RELIABLE DEA-C02 TEST DUMPS & RELIABLE DEA-C02 EXAM BLUEPRINT

Reliable DEA-C02 Test Dumps & Reliable DEA-C02 Exam Blueprint

Reliable DEA-C02 Test Dumps & Reliable DEA-C02 Exam Blueprint

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Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions (Q73-Q78):

NEW QUESTION # 73
Which of the following statements are TRUE regarding Snowflake's Fail-safe mechanism and its relation to Time Travel? (Select all that apply)

  • A. Fail-safe is exclusively used by Snowflake to recover data in the event of a catastrophic system failure, and users have no direct access.
  • B. The Fail-safe period starts immediately after the Time Travel retention period ends.
  • C. Users can query data directly from Fail-safe using SQL commands if Time Travel is insufficient.
  • D. Fail-safe is automatically enabled for all Snowflake accounts and requires no configuration.
  • E. Fail-safe provides a historical data retention period of 7 days, similar to the default Time Travel setting.

Answer: A,B,D

Explanation:
Fail-safe is automatically enabled and managed by Snowflake (B). It kicks in after Time Travel (C) and is not directly accessible to users (E). Users cannot query data from Fail-safe using SQL commands. Fail-safes duration depends on the Snowflake Edition but not for the same days as time travel.


NEW QUESTION # 74
You are working with a very large Snowflake table named 'CUSTOMER TRANSACTIONS which is clustered on 'CUSTOMER ID and 'TRANSACTION DATE. After noticing performance degradation on queries that filter by 'TRANSACTION AMOUNT and 'REGION' , you decide to explore alternative clustering strategies. Which of the following actions, when performed individually, will LEAST likely improve query performance specifically for queries filtering by 'TRANSACTION AMOUNT and 'REGION', assuming you can only have one clustering key?

  • A. Creating a new table clustered on 'TRANSACTION_AMOUNT and 'REGION', and migrating the data.
  • B. Creating a search optimization on 'TRANSACTION_AMOUNT' and 'REGION' columns.
  • C. Dropping the existing clustering key and clustering on 'TRANSACTION_AMOUNT' and 'REGION'.
  • D. Creating a materialized view that pre-aggregates data by 'TRANSACTION_AMOUNT and 'REGION'.
  • E. Adding ' TRANSACTION_AMOUNT and 'REGIO!V to the existing clustering key while retaining 'CUSTOMER_ID and 'TRANSACTION_DATE

Answer: E

Explanation:
Adding 'TRANSACTION_AMOUNT and 'REGION' to the existing clustering key while retaining 'CUSTOMER ID and 'TRANSACTION_DATE (option D) is the LEAST likely to improve performance for queries filtering by 'TRANSACTION_AMOUNT and 'REGION' Clustering is most effective when the order of columns in the clustering key matches the order in which they are filtered in the query. Because the query filters on 'TRANSACTION_AMOUNT and 'REGION', these columns should be the leading columns in the clustering key for optimal pruning. Since the leading keys are and , snowflake would still read significant amount of unnecessary data for filtering. A, C, and E all address having 'TRANSACTION_AMOUNT and 'REGION' as keys, and B addresses caching.


NEW QUESTION # 75
You are performing a series of complex data transformations on a large table named 'TRANSACTIONS' in Snowflake. After running several DML statements, you realize that an earlier transformation step introduced incorrect data into the table. You want to rollback the table to a state before that specific transformation occurred. Which of the following methods could be used to achieve this rollback, assuming you know the exact timestamp or query ID of the state you want to revert to? Select all that apply.

  • A. Create a clone of the ' TRANSACTIONS' table using Time Travel, specifying the 'AT' or 'BEFORE clause with either the timestamp or query ID of the desired state. Then, replace the original table with the cloned table.
  • B. Create a new table with the correct data and load from the original table filtered by a range of transaction IDs excluding the incorrect range.
  • C. Restore the entire Snowflake account to a point in time before the incorrect transformation.
  • D. Use the UNDROP TABLE command if the table was dropped accidentally, then manually re-apply the correct transformations.
  • E. Use Time Travel to query the historical version of the 'TRANSACTIONS' table using the 'AT' or 'BEFORE clause with either the timestamp or query ID. Then, use 'INSERT OVERWRITES or ' REPLACE TABLES statement to replace the current content of the original table with the historical data.

Answer: A,E

Explanation:
Options B and E are the correct methods for rolling back changes using Time Travel. Option B clones the table to the desired state and replaces the original, effectively rolling it back. Option E directly overwrites the original table with the historical data obtained through Time Travel. Option A is an extreme measure. Option C is for recovering dropped tables, not rolling back changes within a table. Option D does not directly rollback the table to the previous state, the correct data has to be loaded, that will take more time.


NEW QUESTION # 76
You are designing a data sharing solution in Snowflake where a provider account shares a view with a consumer account. The view is based on a table that undergoes frequent DML operations (inserts, updates, deletes). The consumer account needs to see a consistent snapshot of the data, even during these DML operations. Which of the following strategies, or combination of strategies, would be MOST effective in ensuring data consistency from the consumer's perspective, and what considerations should be made?

  • A. A and B
  • B. Using Snowflake's Time Travel feature by querying the view with a specific 'AT' or 'BEFORE' clause in the consumer account. The provider account needs to inform the consumer account of a specific timestamp that guarantees consistency, adding administrative overhead.
  • C. Creating a stream on the base table in the provider account and building a view on top of the stream. This way, changes are only reflected when the stream is consumed, allowing for batch processing and controlled updates in the consumer account.
  • D. Creating a materialized view in the provider account and sharing that materialized view. This adds compute costs to the provider but ensures a consistent snapshot for the consumer account. The materialized view needs to be refreshed periodically, based on the rate of DML changes.
  • E. Creating a standard view in the provider account and relying on Snowflake's inherent transactional consistency. The consumer account will always see a consistent snapshot of the data as it existed at the beginning of their query execution. No additional configurations are necessary.

Answer: E

Explanation:
Snowflake's architecture inherently provides transactional consistency. When the consumer account queries the shared view, they will see a consistent snapshot of the data as it existed at the beginning of their query execution. No additional mechanisms like Time Travel (A) or materialized views (B) are strictly necessary to ensure consistency. While streams (D) can be useful for change data capture, they don't directly guarantee consistency for a standard view shared with a consumer. Time travel in this case would also require significant coordination overhead.


NEW QUESTION # 77
You are developing a data transformation pipeline in Snowpark Python to aggregate website traffic data'. The raw data is stored in a Snowflake table named 'website_events' , which includes columns like 'event_timestamp' , 'user_id', 'page_urr , and 'event_type'. Your goal is to calculate the number of unique users visiting each page daily and store the aggregated results in a new table named Considering performance and resource efficiency, select all the statements that are correct:

  • A. Defining the schema for the table before writing the aggregated results is crucial for ensuring data type consistency and optimal storage.
  • B. Using is the most efficient method for writing the aggregated results to Snowflake, regardless of data size.
  • C. Caching the 'website_eventS DataFrame using 'cache()' before performing the aggregation is always beneficial, especially if the data volume is large.
  • D. Applying a filter early in the pipeline to remove irrelevant 'event_type' values can significantly reduce the amount of data processed in subsequent aggregation steps.
  • E. Using followed by is an efficient approach to calculate unique users per page per day.

Answer: A,D,E

Explanation:
Option A is correct: Grouping by page URL and the date part of the timestamp, followed by a distinct count of user IDs, accurately calculates unique users per page per day. Option C is correct: Defining the schema ensures data types are correctly mapped and enforced, preventing potential issues during data loading and improving storage efficiency. Option E is correct: Filtering early reduces the data volume for subsequent operations, improving performance.


NEW QUESTION # 78
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