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[General] Free PDF 2026 Snowflake Authoritative DEA-C02 Online Exam

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【General】 Free PDF 2026 Snowflake Authoritative DEA-C02 Online Exam

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Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions (Q96-Q101):NEW QUESTION # 96
You are tasked with creating a resilient data ingestion pipeline using Snowpipe and external tables on AWS S3. The data consists of JSON files, some of which may occasionally contain invalid JSON structures (e.g., missing closing brackets, incorrect data types). You want to ensure that even if some files are corrupted, the valid data is still ingested into your target Snowflake table, and the corrupted files are logged for later investigation. Which of the following steps would BEST achieve this?
  • A. Configure the external table definition with 'VALIDATION MODE = 'RETURN ERRORS" and then create a view on top of the external table that filters out rows where the 'METADATA$FILE ROW NUMBER column contains errors.
  • B. Configure Snowpipe to use the 'ON ERROR = 'SKIP FILE" copy option and then create a separate task to query the 'VALIDATION MODE metadata column in the external table to identify and log the corrupted files.
  • C. Create a custom error handler using a Snowflake stored procedure that catches the 'JSON PARSER ERROR exception and logs the filename to a separate error table. Use the ERROR = 'CONTINUE" copy option in the Snowpipe definition.
  • D. Use Snowflake's => 'JSON', job_id => function against the external stage before ingesting data with Snowpipe to pre-validate files. Then ingest only validated files to your target table
  • E. Set the 'ON ERROR option to 'ABORT STATEMENT in the Snowpipe definition. This will stop the entire Snowpipe process when a JSON error is detected, allowing you to manually investigate and fix the corrupted files before restarting the pipeline.
Answer: B
Explanation:
Configuring ERROR = 'SKIP FILE'' will ensure that Snowpipe skips any file containing errors and continues processing other valid files. Using the 'VALIDATION MODE' metadata column in the external table allows you to identify which files were skipped due to errors. While custom error handlers could be used, using Snowpipe built-in feature with metadata column is more simpler and effective for the task. Validate function needs a job_id and it is not commonly used for external stages. 'ON ERROR = 'ABORT STATEMENT'' will cause pipeline to stop and hence is less preferable.

NEW QUESTION # 97
A Snowflake table, contains product information in a VARIANT column named This column holds JSON structures. You need to create a view, , that exposes specific fields , and 'category') as structured columns, and should gracefully handle scenarios where may contain characters incompatible with VARCHAR, 'category' is nested inside an array called 'tags'. What is the BEST and the MOST robust approach?

  • A. Option E
  • B. Option C
  • C. Option D
  • D. Option B
  • E. Option A
Answer: A
Explanation:
Option E is the best because it uses 'TRY CAST to safely convert 'product_name' to VARCHAR, handling potential invalid characters without causing the query to fail. Further, to select one category element from array of 'tags' , the way it is done in other options is wrong and we should use 'FLATTEN' with limit 1. Option A will cause problems if 'product_name' cannot be directly cast to VARCHAR. Option B is flawed as 'GET_PATH' requires specific index and its unnecessarily complicated for a simple array access, and if the tags array has other elements, this will arbitrarily get the first tag instead of consistently getting the intended first element (if exists). Option C and D don't use 'LIMIT 1 ' and so throw error as subquery returns more than 1 row.

NEW QUESTION # 98
Consider a scenario where you need to transform data in a Snowflake table using a complex custom transformation logic best implemented in Java'. You decide to use a Snowpark Java UDF. You've packaged your Java code into a JAR file and uploaded it to an internal stage named Which of the following steps are necessary and correctly ordered to deploy and use this Java UDF within Snowflake?

  • A. Option B
  • B. Option C
  • C. Option D
  • D. Option E
  • E. Option A
Answer: A
Explanation:
Option B is the most accurate and complete. It explicitly states that the JAR needs to be uploaded first, and then correctly shows the 'CREATE FUNCTION' syntax including 'IMPORTS', 'HANDLER, and specifying the language as 'JAVA". The handler specifies the method to be invoked inside the class. The execution follows the correct order. Option D is incorrect because HANDLER needs full qualified method name instead of the class name. And option E just ensures stage is present and jar file is there, rather explicitly mention upload.

NEW QUESTION # 99
A daily process loads data into a Snowflake table named 'TRANSACTIONS using a COPY INTO statement. The table is clustered on 'TRANSACTION DATE'. Over time, you observe a significant degradation in query performance when querying data within specific date ranges. Analyzing the 'SYSTEM$CLUSTERING INFORMATION' function output for the 'TRANSACTIONS' table reveals a low 'effective clustering_ratio' and a high 'average_overlaps'. Which combination of actions below would BEST address the performance degradation and improve query efficiency?
  • A. Create a new table with the desired clustering and load data using 'CREATE TABLE AS SELECT statement.
  • B. Implement a data maintenance schedule that regularly reclusters the table using 'ALTER TABLE TRANSACTIONS RECLUSTER;' during off-peak hours and monitor the 'SYSTEM$CLUSTERING INFORMATION' function periodically.
  • C. Drop the current clustered table and create a new table with partition by clauses
  • D. Recluster the table using 'ALTER TABLE TRANSACTIONS RECLUSTER$ and adjust the virtual warehouse size to maximize resource allocation during the recluster operation.
  • E. Drop the existing clustering key on 'TRANSACTION_DATE, then recreate it with a different clustering key such as 'HASH(TRANSACTION_ID)'.
Answer: B,D
Explanation:
A low 'effective_clustering_ratio' and high 'average_overlaps' indicate that the data is not well-clustered, leading to inefficient query performance. Reclustering the table (A) reorganizes the data based on the clustering key, improving clustering. Creating a schedule (D) ensures that the table remains well-clustered over time. Dropping the clustering key and recreating it with a hash on (B) is unlikely to improve performance for date-range queries. Creating new table via 'CREATE TABLE AS SELECT statement is not the right way. In addition, 'Partition By' clause doesn't exist in Snowflake.

NEW QUESTION # 100
A data engineering team observes that queries against a large fact table ('SALES FACT') are slow, even after clustering and partitioning. The table contains columns like 'SALE ID', 'PRODUCT ID, 'CUSTOMER D', 'SALE DATE', 'QUANTITY', and 'PRICE' Queries commonly filter on 'PRODUCT ID' and 'SALE DATE. After implementing search optimization on these two columns, performance only marginally improves. You suspect the data distribution for 'PRODUCT ID' might be skewed. What steps can you take to further investigate and improve query performance?
  • A. Experiment with different clustering keys, possibly including 'PRODUCT_ID and "SALE_DATE in the clustering key.
  • B. Create separate tables for each "PRODUCT_ID' to improve query performance.
  • C. Analyze the cardinality and data distribution of the 'PRODUCT_ID column using 'APPROX COUNT_DISTINCT and histograms to confirm the skewness.
  • D. Drop and recreate the 'SALES FACT table, as the metadata might be corrupted.
  • E. Use to estimate the cost of search optimization on the 'SALES_FACT table and consider disabling it if the cost is too high.
Answer: C
Explanation:
Analyzing the cardinality and data distribution (Option B) is crucial to understanding the effectiveness of search optimization. If 'PRODUCT_ID has skewed data distribution, search optimization might not be as effective. helps estimate the number of unique values, and histograms reveal the distribution. While estimating the cost of search optimization (Option A) is good practice, it doesn't directly address the potential skewness issue. Clustering (Option C) is a different optimization technique, and dropping/recreating the table (Option D) is a drastic measure without evidence of corruption. Creating separate tables for each 'PRODUCT_ID is not scalable and will drastically increase maintenance overhead.

NEW QUESTION # 101
......
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