SNOWPRO ADVANCED: DATA ENGINEER (DEA-C02) RELIABLE PRACTICE TORRENT & DEA-C02 EXAM GUIDE DUMPS & SNOWPRO ADVANCED: DATA ENGINEER (DEA-C02) TEST TRAINING VCE

SnowPro Advanced: Data Engineer (DEA-C02) reliable practice torrent & DEA-C02 exam guide dumps & SnowPro Advanced: Data Engineer (DEA-C02) test training vce

SnowPro Advanced: Data Engineer (DEA-C02) reliable practice torrent & DEA-C02 exam guide dumps & SnowPro Advanced: Data Engineer (DEA-C02) test training vce

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Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions (Q19-Q24):

NEW QUESTION # 19
You're using Snowpark Python to transform data in a Snowflake table called 'employee_data' which includes columns , 'department, 'salary' , and 'performance_rating'. You need to identify the top 3 highest-paid employees within each department based on their salary, but only for departments where the average performance rating is above 4.0. Which of the following approaches using Snowpark efficiently combines window functions, filtering, and aggregations to achieve this?

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

Answer: E

Explanation:
Option E is the most efficient approach, as it calculates the average performance rating per department first and then joins the original DataFrame to only include departments with an average rating above 4.0 using 'left join'. The correct department and salary calculations, filter to only the top 3 with high performace review.


NEW QUESTION # 20
You are responsible for ensuring data consistency across multiple Snowflake tables involved in a financial reporting system. You've noticed discrepancies in aggregate calculations between a 'TRANSACTIONS" table and a summary table 'MONTHLY REPORTS'. The 'TRANSACTIONS' table is frequently updated via streams and tasks. Which combination of the following strategies would be MOST effective in identifying and resolving these inconsistencies in near real-time?

  • A. Implement a Snowflake task that periodically recalculates the 'MONTHLY_REPORTS' table from the 'TRANSACTIONS table and compares the results with the existing data, logging any discrepancies. Use a smaller warehouse size to minimize cost.
  • B. Create a Snowflake alert that triggers when the difference in the total 'SALE_AMOUNT between the 'TRANSACTIONS' table and 'MONTHLY REPORTS' exceeds a predefined threshold within a specified time window.
  • C. Utilize Snowflake's Time Travel feature to compare the ' TRANSACTIONS' table and 'MONTHLY _ REPORTS' table at a specific point in time and identify the changes that led to the discrepancies.
  • D. Implement data validation checks within the data pipeline (streams and tasks) that update the 'TRANSACTIONS' table to reject transactions that violate predefined business rules.
  • E. Use Snowflake's row access policies to restrict access to the 'TRANSACTIONS' table, forcing users to only access the 'MONTHLY REPORTS table.

Answer: B,C,D

Explanation:
Options B, C and D are the most useful. Option B leverages Time Travel to compare data at a specific point. Option C creates an alert to check the difference in total amount. Option D prevents inconsistent data from ever entering the table. Option A attempts to fix an issue, rather than preventing it and a small warehouse will take too long to complete. Option E restricts table access, but does not resolve inconsistent data, which is not what the prompt requests.


NEW QUESTION # 21
You have a Snowflake stage pointing to an external cloud storage location containing numerous Parquet files. A directory table is created on top of it. Over time, some files are deleted or moved from the external location. You notice discrepancies between the directory table's metadata and the actual files present in the storage location. Choose the option that best describes how Snowflake handles these discrepancies and the actions you should take.

  • A. Snowflake does not track file deletions. If a file is deleted from cloud storage after being added to a directory table, Snowflake continues to reference the deleted file, potentially causing errors during data loading. Run 'VALIDATE on the directory table.
  • B. Snowflake requires you to drop and recreate the directory table periodically to synchronize the metadata with the external storage. Using 'ALTER DIRECTORY TABLE REFRESH' will not remove deleted files from the directory table's metadata. However, these invalid files wont be shown in select unless explicitly used.
  • C. Snowflake automatically updates the directory table in real-time, reflecting the changes immediately. No action is needed.
  • D. Snowflake does not automatically detect these changes. You must manually refresh the directory table using 'ALTER DIRECTORY TABLE ... REFRESH' to synchronize the metadata. Snowflake does not provide an automated cleanup of metadata associated with removed files.
  • E. Snowflake automatically detects deleted files and marks them as 'invalid' in the directory table. Queries will automatically exclude these invalid files.

Answer: A

Explanation:
Snowflake's directory tables do not automatically reflect changes in external storage due to file deletions. The 'ALTER DIRECTORY TABLE ... REFRESH' command updates the metadata, but it doesn't automatically remove entries for deleted files. Attempting to load data from a deleted file will likely result in errors. Running 'VALIDATE on the directory table will identify the files that no longer exist in the external stage. Options A, B, C, and D are incorrect because they misrepresent how Snowflake manages changes in the underlying storage.


NEW QUESTION # 22
You have a requirement to continuously load data from a cloud storage location into a Snowflake table. The source data is in Avro format and is being appended to the cloud storage location frequently. You want to automate this process using Snowpipe. You've already created the Snowpipe and the associated stage and file format. However, you notice that some files are being skipped during the ingestion process, and data is missing in your Snowflake table. What is the MOST likely reason for this issue, assuming all necessary permissions and configurations (stage, file format, pipe definition) are correctly set up?

  • A. The file format definition in Snowflake is incompatible with the Avro schema.
  • B. The cloud storage event notifications are not properly configured to trigger Snowpipe.
  • C. The data files in cloud storage are not being automatically detected by Snowpipe.
  • D. Snowflake does not support Avro format for Snowpipe.
  • E. The Snowpipe is paused due to exceeding the daily quota.

Answer: B

Explanation:
Option D is the most likely reason. Snowpipe relies on cloud storage event notifications (e.g., SQS for AWS S3, Event Grid for Azure Blob Storage, Pub/Sub for Google Cloud Storage) to trigger data ingestion. If these notifications are not properly configured, Snowpipe will not be notified of new files being added to the storage location, resulting in skipped files. Option A is possible, but less likely if the pipe was just created and initial data loading is failing. Option B is incorrect; Snowpipe detects files based on event notifications, not by continuously scanning the storage location. Option C could be an issue, but the question states the file format is defined. Option E is incorrect; Snowpipe does support Avro format.


NEW QUESTION # 23
You have a data pipeline that loads data from an internal stage into a Snowflake table Craw_data'). The pipeline is experiencing intermittent failures with the error 'SQL compilation error: Stage 'MY INTERNAL STAGE' is immutable'. What are the potential causes of this error and how would you troubleshoot it?

  • A. The user executing the COPY INTO command lacks the necessary privileges (USAGE on the stage). Grant the appropriate privileges to the user or role.
  • B. This error is caused by insufficient warehouse size. Increase the warehouse size to accommodate the COPY INTO operation.
  • C. The internal stage is being used by multiple COPY INTO commands simultaneously, causing a resource contention issue. Implement queuing or throttling mechanisms to manage concurrent data loading.
  • D. The internal stage has been accidentally dropped and recreated with the same name during the COPY operation. Verify the stage's existence and creation timestamp.
  • E. Another concurrent process is attempting to drop or alter the internal stage while the COPY INTO command is running. Implement proper locking mechanisms to prevent concurrent modifications.

Answer: D,E

Explanation:
The 'Stage is immutable' error typically indicates that the stage's definition has changed during the COPY operation. This can happen if the stage is dropped and recreated (option A) or if another process is altering the stage concurrently (option C). Privilege issues (option B) would usually result in a different error message. Resource contention (option D) is less likely to cause this specific error but could impact performance. Warehouse size (option E) is generally not directly related to this error.


NEW QUESTION # 24
......

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