - SQL-based transformations in Snowflake |
| Topic 4: Performance and Optimization | - Warehouse sizing and scaling - Query optimization techniques - Clustering and partition strategies |
| Topic 5: Data Engineering Fundamentals | - Data pipelines concepts and patterns - Snowflake architecture for data engineering |
1. 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?
A) Examine the data load process to ensure the data is loaded in 'TRANSACTION_DATE order. If not, sort the data by 'TRANSACTION_DATE before loading.
B) Implement data skipping by creating a masking policy on the 'TRANSACTION_DATE column.
C) Re-create the 'TRANSACTIONS' table with a larger virtual warehouse and re-load the entire dataset.
D) Partition the data in the source system by 'TRANSACTION DATE' and load data in parallel corresponding to each partition.
E) Enable automatic clustering on the 'TRANSACTION_DATE column of the 'TRANSACTIONS table.
2. You are tasked with implementing data masking on a 'CUSTOMER' table. The requirement is to mask the 'EMAIL' column for all users except those with the 'DATA ADMIN' role. You have the following code snippet. What is wrong with it?
A) There is no code provided, so there is nothing wrong with it.
B) Without masking poliy code, it's impossible to determine if there is anything wrong.
C) The masking policy is applied to the wrong column. It should be applied to the ID column, not the EMAIL column.
D) The masking policy syntax is incorrect. It should use 'CASE WHEN IS_ROLE_IN_SESSION('DATA_ADMIN') THEN EMAIL ELSE '[email protected]' END'.
E) The WITH clause is unneccessary.
3. You are implementing a data pipeline in Snowpark that reads data from an external stage (e.g., AWS S3) and performs complex transformations, including joins with large Snowflake tables. You notice that the pipeline's performance is significantly slower than expected, despite having sufficient warehouse resources. Which of the following actions would MOST likely improve the performance of the Snowpark data pipeline?
A) Persist the DataFrame representing the data from the external stage using 'df.cache()' before performing the joins.
B) Optimize the SQL joins within the Snowpark DataFrame operations by using broadcast joins when appropriate and ensuring correct join key data types.
C) Ensure that the external stage is properly configured with appropriate data formats (e.g., Parquet) and partitioning schemes that align with the join keys.
D) Increase the warehouse size to the largest available option (e.g., X-Large or larger).
E) Reduce the number of partitions in the DataFrame representing the data from the external stage using 'df.repartition(l )'.
4. You are tasked with implementing a data recovery strategy for a critical table 'SALES DATA' in Snowflake. The table is frequently updated, and you need to ensure you can recover to a specific point in time in case of accidental data corruption. Which approach provides the most efficient and granular recovery option, minimizing downtime and data loss? Consider performance and storage implications of each method.
A) Relying solely on Snowflake's Time Travel feature with the default data retention period.
B) Create a scheduled task that takes a snapshot of the sales data and store it to an external staging location.
C) Using the 'UNDROP TABLE command in conjunction with the 'AT' clause to revert the table to a previous state.
D) Creating a Snowflake Stream on 'SALES_DATR and capturing all DML changes for point-in-time recovery.
E) Regularly creating full clones of the 'SALES_DATR table to a separate database.
5. You are ingesting data from an external stage (AWS S3) into a Snowflake table using Snowpipe. Data files are continuously being uploaded to the stage. After several hours, you notice that some data files are not being loaded. You check the Snowpipe error notifications and see 'net.snowflake.ingest.errors.FileSizeLimitExceededError'. You have already verified that the Snowpipe is correctly configured and the user has the necessary permissions. What are the MOST LIKELY reasons for this error and how can you resolve them?
A) The Snowpipe is encountering a transient network error. Reset the pipe using ALTER PIPE REFRESH;
B) The size of the data files in the stage exceeds the maximum allowed size for Snowpipe. Split the large files into smaller files before uploading to the stage.
C) The data files are being uploaded to the stage faster than Snowpipe can process them. Increase the value of the 'MAX CONCURRENCY parameter in the Snowpipe definition.
D) Snowflake has reached its maximum allowable data storage capacity. Increase your Snowflake storage capacity to resolve this issue.
E) The Snowpipe configuration is incorrect; specifically, the 'FILE FORMAT parameter is not correctly specified to handle the file type. Reconfigure the Snowpipe with the correct 'FILE FORMAT.
Solutions:
| Question # 1 Answer: A,E | Question # 2 Answer: B | Question # 3 Answer: A,B,C | Question # 4 Answer: D | Question # 5 Answer: B |
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