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100%유효한DEA-C02참고자료시험자료

ExamPassdump를 선택함으로, ExamPassdump는 여러분Snowflake인증DEA-C02시험을 패스할 수 있도록 보장하고,만약 시험실패시 ExamPassdump에서는 덤프비용전액환불을 약속합니다.

Snowflake DEA-C02 인증시험은 최근 가장 핫한 시험입니다. 인기가 높은 만큼Snowflake DEA-C02시험을 패스하여 취득하게 되는 자격증의 가치가 높습니다. 이렇게 좋은 자격증을 취득하는데 있어서의 필수과목인Snowflake DEA-C02시험을 어떻게 하면 한번에 패스할수 있을가요? 그 비결은 바로ExamPassdump의 Snowflake DEA-C02덤프를 주문하여 가장 빠른 시일내에 덤프를 마스터하여 시험을 패스하는것입니다.

>> DEA-C02참고자료 <<

DEA-C02퍼펙트 덤프공부문제 & DEA-C02학습자료

ExamPassdump는 고객님께서 첫번째Snowflake DEA-C02시험에서 패스할수 있도록 최선을 다하고 있습니다. 만일 어떤 이유로 인해 고객이 첫 번째 시도에서 실패를 한다면, ExamPassdump는 고객에게Snowflake DEA-C02덤프비용 전액을 환불 해드립니다.환불보상은 다음의 필수적인 정보들을 전제로 합니다.

최신 SnowPro Advanced DEA-C02 무료샘플문제 (Q201-Q206):

질문 # 201
You are working with a Snowpark DataFrame named 'customer data' that contains sensitive Personally Identifiable Information (PII). The DataFrame has columns such as 'customer id', 'name', 'email' , and 'phone number'. Your task is to create a new DataFrame that only contains 'customer id' and a hash of the 'email' address for anonymization purposes, while also filtering out any customers whose 'customer id' starts with 'TEMP'. Which of the following approaches adheres to best practices for data security and efficiency in Snowpark, using secure hashing algorithms provided by Snowflake?

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

정답：B

설명：
Option D is the most appropriate. 'sha2 with a bit length of 256 or higher (like 256 in this example) is a strong cryptographic hash function suitable for anonymizing sensitive data. The 'where' function is used with the negation of the 'startswith' function (through column reference 'col()'), so it appropriately filters out customer IDs starting with 'TEMP. Using 'select' projects only the necessary columns, minimizing the risk of exposing other PII data. Option A utilizes the 'filter' and provides the correct filter. Option C attempts to utilize However, cache_result() is not suitable for this task. Option B, however, is suboptimal because MD5 is considered cryptographically broken and should not be used for security-sensitive applications. Options A and E are technically correct in filtering out customer IDs. They are not as clear as Option D. The code will accomplish the objective of the question but not clearly show which customer IDs will be retained.

질문 # 202
You are working on a Snowpark Python application that needs to process a stream of data from Kafka, perform real-time aggregations, and store the results in a Snowflake table. The data stream is highly variable, with occasional spikes in traffic that overwhelm your current Snowpark setup, leading to significant latency in processing. Which of the following strategies, either individually or in combination, would be MOST effective to handle these traffic spikes and ensure near real-time processing?

• A. Use 'CACHE RESULT for all queries in snowpark that use Kafka
• B. Configure the Snowflake warehouse used by your Snowpark application to use auto-suspend and auto-resume with a short auto-suspend time to minimize costs during periods of low traffic.
• C. Implement a message queuing system (e.g., RabbitMQ, Kafka) between Kafka and your Snowpark application to buffer incoming data during traffic spikes.
• D. Use Snowpark's async actions (e.g., to offload data processing to separate threads or processes, allowing your main Snowpark application to continue receiving data.
• E. Implement dynamic warehouse scaling. Utilize Snowflake's Resource Monitors and the ability to programmatically resize warehouses through Snowpark. Monitor the queue depth or latency of your Snowpark application, and dynamically scale up the warehouse size when thresholds are exceeded. Then, scale it back down when traffic subsides.

정답：C,E

설명：
Options A and D offer the best approach. Implementing a message queue (A) provides a buffer for incoming data during spikes, preventing your Snowpark application from being overwhelmed. Dynamic warehouse scaling (D) allows you to automatically increase the compute resources available to your Snowpark application when needed, ensuring it can handle the increased workload. Auto suspend/resume (B) is good for cost optimization but doesn't address the processing capacity during spikes. Async actions (C) can help, but are not as scalable or resilient as a proper message queue combined with dynamic warehouse scaling. Caching results (E) is irrelevant since the data from Kafka is always changing.

질문 # 203
You are using Snowpipe with an external function to transform data as it is loaded into Snowflake. The Snowpipe is configured to load data from AWS SQS and S3. You observe that some messages are not being processed by the external function, and the data is not appearing in the target table. You have verified that the Snowpipe is enabled and the SQS queue is receiving notifications. Analyze the following potential causes and select all that apply:

• A. The Snowpipe configuration is missing a setting that allows the external function to access the data files in S3. Ensure that the storage integration is configured to allow access to the S3 location.
• B. The external function is experiencing timeouts or errors, causing it to reject some records. Review the external function logs and increase the timeout settings if necessary.
• C. The data being loaded into Snowflake does not conform to the expected format for the external function. Validate the structure and content of the data before loading it into Snowflake.
• D. The AWS Lambda function (or other external function) does not have sufficient memory or resources to process the incoming data volume, leading to function invocations being throttled and messages remaining unprocessed.
• E. The IAM role associated with the Snowflake stage does not have permission to invoke the external function. Verify that the role has the necessary permissions in AWS IAM.

정답：B,C,D,E

설명：
When using Snowpipe with external functions, several factors can cause messages to be dropped or unprocessed. The most common include external function errors or timeouts (A), permission issues between Snowflake and the external function (B), data format mismatches (C), and the external function lacking resources (E) leading to throttling. Option D is less likely, as the storage integration is primarily for COPY INTO and not direct Lambda function calls, assuming the Lambda function retrieves the data directly from S3 using the event data provided by SQS. The permissions issue B is still relevant as the lambda function will need access to the files in S3.

질문 # 204
You are developing a Snowpark Python stored procedure that performs complex data transformations on a large dataset stored in a Snowflake table named 'RAW SALES'. The procedure needs to efficiently handle data skew and leverage Snowflake's distributed processing capabilities. You have the following code snippet:

Which of the following strategies would be MOST effective to optimize the performance of this Snowpark stored procedure, specifically addressing potential data skew in the 'product id' column, assuming 'product_id' is known to cause uneven data distribution across Snowflake's micro-partitions?

• A. Implement a custom partitioning strategy using before the transformation logic to redistribute data evenly across the cluster.
• B. Increase the warehouse size significantly to compensate for the data skew and improve overall processing speed without modifying the partitioning strategy.
• C. Use the 'pandas' API within the Snowpark stored procedure to perform the transformation, as 'pandas' automatically optimizes for data skew.
• D. Utilize Snowflake's automatic clustering on the 'TRANSFORMED_SALES table by specifying 'CLUSTER BY when creating or altering the table to ensure future data is efficiently accessed.
• E. Combine salting with repartitioning by adding a random number to the 'product_id' before repartitioning, then removing the salt after the transformation to break up the skew. Then, enable automatic clustering on the 'TRANSFORMED SALES' table.

정답：E

설명：
Option E is the most effective solution. Salting breaks up data skew before repartitioning. Automatic clustering on the transformed table optimizes future queries. Repartitioning redistributes the data across Snowflake's processing nodes, and Automatic Clustering will help in maintaining performance as the data changes in TRANSFORMED_SALES table over time. Option A, without salting, may still be inefficient due to the initial skew. Option B improves query performance but doesn't address the initial transformation skew. Option C is incorrect because 'pandas' in Snowpark does not automatically handle data skew at the Snowflake level. Option D is a costly workaround that doesn't fundamentally solve the skew problem.

질문 # 205
You are implementing a data share between two Snowflake accounts. The provider account wants to grant the consumer account access to a function that returns anonymized customer data based on a complex algorithm. The provider wants to ensure that the consumer cannot see the underlying implementation details of the anonymization algorithm. Which of the following approaches can achieve this goal? (Select TWO)

• A. Create a view that calls the secure UDF and share that view with the consumer account.
• B. Create a standard UDF in the provider account and grant usage on the UDF to the share. Share the share with the consumer account.
• C. Share the underlying table and provide the consumer account with the anonymization algorithm separately.
• D. Create an external function in the provider account and grant usage to the share. Share the share with the consumer account.
• E. Create a secure UDF in the provider account and grant usage on the secure UDF to the share. Share the share with the consumer account.

정답：A,E

설명：
A secure UDF hides the underlying implementation details from the consumer. Option 'A' achieves this directly. Creating a view (Option 'D') that calls the secure UDF provides another layer of abstraction, further protecting the algorithm's implementation. A standard UDF (Option B) does not hide the implementation. Sharing the table directly (Option C) defeats the purpose of anonymization. While external functions exist (Option E), they would be unnecessarily complex in this scenario, which can be achieved natively through secure UDF and View combination.

질문 # 206
......

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