DEA-C02 TEST QUESTIONS ANSWERS, DEA-C02 EXAM FLASHCARDS

DEA-C02 Test Questions Answers, DEA-C02 Exam Flashcards

DEA-C02 Test Questions Answers, DEA-C02 Exam Flashcards

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Tags: DEA-C02 Test Questions Answers, DEA-C02 Exam Flashcards, Exam DEA-C02 Bible, Cert DEA-C02 Guide, DEA-C02 Sample Exam

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Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions (Q197-Q202):

NEW QUESTION # 197
Consider a table 'EVENT DATA' that stores events from various applications. The table has columns like 'EVENT ID, 'EVENT TIMESTAMP, 'APPLICATION ID', 'USER ID', and 'EVENT _ TYPE. A significant portion of queries filter on 'EVENT TIMESTAMP ranges AND 'APPLICATION ID. The data volume is substantial, and query performance is crucial. You observe high clustering depth after initial loading. Which combination of actions will provide the MOST effective performance optimization, addressing both clustering depth and query performance?

  • A. Cluster the table on '(EVENT TIMESTAMP, APPLICATION IDY and periodically run 'OPTIMIZE TABLE EVENT DATA' using a warehouse sized appropriately for the table size. Then, monitor clustering depth regularly.
  • B. Cluster the table on 'USER ICY and rely solely on Snowflake's automatic reclustering feature, without running 'OPTIMIZE TABLES manually.
  • C. Create multiple materialized views: one filtering on common 'EVENT TIMESTAMP' ranges, and another filtering on common 'APPLICATION ID' values.
  • D. Create separate tables for each ' , each clustered on 'EVENT_TIMESTAMP'. Then, create a view that UNION ALLs these tables.
  • E. Cluster the table on 'EVENT TIMESTAMP' and periodically run 'OPTIMIZE TABLE EVENT DATA' using a small warehouse. Also, create a separate table clustered on 'APPLICATION

Answer: A

Explanation:
Clustering on '(EVENT _ TIMESTAMP, APPLICATION_ID)' directly addresses the common query patterns. Regularly running 'OPTIMIZE TABLE EVENT DATA' with an appropriately sized warehouse ensures the data remains well-clustered as new data is added, reducing clustering depth and maintaining performance. Monitoring clustering depth is essential to identify when reclustering is needed. Clustering on a single dimension like 'USER IDS (C) doesn't address the primary query patterns. Creating separate tables (A, D) introduces complexity and management overhead. Materialized views (E) are helpful for specific pre-aggregated results, but clustering optimizes the base table for a wider range of queries. Optimizing with the right sized warehouse is crucial, a small warehouse might take an extremely long time.


NEW QUESTION # 198
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. 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$PIPE to troubleshoot the files not ingested
  • B. Snowflake's auto-ingest feature has limitations and may not be suitable for inconsistent data formats. Consider using the Snowpipe REST API to implement custom error handling and data validation logic. Monitor the Snowflake event queue to ensure events are being received.
  • C. The 'DATE FORMAT parameter is case-sensitive. Ensure it matches the case of the incoming data. Also, check the 'VALIDATION MODE and ERROR parameters to ensure error handling is appropriately configured for files with date format errors. For the files that are not ingested use 'SYSTEM$PIPE to find the cause of the issue.
  • D. 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'.
  • 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: A,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 # 199
You are tasked with implementing row-level security (RLS) on a 'SALES' table to restrict access based on the 'REGION' column. Users with the 'NORTH REGION ROLE should only see data where 'REGION = 'NORTH". You've created a row access policy named north_region_policy'. After applying the policy to the 'SALES table, users with the 'NORTH REGION ROLE are still seeing all rows.
Which of the following is the MOST likely reason for this and how can it be corrected?

  • A. The is not enabled. Execute 'ALTER ROW ACCESS POLICY ON SALES SET ENABLED = TRUE;'
  • B. The policy needs to be explicitly refreshed. Execute 'REFRESH ROW ACCESS POLICY north_region_policy ON SALES;'
  • C. The user has not logged out and back in since the role was granted to them. Force the user to re-authenticate.
  • D. The ' does not have the USAGE privilege on the database and schema containing the 'SALES' table. Grant the USAGE privilege to the role.
  • E. The policy function within is not using the correct context function to determine the user's role. It should use 'CURRENT ROLE()' instead of 'CURRENT_USER()'

Answer: D

Explanation:
Row access policies require the role to have USAGE privilege on the database and schema. Without this privilege, the policy cannot be enforced. The other options, while potentially relevant in other scenarios, are not the most likely cause for the described issue. Row access policies are automatically enabled when applied and the correct context function would be CURRENT_ROLE(). A refresh command is not required.


NEW QUESTION # 200
You are tasked with designing a data pipeline to load data from an Azure Blob Storage container into Snowflake using an external stage. The data is in CSV format, compressed using GZIP. The container contains millions of small CSV files. To optimize the data loading process and minimize cost, which of the following strategies would you implement, considering both stage configuration and COPY INTO options? Choose TWO that apply.

  • A. Leverage Snowflake's Snowpipe with a REST API endpoint to trigger data loads whenever new files are available in the Azure Blob Storage container.
  • B. Use the 'MATCH BY COLUMN NAME = CASE INSENSITIVE option with a copy transformation in the 'COPY INTO' statement to ensure that the column order in the CSV files doesn't affect the data load.
  • C. Use the 'VALIDATION MODE = RETURN ERRORS option in the 'COPY INTO' statement to identify and correct any data quality issues during the load. This ensures that only clean data is loaded into Snowflake.
  • D. Consolidate the small CSV files in the Azure Blob Storage container into larger files before loading them into Snowflake. This reduces the overhead of processing numerous small files.
  • E. Create a pipe object with 'AUTO INGEST = TRUE to automatically ingest new files as they are added to the Azure Blob Storage container. This ensures near real-time data ingestion.

Answer: A,D

Explanation:
Consolidating small files into larger files (option E) significantly improves COPY INTO performance by reducing overhead. Using Snowpipe with a REST API endpoint (option C) allows for efficient, triggered data loading. 'VALIDATION MODE (option A) is useful for data quality but doesn't directly address optimization for millions of small files. 'AUTO INGEST (option B) is specific to AWS S3 and Google Cloud Storage, not Azure Blob Storage event notifications. Column matching (option D) addresses schema flexibility, not optimization.


NEW QUESTION # 201
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. Create a materialized view that pre-aggregates the data based on 'ORDER_DATE and "CUSTOMER_ID
  • B. Enable search optimization on both the 'ORDER DATE and 'CUSTOMER IDS columns.
  • C. Enable search optimization on the 'PRODUCT ID column.
  • 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 # 202
......

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