PASS GUARANTEED 2025 SNOWFLAKE AUTHORITATIVE DEA-C02: SNOWPRO ADVANCED: DATA ENGINEER (DEA-C02) VALID EXAM QUESTION

Pass Guaranteed 2025 Snowflake Authoritative DEA-C02: SnowPro Advanced: Data Engineer (DEA-C02) Valid Exam Question

Pass Guaranteed 2025 Snowflake Authoritative DEA-C02: SnowPro Advanced: Data Engineer (DEA-C02) Valid Exam Question

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Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions (Q336-Q341):

NEW QUESTION # 336
A Snowflake table 'ORDERS' contains billions of records and is frequently queried for reporting purposes. The reporting queries often filter on 'ORDER DATE and 'CUSTOMER ID'. The data engineering team is considering creating a clustering key to improve query performance. They are evaluating two options: (1) clustering on 'ORDER DATE' alone and (2) clustering on '(ORDER DATE, CUSTOMER ID)'. Which of the following statements best describes the trade-offs between these two options in the context of query performance and data maintenance?

  • A. Clustering on ORDER DATE alone is preferable because it eliminates the risk of data skewness associated with 'CUSTOMER ID, leading to more balanced micro-partitions and consistent query performance.
  • B. Clustering on 'ORDER_DATE alone will result in better overall query performance because it's a single dimension, simplifying the clustering process and reducing the need for Snowflake to perform complex data scans.
  • C. Clustering on ' (ORDER DATE, CUSTOMER_ID)' will provide better performance for queries filtering on both columns, but may lead to increased reclustering costs if distribution is skewed within "ORDER_DATE' partitions.
  • D. Clustering on '(ORDER DATE, CUSTOMER IDY is always the best option because it allows for more granular filtering and reduces the need to scan unnecessary micro-partitions, regardless of data distribution.
  • E. Neither clustering option will significantly improve performance, and the team should focus on optimizing the queries themselves through techniques like query rewriting and the use of appropriate indexes.

Answer: C

Explanation:
Clustering on multiple columns (in this case, 'ORDER_DATE' and 'CUSTOMER_ID') improves performance for queries that filter on both columns. However, if the distribution of is uneven within the 'ORDER DATE partitions (skew), it can lead to some micro-partitions still containing a large range of 'CUSTOMER_ID' values, requiring Snowflake to scan more data. This can also increase reclustering costs as Snowflake tries to optimize the clustering. Clustering on only may be beneficial if most queries primarily filter on date. Option E is incorrect because clustering is a Snowflake feature that improves performance.


NEW QUESTION # 337
You're designing a data pipeline in Snowflake that utilizes an external function to perform sentiment analysis on customer reviews using a third-party NLP service. This service charges per request. You need to minimize costs while ensuring timely processing of the reviews.
Which of the following strategies would be most effective in optimizing the cost and performance of your external function?

  • A. Pre-process the customer reviews in Snowflake to filter out irrelevant reviews (e.g., very short reviews or reviews with stop words) before sending them to the external function.
  • B. Implement a caching mechanism (e.g., using a Snowflake table or an external cache) to store the sentiment analysis results for frequently occurring reviews or similar text patterns, avoiding redundant API calls.
  • C. Implement rate limiting and error handling in the external service (e.g., AWS Lambda or Azure Function) to gracefully handle API usage limits and prevent excessive charges due to errors.
  • D. Bypass the external function completely and rely solely on Snowflake's built-in NLP functions for sentiment analysis.
  • E. Set 'MAX BATCH_ROWS' to a very high value (e.g., 10000) to maximize the number of rows processed per API call, even if it increases latency for individual reviews.

Answer: A,B,C

Explanation:
The correct answers are B, C, and D. Option B avoids redundant API calls by caching results. Option C handles API rate limits to prevent excessive charges. Option D reduces the number of API calls by pre-filtering irrelevant reviews. Option A will increase latency and is not a cost optimization. Option E is not always feasible if Snowflake's built-in functions are insufficient.


NEW QUESTION # 338
You need to unload data from a Snowflake table named 'CUSTOMER DATA to an AWS S3 bucket The data should be unloaded in Parquet format, partitioned by the 'CUSTOMER REGION' column, and automatically compressed with GZIP. Furthermore, you only want to unload customers whose 'REGISTRATION DATE is after '2023-01-01'. Which of the following 'COPY INTO' statement correctly achieves this?

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

Answer: B

Explanation:
The correct 'COPY INTO' statement requires using a named stage and a named file format. A subquery is used to filter the data based on the 'REGISTRATION_DATE. The 'PARTITION BY clause is used to partition the data by 'CUSTOMER REGION'. You must create a FILE FORMAT seperately and refer to it later. Other solutions have syntax errors, incorrect stage references, or incorrect ordering of clauses. Option 'A' doesn't use a stage nor it allows for a where condition. Option 'B' doesn't work since 'TYPE' and 'COMPRESSIONS are properties of a file format, not direct arguments to FILE_FORMAT. 'C' includes the 'TYPE and 'COMPRESSION' inline when this is not allowed. 'D' contains the same error with FILE FORMAT as 'C' and 'B' and does not use a stage.


NEW QUESTION # 339
A Snowflake data warehouse contains a table named 'SALES TRANSACTIONS' with the following columns: 'TRANSACTION ID', 'PRODUCT D', 'CUSTOMER D', 'TRANSACTION DATE, and 'SALES AMOUNT'. You need to optimize a query that calculates the total sales amount per product for a given month. The 'SALES TRANSACTIONS' table is very large (billions of rows), and queries are slow. Given the following initial query: SELECT PRODUCT ID, SUM(SALES AMOUNT) AS TOTAL SALES FROM SALES TRANSACTIONS WHERE TRANSACTION DATE BETWEEN '2023-01-07' AND '2023-01-31' GäOUP BY PRODUCT ID; Which of the following actions, when combined, would MOST effectively improve the performance of this query?

  • A. Convert the column to a VARCHAR data type.
  • B. Create a clustering key on 'PRODUCT_ID and 'TRANSACTION_DATE columns in the 'SALES_TRANSACTIONS' table.
  • C. Increase the virtual warehouse size to the largest available size.
  • D. Create a temporary table with the results of the query and query that table instead.
  • E. Create a materialized view that pre-aggregates the total sales amount per product and month.

Answer: B,E

Explanation:
Creating a clustering key on 'PRODUCT ID and 'TRANSACTION DATE' allows Snowflake to efficiently prune micro-partitions based on the date range filter, and then quickly group by "PRODUCT_ID. A materialized view pre-aggregates the data, significantly reducing the amount of computation required at query time. While increasing the warehouse size might provide some improvement, it is not the most efficient solution. Converting 'TRANSACTION_DATE to VARCHAR is detrimental. Using a temporary table is not necessarily an optimization.


NEW QUESTION # 340
Your company has a Snowflake account in the AWS cloud (us-west-2). You are planning to implement a disaster recovery strategy by replicating data to a separate Snowflake account in the Azure cloud (eastus2). You need to replicate multiple databases and shared objects. Which of the following steps are REQUIRED to configure and manage the replication process successfully? (Choose all that apply)

  • A. Create a storage integration in the target Azure account and grant the 'USAGE privilege on it to the replication group.
  • B. Create a replication group in the source AWS account and add the databases and shared objects to it.
  • C. Create a secondary database in the target Azure account using the 'CREATE DATABASE AS REPLICA OF command.
  • D. Configure network policies in both AWS and Azure accounts to allow communication between the Snowflake instances, particularly ingress and egress rules.
  • E. Grant the REPLICATE privilege on the source AWS account to the account locator of the target Azure account.

Answer: B,D,E

Explanation:
A, C, and D are required steps. Option A: Creating a replication group is essential to define what to replicate. Option C: The REPLICATE privilege allows the target account to pull data from the source. Option D: Network policies are crucial for establishing secure communication. Option B is incorrect; you create a secondary database using 'CREATE DATABASE AS REPLICA OF : after enabling replication on the source, not before. Option E is related to data loading from external stages, not replication itself in this direct account-to-account scenario.


NEW QUESTION # 341
......

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