100% PASS 2025 SNOWFLAKE HIGH HIT-RATE DEA-C02: TRAINING SNOWPRO ADVANCED: DATA ENGINEER (DEA-C02) FOR EXAM

100% Pass 2025 Snowflake High Hit-Rate DEA-C02: Training SnowPro Advanced: Data Engineer (DEA-C02) For Exam

100% Pass 2025 Snowflake High Hit-Rate DEA-C02: Training SnowPro Advanced: Data Engineer (DEA-C02) For Exam

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Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions (Q327-Q332):

NEW QUESTION # 327
A data engineer is tasked with implementing a data governance strategy in Snowflake. They need to automatically apply a tag 'PII CLASSIFICATION' to all columns containing Personally Identifiable Information (PII). Given the following requirements: 1. The tag must be applied as close to data ingestion as possible. 2. The tagging process should be automated and scalable. 3. The tag value should be dynamically set based on a regular expression match against column names and data types. Which of the following approaches would be MOST effective and efficient in achieving these goals?

  • A. Manually tag each column containing PII using the Snowflake web UI or the 'ALTER TABLE ... ALTER COLUMN ... SET TAG' command. Train data stewards to identify and tag new columns.
  • B. Implement a custom application using the Snowflake JDBC driver to periodically scan table schemas, detect PII columns, and apply tags using dynamic SQL.
  • C. Use Snowflake's Event Tables in conjunction with a stream and task. Configure the stream to capture DDL changes, and the task to evaluate new columns and apply the tag based on the column metadata using regular expressions.
  • D. Implement a stored procedure that leverages external functions to call a Python script hosted on AWS Lambda, which uses a machine learning model to identify PII and apply Snowflake tags.
  • E. Create a Snowflake Task that runs daily, querying the INFORMATION SCHEMCOLUMNS view, identifying potential PII columns based on regular expressions, and then executing ALTER TABLE ... ALTER COLUMN ... SET TAG commands.

Answer: C

Explanation:
Option C is the most effective because it leverages Snowflake's native event capture mechanisms (Event Tables, Streams and Tasks) to react to DDL changes in near real-time. This approach is automated, scalable, and avoids the overhead of periodic polling. Options A and B involve periodic scanning which is less efficient. Option D is manual and doesn't scale. Option E introduces unnecessary complexity with external functions and ML models for a relatively simple task, increasing operational overhead.


NEW QUESTION # 328
A data engineering team is building a real-time dashboard in Snowflake to monitor website traffic. The dashboard relies on a complex query that joins several large tables. The query execution time is consistently exceeding the acceptable threshold, impacting dashboard responsiveness. Historical data is stored in a separate table and rarely changes. You suspect caching is not being utilized effectively. Which of the following actions would BEST improve the performance of this dashboard and leverage Snowflake's caching features?

  • A. Replace the complex query with a series of simpler queries. This will reduce the amount of data that needs to be processed at any one time.
  • B. Materialize the historical data into a separate table that utilizes clustering and indexing for faster query performance. Refresh this table periodically.
  • C. Increase the size of the virtual warehouse. A larger warehouse will have more resources to execute the query, and the results will be cached for a longer period.
  • D. Use 'RESULT_SCAN' to cache the query result in the user session for subsequent queries. This is especially effective for large datasets that don't change frequently.
  • E. Create a materialized view that pre-computes the results of the complex query. Snowflake will automatically refresh the materialized view when the underlying data changes.

Answer: E

Explanation:
Materialized views are the best option in this scenario. They pre-compute the results of the complex query and store them in a separate table. Snowflake automatically refreshes the materialized view when the underlying data changes, ensuring that the dashboard always displays the most up-to-date information. While increasing the virtual warehouse size (D) can help initially, it's a more expensive and less targeted solution. 'RESULT_SCAN' (A) is session-specific and not suitable for persistent caching for a dashboard accessed by multiple users. Materializing the historical data (B) might help, but it doesn't address the core issue of the complex query. Breaking the query into smaller parts (E) might not be efficient and can introduce complexity.


NEW QUESTION # 329
You're using Snowpipe Streaming to ingest JSON data into a Snowflake table. The JSON data contains nested objects and arrays. You're encountering errors related to data type mismatches during ingestion. The target table schema is defined with specific data types for each column. Which of the following approaches is MOST effective for handling this data type mismatch issue within the Snowpipe Streaming context, considering minimal transformation?

  • A. Modify the client-side application to cast the data to the correct data type before sending it to Snowpipe Streaming.
  • B. Use a COPY INTO statement with a transformation function to cast the data during ingestion.
  • C. Adjust the auto_ingest property of the Snowpipe object to force data type conversion.
  • D. Create a Stored Procedure in Snowflake to transform the data after it has been ingested using Snowpipe Streaming.
  • E. Use a variant column in the Snowflake table and perform data type casting during querying.

Answer: A

Explanation:
B is the most effective approach. Snowpipe Streaming is designed for low-latency ingestion with minimal transformation at ingestion time. Data type mismatches are best resolved at the source (client-side) before the data is sent to Snowflake. A introduces complexity at query time and isn't ideal for a fixed schema. C adds latency and post-ingestion processing. D is not applicable to Snowpipe Streaming; COPY INTO is for Classic Snowpipe. E, is not related to data type conversion.


NEW QUESTION # 330
A data engineering team is implementing Row Access Policies (RAP) on a table 'employee_data' containing sensitive salary information. They need to ensure that only managers can see the salary information of their direct reports. A user-defined function (UDF) 'GET returns a comma-separated string of manager usernames for a given username. Which of the following SQL statements correctly creates and applies a RAP to achieve this?

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

Answer: B

Explanation:
Option D correctly uses EXISTS and SPLIT TO TABLE to check if the employee's username is present in the list of managers returned by the GET_MANAGERS UDE Options A, B, C, and E have logical errors in how they determine manager-employee relationships, or don't correctly handle the comma-separated string of managers returned by the UDF. Options A and C also use CURRENT ROLE() = 'MANAGER' which requires the user to explicitly set their role to 'MANAGER', which might not be practical.


NEW QUESTION # 331
A data engineering team uses Snowflake to analyze website clickstream data stored in AWS S3. The data is partitioned by year and month in the S3 bucket. They need to query the data frequently for reporting purposes but don't want to ingest the entire dataset into Snowflake due to storage costs and infrequent full dataset analysis. Which approach is the MOST efficient and cost-effective way to enable querying of this data in Snowflake?

  • A. Create a Snowflake internal stage, copy the necessary files into the stage, and then load the data into a Snowflake table.
  • B. Create a Snowpipe pointing to the S3 bucket and ingest the data continuously into a Snowflake table.
  • C. Load all the data into a Snowflake table and create a materialized view on top of the table to pre-aggregate the data for reporting.
  • D. Use Snowflake's COPY INTO command to ingest data directly from S3 into a Snowflake table on a scheduled basis.
  • E. Create a Snowflake external stage pointing to the S3 bucket, define an external table on the stage, and use partitioning metadata to optimize queries.

Answer: E

Explanation:
Using an external table pointing to the S3 bucket is the most efficient and cost-effective approach. It allows you to query the data directly in S3 without ingesting it into Snowflake, saving on storage costs. Partitioning metadata further optimizes query performance by allowing Snowflake to only scan relevant partitions based on the query criteria.


NEW QUESTION # 332
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