DEA-C02 Practice Exam 2 — 20 Questions

Question 1

A company requires continuous ingestion of JSON logs from an S3 bucket into a Snowflake table with minimal latency. They decide to use Snowpipe with auto-ingest. How does Snowflake determine which new files need to be processed once the pipe is created?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

It relies on event notifications from S3 sent to a Snowflake-managed SQS queue to trigger the pipe.. Snowpipe with auto-ingest relies on cloud-native messaging services to notify Snowflake of new data. When a file is uploaded to S3, an S3 Event Notification is triggered, which sends a message to a Snowflake-managed SQS queue. Snowpipe constantly monitors this queue and triggers the ingestion process as soon as it receives a notification, ensuring near real-time data movement without manual intervention.

Answer

Snowflake performs a metadata scan of the S3 bucket every 60 seconds to identify files with new timestamps.

Answer

The Snowpipe object uses the LIST command internally to compare the stage contents against the load history table.

Answer

The data engineer must execute the ALTER PIPE... REFRESH command every time new data is uploaded to the stage.

Question 2

A data engineer must load semi-structured JSON from an external stage where each file contains a top-level array of objects. The engineer wants each object in the array to become one row, with each object's keys exposed as columns. Which file format option should be configured?

Accepted Answer

STRIP_OUTER_ARRAY = TRUE. When JSON files contain a top-level array, Snowflake by default loads the entire array as one row. Setting STRIP_OUTER_ARRAY to TRUE in the JSON file format removes the outer brackets so each element of the array is parsed as an individual row. This aligns each object with a table row while preserving the object keys as columns or variant fields.

Answer

STRIP_NULL_VALUES = TRUE

Answer

REPLACE_INVALID_CHARACTERS = TRUE

Answer

SKIP_HEADER = 1

Question 3

A data engineer must unload query results from a Snowflake table into a named internal stage so another team can download the files. The engineer wants the unloaded data to be encodable in a columnar format. Which command should be used?

Accepted Answer

COPY INTO @my_stage/path/ FROM my_table FILE_FORMAT = (TYPE = PARQUET);. COPY INTO targeting a stage is the unload mechanism, and pairing it with TYPE = PARQUET produces columnar files in the named internal stage. GET and PUT move files between local storage and stages, and CREATE STAGE only defines the object, so none of those perform the required table-to-stage unload.

Answer

PUT file://local_data.csv @my_stage/path/ AUTO_COMPRESS = TRUE;

Answer

CREATE STAGE my_stage FILE_FORMAT = (TYPE = PARQUET);

Answer

GET @my_stage/path/ file://local_path;

Question 4

What is the effect of the UNDROP command on a table that has already been recreated with the same name?

Accepted Answer

The UNDROP command fails due to a name collision.. If a table is dropped and a new table is created with the same name, the UNDROP operation will encounter a name conflict. In such cases, the UNDROP command fails, as the target namespace is already occupied. To recover the dropped table, the user must first rename or drop the new table, then execute UNDROP on the original. This safety mechanism prevents data loss and accidental overwriting of active production tables.

Answer

It automatically renames the dropped table with a suffix.

Answer

It overwrites the existing table with the old version.

Answer

It merges the data from both tables.

Question 5

A data engineer needs to ensure that PII data in the 'SALES' table is obscured for non-admin users while maintaining original data types for downstream analytical models. Which approach provides the most scalable governance?

Accepted Answer

Apply a masking policy to the columns and grant the APPLY MASKING POLICY privilege.. Dynamic Data Masking allows policies to be applied to columns based on the user's role without duplicating data. By leveraging masking policies, you maintain a single source of truth while ensuring sensitive information is protected at query runtime. This method is highly scalable as a single policy can be assigned to multiple columns across different tables, simplifying maintenance and ensuring consistent security posture across the enterprise.

Answer

Create separate secure views for every user role requiring masked access.

Answer

Use row-level security to filter rows containing PII data for authorized users.

Answer

Physically transform the data during the ETL process and store it in a new table.

Question 6

A query is filtering a table based on a 'TRANSACTION_DATE' column using a range (e.g., BETWEEN '2023-01-01' AND '2023-01-31'). The table is 500GB and not explicitly clustered. Why might this query still perform well and show good partition pruning?

Accepted Answer

The data was inserted in chronological order, creating natural clustering.. Snowflake micro-partitions are immutable and created in the order data is inserted. If the data is naturally loaded in chronological order, the 'TRANSACTION_DATE' values will be naturally clustered within the micro-partitions. This 'natural clustering' allows the metadata-driven pruning to skip partitions that fall outside the date range, even without an explicit clustering key.

Answer

The Result Cache is automatically storing the range for all users.

Answer

Snowflake automatically clusters all Date columns by default.

Answer

The query is small enough to fit entirely in the warehouse's metadata cache.

Question 7

Which TWO statements describe the benefits of using Snowflake's 'Change Tracking' for data transformation?

Accepted Answer

It allows for efficient incremental processing of only modified data.. Change Tracking is the underlying mechanism that enables Streams. It allows Snowflake to record which rows were inserted, updated, or deleted, making incremental transformations possible. Instead of scanning entire tables to identify changes, the engine can simply query the stream, which significantly reduces compute time and costs for ETL processes. This is critical for high-frequency data loading and incremental updates in modern data warehouses.

Answer

It eliminates the need for primary keys on tables.

Answer

It automatically cleans up stale data in the target table.

Answer

It is only available for tables in the Enterprise edition.

Question 8

A data engineer needs to identify all columns across a multi-database Snowflake account that have been assigned the 'PII_Type' tag to ensure compliance with a new privacy regulation. Which approach provides the most comprehensive and efficient result for this account-level audit?

Accepted Answer

Query the TAG_REFERENCES view within the SNOWFLAKE.ACCOUNT_USAGE schema.. Snowflake object tagging allows for fine-grained metadata management across the account. Using the ACCOUNT_USAGE.TAG_REFERENCES view is the most efficient way to track PII tags globally, as it aggregates data from all databases. This centralized approach ensures that data engineers can maintain compliance and auditability without needing to query individual database schemas, which is crucial for scalable governance strategies.

Answer

Query the INFORMATION_SCHEMA.TAG_REFERENCES table function in every database.

Answer

Use the SYSTEM$GET_TAG function on every table in the account sequentially.

Answer

Execute a SHOW TAGS command and filter for the 'PII_Type' string in the output.

Question 9

When designing a clustering key for a table that is frequently joined with other tables, which strategy generally provides the best performance for the join operation?

Accepted Answer

Cluster on the column used as the join key.. Clustering a table on the columns used in join predicates (the join keys) ensures that Snowflake can use join pruning and potentially more efficient join algorithms. When data is co-located within micro-partitions based on the join key, the engine can skip entire partitions that do not have matching keys in the other table, reducing I/O.

Answer

Cluster on the most frequently used filter column in the WHERE clause.

Answer

Cluster on the column with the highest cardinality in the table.

Answer

Cluster on a column that is never updated or changed.

Question 10

A data engineer needs to ingest files from an S3 bucket into Snowflake as soon as they are uploaded. The files arrive every few minutes and are generally smaller than 50MB. Which approach provides the most cost-effective and low-latency solution for this requirement?

Accepted Answer

Configuring Snowpipe with auto-ingest enabled using SQS notifications.. Snowflake supports several methods for loading data, and choosing the right one depends on file size, frequency, and latency requirements. Snowpipe is specifically designed for continuous, automated loading of small files. This approach reduces manual overhead and ensures that data is available for analysis shortly after it is generated at the source, which is a core requirement for modern data engineering pipelines.

Answer

Using a Task to run a COPY INTO statement every 5 minutes on a Small warehouse.

Answer

Executing a COPY INTO command via a Python script triggered by a cron job.

Answer

Creating an External Table and using the REFRESH command every minute.

Question 11

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.

Answer

Warehouse Local Cache.

Answer

Metadata Cache.

Answer

Search Optimization Service.

Question 12

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.

Answer

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

Answer

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

Answer

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

Question 13

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.

Answer

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

Answer

Enable Result Cache by setting USE_CACHED_RESULT to TRUE.

Answer

Implement a materialized view on the joined columns.

Question 14

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.

Answer

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

Answer

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

Answer

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

Question 15

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.

Answer

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

Answer

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

Answer

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

Question 16

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.

Answer

Create policies within each individual schema.

Answer

Use a single shared role for all policy management.

Answer

Manually recreate all policies in every environment.

Question 17

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.

Answer

SnowSQL with VPN tunneling.

Answer

Snowflake Data Exchange.

Answer

Snowpipe with an external stage.

Question 18

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.

Answer

To automatically correct data type mismatches during the load process.

Answer

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

Answer

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

Question 19

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.

Answer

Data classification can only be applied to existing tables.

Answer

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

Answer

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

Question 20

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.

Answer

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

Answer

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

Answer

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