DEA-C02 Practice Test 4 — 10 Questions

Question 1

A user runs a query twice in succession with no data changes. The second query completes in near-zero time. What feature is responsible for this performance?

Accepted Answer

Query Result Cache.. The Query Result Cache stores the output of every query for a rolling 24-hour period. When a subsequent query matches the exact SQL text and the underlying data in the source tables has not changed, Snowflake returns the result from the cache. This mechanism avoids the need to spin up compute resources or scan data, providing an instantaneous response for repeated queries, which is a major efficiency feature for frequently accessed reports.

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Warehouse Local Cache.

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Metadata Cache.

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Search Optimization Service.

Question 2

What is the primary benefit of using a file format object in Snowflake when dealing with multiple stages and load jobs?

Accepted Answer

It centralizes file configuration to ensure consistency across multiple load jobs.. File format objects provide a centralized, reusable definition of file settings (like delimiter, compression, or error handling). By referencing a single object in multiple COPY commands, an engineer ensures consistency across the entire data pipeline. This eliminates drift and makes it easy to update the configuration for all load jobs at once, reducing maintenance and preventing errors associated with manually defining parameters repeatedly across many different SQL statements.

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It automatically compresses data before it is uploaded to the cloud.

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It encrypts the data at rest in the stage before loading.

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It stores the credentials for the external stages for easier access.

Question 3

A data engineer is tuning a query that joins two large tables. The query is performing a 'Remote Disk Spilling' operation. Which optimization strategy is most effective to resolve this?

Accepted Answer

Scale up the warehouse to a larger size.. Remote disk spilling occurs when the memory allocated to the virtual warehouse is insufficient to hold the intermediate result sets of a join or aggregation. By increasing the size of the warehouse, the memory available per node doubles, allowing larger datasets to be processed entirely in-memory. This significantly reduces latency associated with I/O operations and speeds up complex join operations that frequently cause spilling in standard configurations.

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Increase the multi-cluster warehouse scaling policy to 'Maximized'.

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Enable Result Cache by setting USE_CACHED_RESULT to TRUE.

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Implement a materialized view on the joined columns.

Question 4

Which approach is most efficient for transforming a large volume of data in Snowflake when the logic requires complex window functions and stateful processing?

Accepted Answer

Using set-based SQL transformations in a materialized view or dynamic table.. For large-scale, complex transformations, using SQL within a View, Dynamic Table, or a CTAS (Create Table As Select) operation is highly efficient because it runs directly on the Snowflake compute engine. Snowflake's query optimizer is highly tuned for window functions and complex joins, allowing it to distribute these operations across all nodes in the warehouse, providing superior performance compared to row-by-row procedural processing.

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Using a Python script to fetch data, process locally, and upload.

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Using a single Stored Procedure with row-by-row cursor processing.

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Using a User-Defined Function (UDF) for every column transformation.

Question 5

An organization is unloading data from a Snowflake table to an external S3 bucket for use in a machine learning pipeline. Which TWO practices will optimize the performance and manageability of the exported files? (Select TWO)

Accepted Answer

Use the PARTITION BY clause in the COPY INTO <location> statement to organize files into subfolders.. Optimizing data unloading requires balancing file size and organizational structure. Using the PARTITION BY clause ensures that the exported files are organized into a logical folder hierarchy, which facilitates faster data discovery for downstream tools. Additionally, controlling the MAX_FILE_SIZE ensures that the files are not too large for the consuming application to process efficiently in memory while maintaining parallelism.

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Set the SINGLE parameter to TRUE to ensure all data is consolidated into one large file.

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Disable compression using the COMPRESSION = NONE parameter to reduce the CPU load on the warehouse.

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Always use the OVERWRITE = TRUE parameter to prevent the command from failing if files already exist.

Question 6

Which approach is most effective for managing governance policies across a large, multi-schema data warehouse environment?

Accepted Answer

Deploy policies to a centralized database and schema.. Centralizing governance objects in a dedicated 'GOVERNANCE' database is the best practice. By keeping policies, tags, and data classification results in a single, well-controlled schema, organizations ensure consistency and ease of maintenance. This centralized approach simplifies access control for the security team and allows for easier auditing of policy changes compared to scattering governance artifacts across disparate, business-specific databases or schemas.

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Create policies within each individual schema.

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Use a single shared role for all policy management.

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Manually recreate all policies in every environment.

Question 7

Which Snowflake feature should be used to move data from a private cloud network to Snowflake without traversing the public internet?

Accepted Answer

AWS PrivateLink or Azure Private Link.. For enterprises with strict security requirements, using AWS PrivateLink or Azure Private Link allows for a secure, private connection between the cloud network and Snowflake. This is vital for data movement in highly regulated industries. By eliminating the need for public internet exposure, organizations minimize their attack surface and comply with networking policies, ensuring that sensitive data remains within a controlled, private infrastructure environment throughout the entire transit process.

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SnowSQL with VPN tunneling.

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Snowflake Data Exchange.

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Snowpipe with an external stage.

Question 8

What is the primary purpose of the 'VALIDATION_MODE' parameter in the COPY INTO <table_name> statement?

Accepted Answer

To check the files for errors and return the results without loading the data into the table.. The VALIDATION_MODE parameter allows data engineers to test their COPY commands without actually committing any data to the table. It parses the files and identifies errors, which is a critical step in data movement pipelines to ensure that file formats and mappings are correct before performing a potentially expensive or large-scale ingestion.

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To automatically correct data type mismatches during the load process.

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To verify that the person executing the command has the correct IAM permissions on the S3 bucket.

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To compare the data in the stage with the data already in the table to find duplicates.

Question 9

Which TWO of the following are true regarding the use of Snowflake Data Classification?

Accepted Answer

The process uses system-defined tags to label sensitive data.. Snowflake Data Classification automatically identifies PII and sensitive data within tables, assigning system tags to columns. This process significantly reduces the manual effort required for governance. It works by scanning sample data and applying semantic labels, which can then be used to trigger automated protection measures like masking policies. Understanding how this automates compliance is essential for any modern data engineer managing large, dynamic datasets.

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Data classification can only be applied to existing tables.

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Classification results are stored in the user's local file system.

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Classification requires the data to be in a flat file format.

Question 10

A data engineer is reviewing a query profile for a query that performs a large join. The profile shows a high percentage of time spent in the 'Join' node with significant 'Bytes spilled to local storage'. The engineer wants to reduce local spilling without changing the query. Which action is most appropriate?

Accepted Answer

Increase the warehouse size to provide more memory for the join operation.. Local spilling during a join indicates that the join operation is exceeding the memory available on a warehouse node. Increasing the warehouse size provides more memory per node, which can accommodate larger intermediate results and reduce spilling. Filtering, clustering, or changing join types may have indirect benefits but do not directly resolve the memory pressure. Therefore, scaling up the warehouse is the most appropriate action.

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Reduce the size of the smaller table in the join by filtering it.

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Add a clustering key on the join column of the larger table.

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Change the join type from a hash join to a sort-merge join.