Snowflake DEA-C02 - PDF電子當

DEA-C02 pdf
  • 考試編碼:DEA-C02
  • 考試名稱:SnowPro Advanced: Data Engineer (DEA-C02)
  • 更新時間:2025-06-29
  • 問題數量:354 題
  • PDF價格: $59.98
  • 電子當(PDF)試用

Snowflake DEA-C02 超值套裝
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DEA-C02 Online Test Engine

在線測試引擎支持 Windows / Mac / Android / iOS 等, 因爲它是基於Web瀏覽器的軟件。

  • 考試編碼:DEA-C02
  • 考試名稱:SnowPro Advanced: Data Engineer (DEA-C02)
  • 更新時間:2025-06-29
  • 問題數量:354 題
  • PDF電子當 + 軟件版 + 在線測試引擎(免費送)
  • 套餐價格: $119.96  $79.98
  • 節省 50%

Snowflake DEA-C02 - 軟件版

DEA-C02 Testing Engine
  • 考試編碼:DEA-C02
  • 考試名稱:SnowPro Advanced: Data Engineer (DEA-C02)
  • 更新時間:2025-06-29
  • 問題數量:354 題
  • 軟件版價格: $59.98
  • 軟件版

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Free Download DEA-C02 pdf braindumps

最新的 SnowPro Advanced DEA-C02 免費考試真題:

1. A critical database, 'PRODUCTION DB', in your Snowflake account was accidentally dropped. You need to restore it as quickly as possible, but you're unsure if Time Travel retention is sufficient. Which method guarantees restoration of the database even if it falls outside the Time Travel window?

A) Restore from a Snowflake-managed backup using the 'CREATE DATABASE ... FROM BACKUP' command. Specify the timestamp before the drop occurred.
B) Fail-safe cannot be directly accessed by the user for restoration purposes; it is only used by Snowflake Support in extreme disaster recovery scenarios.
C) Utilize the data cloning feature: 'CREATE DATABASE CLONE PRODUCTION_DB BEFORE (STATEMENT 'DROP DATABASE PRODUCTION_DB');'
D) Use the 'UNDROP DATABASE PRODUCTION command.
E) Contact Snowflake Support and request restoration from Fail-safe.


2. You are developing a Snowpark Python application that reads data from a large Snowflake table, performs several transformations, and then writes the results back to a new table. You notice that the write operation is taking significantly longer than the read and transformation steps. The target table is not clustered. Which of the following actions, either individually or in combination, would likely improve the write performance most significantly ?

A) Use the FILE SIZE', value)' method to reduce the size of the output files, potentially leading to more parallelism during the write operation.
B) Disable auto-tuning for the warehouse to ensure consistent performance
C) Cluster the target table on the primary key before writing to it. Then, ensure the data being written is pre-sorted according to the clustering key.
D) Use the 'DataFrame.repartition(numPartitions)' method before writing to the table. Choose a 'numPartitionS value that is significantly higher than the number of virtual warehouses in your warehouse size.
E) Increase the size of the Snowflake warehouse used for the Snowpark session.


3. You are using Snowpark Python to perform data transformation on a large dataset stored in a Snowflake table named customer transactions'. This table contains columns such as 'customer id', 'transaction date', 'transaction amount', and product_category'. Your task is to identify customers who have made transactions in more than one product category within the last 30 days. Which of the following Snowpark Python snippets is the most efficient way to achieve this, minimizing data shuffling and maximizing query performance?

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


4. You are developing a Snowpark Python application that processes data from a large table. You want to optimize the performance by leveraging Snowpark's data skipping capabilities. The table 'CUSTOMER ORDERS is partitioned by 'ORDER DATE. Which of the following Snowpark operations will MOST effectively utilize data skipping during data transformation?

A) Executing 'df.collect()' to load the entire table into the client's memory before filtering.
B) Using the 'cache()' method on the DataFrame before filtering by 'ORDER DATE
C) Applying a filter '2023-01-01') & '2023-03-31'))' before performing any join or aggregation operations.
D) Creating a new DataFrame with only the columns needed using 'ORDER_DATE', 'ORDER_AMOUNT')' before any filtering operations.
E) Applying a filter >= '2023-01-01') & (col('ORDER_DATE') <= '2023-03-31'))' after performing a complex join operation.


5. You are designing a data pipeline that involves unloading large amounts of data (hundreds of terabytes) from Snowflake to AWS S3 for archival purposes. To optimize cost and performance, which of the following strategies should you consider? (Select ALL that apply)

A) Partition the data during the unload operation based on a high-cardinality column to maximize parallelism in S3.
B) Choose a file format such as Parquet or ORC with compression enabled to reduce storage costs and improve query performance in S3.
C) Enable client-side encryption with KMS in S3 and specify the encryption key in the 'COPY INTO' command to enhance security.
D) Utilize the 'MAX FILE SIZE parameter in the 'COPY INTO' command to control the size of individual files unloaded to S3. Smaller files generally improve query performance in S3.
E) Use a large Snowflake warehouse size to parallelize the unload operation and reduce the overall unload time.


問題與答案:

問題 #1
答案: B
問題 #2
答案: C
問題 #3
答案: A
問題 #4
答案: C
問題 #5
答案: B,C,E

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