100% PASS 2025 FANTASTIC DEA-C02: SNOWPRO ADVANCED: DATA ENGINEER (DEA-C02) VALID DUMPS SHEET

100% Pass 2025 Fantastic DEA-C02: SnowPro Advanced: Data Engineer (DEA-C02) Valid Dumps Sheet

100% Pass 2025 Fantastic DEA-C02: SnowPro Advanced: Data Engineer (DEA-C02) Valid Dumps Sheet

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Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions (Q166-Q171):

NEW QUESTION # 166
A data engineering team is responsible for an ELT pipeline that loads data into Snowflake. The pipeline has two distinct stages: a high- volume, low-complexity transformation stage using SQL on raw data, and a low-volume, high-complexity transformation stage using Python UDFs that leverages an external service for data enrichment. The team is experiencing significant queueing during peak hours, particularly impacting the high-volume stage. You need to optimize warehouse configuration to minimize queueing. Which combination of actions would be MOST effective?

  • A. Create a single, large (e.g., X-Large) warehouse and rely on Snowflake's automatic scaling to handle the workload.
  • B. Create two separate warehouses: a Small warehouse configured for auto-suspend after 5 minutes for the high-volume, low-complexity transformations and a Large warehouse configured for auto-suspend after 60 minutes for the low-volume, high-complexity transformations.
  • C. Create two separate warehouses: a Medium warehouse for the high-volume, low-complexity transformations and an X-Small warehouse for the low-volume, high-complexity transformations.
  • D. Create two separate warehouses: a Large, multi-cluster warehouse configured for auto-scale for the high-volume, low-complexity transformations and a Small warehouse for the low-volume, high-complexity transformations.
  • E. Create a single, X-Small warehouse and rely on Snowflake's query acceleration service to handle the workload.

Answer: D

Explanation:
Creating separate warehouses allows for independent scaling and resource allocation based on workload characteristics. Using a larger, multi-cluster warehouse with auto-scale for the high-volume stage ensures that sufficient resources are available to handle the load without queueing. A smaller warehouse is sufficient for the low-volume, high-complexity transformations. Options A, D and E are incorrect as they do not appropriately separate and size warehouses according to the workload profile. Option B sizes warehouses incorrectly.


NEW QUESTION # 167
You are building a data pipeline to ingest JSON data from an external API using the Snowflake SQL API. The API returns nested JSON structures, and you need to extract specific fields and load them into a Snowflake table with a flattened schem a. You also need to handle potential schema variations and missing fields in the JSON data. Which approach provides the MOST robust and flexible solution for this scenario, maximizing data quality and minimizing manual intervention?

  • A. Use a stored procedure with dynamic SQL to parse the JSON, create new tables based on the current schema, and load data. Maintain metadata on table versions.
  • B. Use the 'JSON TABLE function in a Snowflake SQL query executed via the SQLAPI to flatten the JSON data and extract the required fields. Handle missing fields by using 'DEFAULT values in the table schema.
  • C. Utilize Snowflake's schema detection feature during the COPY INTO process. This will automatically infer the schema from the JSON data and create the table accordingly.
  • D. Parse the JSON data in your client application (e.g., Python) using a library like 'json' or , transform the data into a tabular format, and then use the Snowflake Connector for Python to load the data into Snowflake.
  • E. Load the raw JSON data into a VARIANT column in Snowflake. Create a series of views on top of the VARIANT column to extract the required fields and handle schema variations using 'TRY TO ' functions.

Answer: E

Explanation:
Loading the raw JSON into a VARIANT column and creating views with 'TRY TO functions provides the most flexible and robust solution. It allows you to handle schema variations gracefully without requiring changes to the underlying table. 'JSON TABLE (A) can be complex for deeply nested structures. Parsing in the client application (B) requires more coding and infrastructure. Schema detection during COPY INTO (D) is less flexible for handling variations. Stored procedures with dynamic SQL(E) introduces complexity in schema maintenance and evolution.


NEW QUESTION # 168
You have a Snowflake table 'ORDERS' with billions of rows storing order information. The table includes columns like 'ORDER ID', 'CUSTOMER ID', 'ORDER DATE, 'PRODUCT_ID', and 'ORDER AMOUNT'. Analysts frequently run queries filtering by 'ORDER DATE' and 'CUSTOMER ID to analyze customer ordering trends. The performance of these queries is slow. Assuming you've already considered clustering and partitioning, which of the following strategies would BEST improve query performance, specifically targeting these filtering patterns? Assume the table is large enough for search optimization to be beneficial.

  • A. Enable search optimization on the 'PRODUCT ID column.
  • B. Enable search optimization on both the 'ORDER DATE and 'CUSTOMER IDS columns.
  • C. Create a materialized view that pre-aggregates the data based on 'ORDER_DATE and "CUSTOMER_ID
  • D. Enable search optimization on the 'ORDER_ID column.
  • E. Enable search optimization on the 'ORDER_DATE' column.

Answer: B

Explanation:
Enabling search optimization on both 'ORDER_DATE and will directly benefit queries filtering by these columns. Search optimization is designed to significantly speed up point lookups and range scans. A materialized view (option D) might help, but it introduces the overhead of maintaining the view and might not be as flexible as search optimization for ad-hoc queries. Options A and E are incorrect since they focus on columns not frequently used in the specified filtering criteria.


NEW QUESTION # 169
You are developing a Snowpark Python application that needs to process data from a Kafka topic. The data is structured as Avro records. You want to leverage Snowpipe for ingestion and Snowpark DataFrames for transformation. What is the MOST efficient and scalable approach to integrate these components?

  • A. Configure Snowpipe to ingest the raw Avro data into a VARIANT column in a staging table. Utilize a Snowpark DataFrame with Snowflake's get_object field function on the variant to get an object by name, and create columns based on each field.
  • B. Use Snowpipe to ingest the Avro data to a raw table stored as binary. Then, use a Snowpark Python UDF with an Avro deserialization library to convert the binary data to a Snowpark DataFrame.
  • C. Create a Kafka connector that directly writes Avro data to a Snowflake table. Then, use Snowpark DataFrames to read and transform the data from that table.
  • D. Create external functions to pull the Avro data into a Snowflake stage and then read the data with Snowpark DataFrames for transformation.
  • E. Convert Avro data to JSON using a Kafka Streams application before ingestion. Use Snowpipe to ingest the JSON data to a VARIANT column and then process it using Snowpark DataFrames.

Answer: E

Explanation:
Option D is generally the most efficient. Converting Avro to JSON before ingestion simplifies the integration with Snowpipe and Snowpark. Snowpipe is optimized for semi-structured data like JSON within a VARIANT column. Subsequently, Snowpark DataFrames can easily process the JSON data using built-in functions, avoiding the complexity and potential performance bottlenecks of UDFs (Option B) or custom connectors (Option A). Although Snowflake's function can work with variant data, operating on raw Avro data is not natively supported by Snowpipe without pre-processing or complex UDF logic. External functions (Option E) add another layer of complexity for data retrieval.


NEW QUESTION # 170
You are tasked with creating an external function in Snowflake that calls a REST API. The API requires a bearer token for authentication, and the function needs to handle potential network errors and API rate limiting. Which of the following code snippets demonstrates the BEST practices for defining and securing this external function, including error handling?

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

Answer: B

Explanation:
Option A uses SECURITY_INTEGRATION, which is suitable for cloud provider-managed security but doesn't directly handle the API key. Option B uses CREDENTIAL, which is deprecated. Option C and D use AUTH POLICY and SECRET, but C doesn't use SYSTEM$GET_SECRET within a 'USING' clause or CONTEXT_HEADERS. Option D uses the 'USING' clause but does not use 'CONTEXT HEADERS to pass the token correctly. Option E is the BEST approach because it utilizes 'SECURITY INTEGRATION' along with 'CONTEXT_HEADERS' to pass the Bearer token securely retrieved from the Snowflake secret, ensuring proper authentication. Using CONTEXT HEADERS allows setting the authorization header directly. Also, its importand to create the 'SECRET api_secret' for this code to work correctly and this options uses it.


NEW QUESTION # 171
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