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最新的 SnowPro Advanced DEA-C02 免費考試真題:

1. You accidentally truncated a large table named 'SALES DATA' in your 'REPORTING DB" database. You realize this happened 2 days ago, and your account has the default Time Travel retention of 1 day. You need to recover this table with minimal downtime. Analyze the situation and determine the best course of action, considering cost and recovery time.

A) Raise a support ticket requesting data recovery from failsafe. Since data retention period has expired.
B) Immediately contact Snowflake Support to initiate a restore from Fail-safe, understanding that this process may take several hours or even days.
C) Because the data retention period has expired, the table is unrecoverable using Snowflake's built-in features; you must restore from an external backup solution if available.
D) Create a clone of the table using the 'AT clause and a timestamp from 1 day ago. This would prevent any additional cost.
E) Increase the account-level to 2 days and then use the UNDROP TABLE SALES_DATA' command.

2. You're designing a data masking solution for a 'CUSTOMER' table with columns like 'CUSTOMER ID', 'NAME', 'EMAIL', and 'PHONE NUMBER. You want to implement the following requirements: 1. The 'SUPPORT' role should be able to see the last four digits of the 'PHONE NUMBER and a hashed version of the 'EMAIL'. 2. The 'MARKETING' role should be able to see the full 'NAME' and a domain-only version of the 'EMAIL' (everything after the '@' symbol). 3. All other roles should see masked values for 'EMAIL' and 'PHONE NUMBER. Which of the following masking policy definitions BEST achieves these requirements using Snowflake's built-in functions and RBAC?

A)

B)

C)

D)

E)

3. You are using Snowpark Python to transform a DataFrame 'df_orderS containing order data'. You need to filter the DataFrame to include only orders with a total amount greater than $1000 and placed within the last 30 days. Assume the DataFrame has columns 'order_id', 'order_date' (timestamp), and 'total_amount' (numeric). Which of the following code snippets is the MOST efficient and correct way to achieve this filtering using Snowpark?

A) Option E
B) Option B
C) Option C
D) Option A
E) Option D

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

5. You have an external table named in Snowflake that points to a set of CSV files in an AWS S3 bucket. The CSV files have a header row, and the data is comma-separated. However, some of the files in the S3 bucket are gzipped. You need to define the external table to correctly read both compressed and uncompressed files. Which of the following SQL statements BEST achieves this?

A) Option E
B) Option B
C) Option C
D) Option A
E) Option D

問題與答案：