SNOWFLAKE - DEA-C02–PROFESSIONAL MOST RELIABLE QUESTIONS

Snowflake - DEA-C02–Professional Most Reliable Questions

Snowflake - DEA-C02–Professional Most Reliable Questions

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Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions (Q63-Q68):

NEW QUESTION # 63
A data engineering team is loading a large fact table 'SALES DATA' daily, partitioned by 'SALE DATE. After several months, query performance degrades significantly. An analyst reports that queries filtering on 'CUSTOMER are slow, despite 'CUSTOMER ID' having high cardinality. The table definition is as follows: CREATE TABLE SALES_DATA ( SALE DATE DATE NOT NULL, CUSTOMER_ID NUMBER NOT NULL, PRODUCT ID NUMBER NOT NULL, SALE_AMOUNT ... Which of the following actions would BEST improve query performance for queries filtering on 'CUSTOMER ID', considering the existing partitioning by 'SALE DATE'?

  • A. Partition the table by 'CUSTOMER_ID instead of 'SALE_DATE.
  • B. Cluster the 'SALES DATA' table on 'CUSTOMER ID.
  • C. Create a materialized view that aggregates data by 'CUSTOMER_ID and relevant dimensions.
  • D. Create a secondary index on 'CUSTOMER ID'
  • E. Increase the virtual warehouse size.

Answer: B

Explanation:
Clustering the table on 'CUSTOMER_ID' will physically organize the data based on this column, improving the performance of queries filtering on 'CUSTOMER ID. While increasing warehouse size (E) might provide some performance boost, clustering addresses the underlying data organization issue. Secondary indexes (A) are not supported in Snowflake. Partitioning by 'CUSTOMER_ID (D) isn't possible in Snowflake. Materialized views (B) are a valid option for pre-aggregation, but clustering will directly improve base table performance for filtering. Therefore, clustering is the best option.


NEW QUESTION # 64
A Snowflake table 'SALES DATA' is frequently updated via Snowpipe. Historical data is occasionally queried using time travel. You notice that time travel queries are becoming increasingly slow. Which of the following Snowflake features or techniques would BEST address this performance degradation?

  • A. Increase the parameter for the 'SALES_DATX table.
  • B. Implement data clustering on a column that is frequently used in time travel query filters.
  • C. Periodically clone the ' SALES DATA' table to a separate historical table.
  • D. Decrease the parameter for the 'SALES_DATA' table.
  • E. Create a materialized view that pre-computes the results of common time travel queries.

Answer: B

Explanation:
Clustering (E) optimizes the physical storage of data, making it more efficient to retrieve specific historical versions. Decreasing data retention (C) can improve performance but reduces the available time travel window. Increasing retention (A) will degrade performance even more. Materialized Views (B) can help, but they require maintenance and may not be suitable for all time travel scenarios. Cloning (D) creates a full copy, which is inefficient and may not be necessary.


NEW QUESTION # 65
You are designing a Snowpark Python application to process streaming data from a Kafka topic and land it into a Snowflake table 'STREAMED DATA. Due to the nature of streaming data, you want to achieve the following: 1. Minimize latency between data arrival and data availability in Snowflake. 2. Ensure exactly-once processing semantics to prevent data duplication. 3. Handle potential schema evolution in the Kafka topic without breaking the pipeline. Which combination of Snowpark and Snowflake features, applied with the correct configuration, would BEST satisfy these requirements? Select all that apply.

  • A. Implement a Snowpark Python UDF that consumes data directly from the Kafka topic using a Kafka client library. Write data into 'STREAMED_DATX within a single transaction. Use a structured data type for the 'STREAMED DATA'.
  • B. Use Snowflake's native Kafka connector to load data into a staging table. Then, use a Task and Stream combination, using a Snowpark Python UDF, to transform and load the data into 'STREAMED DATA' within a single transaction, handling schema evolution by casting columns to their new types or dropping missing column data.
  • C. Use Snowflake Connector for Kafka to load data into a staging table. Then, use Snowpark Python to transform and load the data into 'STREAMED_DATR within a single transaction. Implement schema evolution logic in the Snowpark code to handle changes in the Kafka topic schema.
  • D. Utilize Snowflake Streams on in conjunction with Snowpark to transform and cleanse the data after it has been ingested by Snowpipe. Apply a merge statement to update an external table of parquet files.
  • E. Use Snowpipe with auto-ingest and configure it to trigger on Kafka topic events. Define a VARIANT column in 'STREAMED_DATX to handle schema evolution.

Answer: B,C

Explanation:
Options D and E represent the most reliable solutions to this problem statement. Option D: The combination of the Snowflake Connector for Kafka and Snowpark offers a balanced approach. The connector efficiently loads the raw data, and Snowpark Python provides the flexibility to transform the data within a transaction and implement schema evolution logic. Option E: Snowflake's Kafka connector, combined with tasks, streams, and a Snowpark IJDF, provides a pipeline that continuously transforms data and is only triggered by new events in the staging table created by the Kafka connector. Implementing schema evolution in the IJDF itself handles small changes effectively. Option A does not provide exactly-once semantics. While VARIANT columns handle schema evolution, Snowpipe itself might deliver messages more than once. Option B is less scalable and harder to manage compared to using the Snowflake Connector for Kafka or Streams/Tasks. Option C, using Streams on 'STREAMED_DATA' , can lead to data duplication if not managed correctly and updating an external table negates a central table stream for change control.


NEW QUESTION # 66
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. Drop and recreate the 'SALES FACT table, as the metadata might be corrupted.
  • B. Use to estimate the cost of search optimization on the 'SALES_FACT table and consider disabling it if the cost is too high.
  • C. Create separate tables for each "PRODUCT_ID' to improve query performance.
  • D. Experiment with different clustering keys, possibly including 'PRODUCT_ID and "SALE_DATE in the clustering key.
  • E. Analyze the cardinality and data distribution of the 'PRODUCT_ID column using 'APPROX COUNT_DISTINCT and histograms to confirm the skewness.

Answer: E

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 # 67
You have implemented a Snowpipe using auto-ingest to load data from an AWS S3 bucket. The pipe is configured to load data into a table with a 'DATE column ('TRANSACTION DATE'). The data files in S3 contain a date field in the format 'YYYYMMDD'. Occasionally, you observe data loading failures in Snowpipe with the error message indicating an issue converting the string to a date. The 'FILE FORMAT' definition includes 'DATE FORMAT = 'YYYYMMDD''. Furthermore, you are also noticing that after a while, some files are not being ingested even though they are present in the S3 bucket. How to effectively diagnose and resolve these issues?

  • A. The issue may arise if the time zone of the Snowflake account does not match the time zone of your data in AWS S3. Try setting the 'TIMEZONE parameter in the FILE FORMAT definition. For files that are not being ingested, manually refresh the Snowpipe with 'ALTER PIPE ... REFRESH'.
  • B. Verify that the 'DATE FORMAT is correct and that all files consistently adhere to this format. Check for corrupted files in S3 that may be preventing Snowpipe from processing subsequent files. Additionally, review the Snowpipe error notifications in Snowflake to identify the root cause of ingestion failures. Use 'SYSTEM$PIPE to troubleshoot the files not ingested
  • C. The 'DATE FORMAT parameter is case-sensitive. Ensure it matches the case of the incoming data. Also, check the 'VALIDATION MODE and ERROR parameters to ensure error handling is appropriately configured for files with date format errors. For the files that are not ingested use 'SYSTEM$PIPE to find the cause of the issue.
  • D. Snowflake's auto-ingest feature has limitations and may not be suitable for inconsistent data formats. Consider using the Snowpipe REST API to implement custom error handling and data validation logic. Monitor the Snowflake event queue to ensure events are being received.
  • E. The error could be due to invalid characters in the source data files. Implement data cleansing steps to remove invalid characters from the date fields before uploading to S3. For files not being ingested, check S3 event notifications for missing or failed events.

Answer: B,C

Explanation:
Option A is partially correct as the validation _ mode parameter in file format needs to be reviewed, not only the casesensitivity for the date. Case sensitivity isn't strictly enforced for DATE FORMAT. Snowflake's documentation specifies the valid specifiers (YYYY, MM, DD, etc.) which are generally case-insensitive in this context.The 'VALIDATION MODE and 'ON ERROR parameters in the copy option are critical. Incorrect handling of files that fails can cause future file ingests to stop. Option E highlights the importance of verifying the data format consistency and checking for corrupted files. Corrupted files or files that do not adhere to the specified format can cause Snowpipe to fail and potentially stop processing further files. Option B is incorrect, while timezone mismatches can cause issues, they don't directly lead to data loading failures with format conversion if the format is wrong or if file validation caused the issue. Option C's suggestion of data cleansing is valid in general, but it addresses a different problem (data quality) than the specific error described in the question. Option D proposes switching to the REST API, which is an overkill for this scenario. The auto-ingest feature is suitable; the problem is likely with data format inconsistencies or error handling.


NEW QUESTION # 68
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