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2025 Exam DEA-C02 Introduction | Perfect DEA-C02 100% Free Hot Questions

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Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions (Q165-Q170):

NEW QUESTION # 165
Given the following scenario: You have an external table 'EXT SALES in Snowflake pointing to a data lake in Azure Blob Storage. The storage account network rules are configured to only allow specific IP addresses and virtual network subnets, enhancing security. You are getting intermittent errors when querying 'EXT SALES. Which of the following could be the cause(s) and the corresponding solution(s)? Select all that apply.

A. The file format specified in the external table definition does not match the actual format of the files in Azure Blob Storage. Solution: Update the 'FILE_FORMAT parameter in the external table definition to match the correct file format.
B. The Snowflake IP addresses used to access the Azure Blob Storage are not whitelisted in the storage account's firewall settings. Solution: Obtain the Snowflake IP address ranges for your region and add them to the storage account's allowed IP addresses.
C. The Snowflake service principal does not have the correct permissions on the Azure Blob Storage account. Solution: Ensure the Snowflake service principal has the 'Storage Blob Data Reader' role assigned to it.
D. The network connectivity between Snowflake and Azure Blob Storage is unstable. Solution: Implement retry logic in your queries to handle transient network errors.
E. The table function cache is stale, causing access to non-existent files. Solution: Run 'ALTER EXTERNAL TABLE EXT_SALES REFRESH'.

Answer: B,C

Explanation:
Options A and C are the most likely causes. Network restrictions often lead to connectivity issues if Snowflake's IP addresses are not whitelisted (A). Incorrect permissions for the Snowflake service principal (C) will also prevent access to the data lake. Option B is relevant if there are issues around schema changes or new files added. Option D will cause errors all the time not intermittently. Option E Snowflake automatically retries some queries. In a secure environment where IP whitelisting is mandated and IAM roles are properly configured, intermittent failures suggest a permission or a networking issue.

NEW QUESTION # 166
You are designing a data pipeline using Snowpipe to ingest data from multiple S3 buckets into a single Snowflake table. Each S3 bucket represents a different data source and contains files in JSON format. You want to use Snowpipe's auto-ingest feature and a single Snowpipe object for all buckets to simplify management and reduce overhead. However, each data source has a different JSON schem a. How can you best achieve this goal while ensuring data is loaded correctly and efficiently into the target table?

A. Since Snowpipe cannot handle multiple schemas with a single pipe, pre-process the data in S3 using an AWS Lambda function to transform all files into a common schema before they are ingested by the Snowpipe.
B. Use a single Snowpipe with a generic FILE FORMAT that can handle all possible JSON schemas. Implement a VIEW on top of the target table to transform and restructure the data based on the source bucket.
C. Use a single Snowpipe and leverage Snowflake's VARIANT data type to store the raw JSON data. Create separate external tables, each pointing to a specific S3 bucket, and use SQL queries to transform and load the data into the target table.
D. Use a single Snowpipe and leverage Snowflake's ability to call a user-defined function (UDF) within the 'COPY INTO' statement to transform the data based on the S3 bucket path. The UDF can parse the bucket path and apply the appropriate JSON schema transformation.
E. Create a separate Snowpipe for each S3 bucket. Although this creates more Snowpipe objects, it allows you to specify a different FILE FORMAT and transformation logic for each data source.

Answer: D

Explanation:
The most efficient and manageable approach is to use a single Snowpipe with a UDF to handle schema variations. The UDF can inspect the S3 bucket path (available as metadata within the 'COPY INTO' statement) and apply the correct transformation logic for each data source. Creating separate Snowpipes (A) adds unnecessary overhead. Using a generic 'FILE FORMAT and a VIEW (B) might work for simple transformations, but it becomes complex with significant schema differences. Using VARIANT and external tables (C) defeats the purpose of Snowpipe. Pre-processing in S3 (E) adds complexity outside of Snowflake. UDF provides schema flexibility during ingest and leverages Snowpipe's capabilities directly.

NEW QUESTION # 167
You are tasked with managing a large Snowflake table called 'TRANSACTIONS'. Due to compliance requirements, you need to archive data older than one year to long-term storage (AWS S3) while ensuring the queries against the current 'TRANSACTIONS' table remain performant. What is the MOST efficient strategy using Snowflake features and considering minimal impact on query performance?

A. Partition the 'TRANSACTIONS table by date. Export the old partitions of the 'TRANSACTIONS' table to S3 using COPY INTO. Then, drop the old partitions from the 'TRANSACTIONS table and create an external table that points to the data in S3.
B. Export the historical data to S3 using COPY INTO, truncate the 'TRANSACTIONS' table, and then create an external table pointing to the archived data in S3.
C. Create an external table pointing to S3. Then create new table named 'TRANSACTIONS_ARCHIVE in Snowflake, copy the historical data from 'TRANSACTIONS' table into 'TRANSACTIONS ARCHIVE, and then delete the archived data from the 'TRANSACTIONS' table.
D. Use Time Travel to clone the "TRANSACTIONS' table to a point in time one year ago. Then, export the cloned table to S3 and drop the cloned table. Delete the archived data from the 'TRANSACTIONS table.
E. Create a new table 'TRANSACTIONS_ARCHIVE in Snowflake, copy the historical data, and then delete the archived data from the 'TRANSACTIONS table.

Answer: B

Explanation:
Option B is the most efficient. Using 'COPY INTO' to export to S3 is a fast and optimized way to move data. Truncating the table is faster than deleting a large number of rows. Creating an external table allows you to query the archived data in S3 if needed, without ingesting it into Snowflake. Options A & C create another Snowflake table which will consume snowflake credits and storage, which might be costly for a long term storage. Option D cloning is an expensive operation. Option E Partitioning in Snowflake is not natively supported, and would require manual management using external tables and views.

NEW QUESTION # 168
You're using Snowpark Python to transform data in a Snowflake table called 'employee_data' which includes columns , 'department, 'salary' , and 'performance_rating'. You need to identify the top 3 highest-paid employees within each department based on their salary, but only for departments where the average performance rating is above 4.0. Which of the following approaches using Snowpark efficiently combines window functions, filtering, and aggregations to achieve this?

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

Answer: B

Explanation:
Option E is the most efficient approach, as it calculates the average performance rating per department first and then joins the original DataFrame to only include departments with an average rating above 4.0 using 'left join'. The correct department and salary calculations, filter to only the top 3 with high performace review.

NEW QUESTION # 169
A large e-commerce company stores clickstream data in an AWS S3 bucket. The data is partitioned by date and consists of Parquet files. They need to analyze this data in Snowflake without physically moving it into Snowflake's internal storage. However, the data frequently changes, and they need to ensure queries reflect the latest updates to the files without significant latency. Which of the following approaches would be MOST suitable, considering cost, performance, and data freshness?

A. Create an Iceberg table backed by the S3 bucket. Snowflake will automatically manage the metadata and handle incremental updates efficiently.
B. Create an external table using a Snowflake-managed catalog. Configure a Snowpipe to automatically refresh the metadata as new files are added to the S3 bucket.
C. Create a series of views on top of the S3 bucket using 'READ_PARQUET function, updating view definitions whenever underlying files change.
D. Create a standard external table with the 'AUTO REFRESH' parameter set to 'TRUE'. This will automatically refresh the metadata whenever changes are detected in S3.
E. Create a standard external table directly on the S3 bucket. Refresh the external table metadata using SALTER EXTERNAL TABLE ... REFRESH' on a daily schedule.

Answer: A

Explanation:
Iceberg tables are the most suitable option in this scenario. They address the limitations of standard external tables regarding metadata management and incremental updates. 'AUTO REFRESH' for external tables isn't ideal for frequent changes as it still relies on scanning the file system. A Snowflake-managed catalog with Snowpipe could work, but Iceberg is more purpose-built for handling the data evolution in place. READ_PARQUET in views is inefficient as it requires parsing the data every time the view is queried. Iceberg provides ACID properties and optimized query performance on data residing in external storage. The key benefit is the automatic metadata management provided by Snowflake for Iceberg tables.

NEW QUESTION # 170
......

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