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DEA-C02적중율 높은 덤프자료 - DEA-C02최신 업데이트 인증공부자료

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최신 SnowPro Advanced DEA-C02 무료샘플문제 (Q279-Q284):

질문 # 279
You are developing a data pipeline to ingest customer feedback data from a third-party service using the Snowflake REST API. This service imposes rate limits, and exceeding them results in temporary blocking. To handle this, you implement exponential backoff with jitter. Which of the following code snippets BEST demonstrates how to correctly implement exponential backoff with jitter when calling the Snowflake REST API in Python, assuming data)' is a function that makes the API call and raises an exception on rate limiting?

정답：C

설명：
Option E correctly implements exponential backoff with jitter. It calculates the delay using 'base_delay 2 attempt (exponential backoff) and adds a random jitter using 'random.uniform(0, 1)'. It also handles non rate-limiting exceptions by re-raising the exception if it's not caused by rate limiting. Option A would fail to re-raise an error other than RateLimitException. Option B lacks jitter. Option C lacks both jitter and correct exponential backoff calculation. Option D does not use exponential backoff and also lacks retry logic. Therefore, option E is the correct answer.

질문 # 280
You are tasked with building a data pipeline that incrementally loads data from an external cloud storage location (AWS S3) into a Snowflake table named 'SALES DATA'. You want to optimize the pipeline for cost and performance. Which combination of Snowflake features and configurations would be MOST efficient and cost-effective for this scenario, assuming the data volume is substantial and constantly growing?

A. Use a Snowflake Task to regularly truncate and reload 'SALES DATA" from S3 using COPY INTO. This ensures data consistency.
B. Develop a custom Python script that uses the Snowflake Connector for Python to connect to Snowflake and execute a COPY INTO command. Schedule the script to run on an EC2 instance using cron.
C. Create an external stage pointing to the S3 bucket. Create a Snowpipe with auto-ingest enabled, using an AWS SNS topic and SQS queue for event notifications. Configure the pipe with an error notification integration to monitor ingestion failures.
D. Use a Snowflake Task scheduled every 5 minutes to execute a COPY INTO command from S3, with no file format specified, assuming the data is CSV and auto-detection will work.
E. Employ a third-party ETL tool to extract data from S3, transform it, and load it into Snowflake using JDBC. Schedule the ETL process using the tool's built-in scheduler.

정답：C

설명：
Snowpipe with auto-ingest is the most efficient and cost-effective solution for continuously loading data into Snowflake from cloud storage. It leverages event notifications to trigger data loading as soon as new files are available, minimizing latency and compute costs. Option A lacks error handling and proper file format specification. Option C involves custom coding and infrastructure management. Option D introduces overhead and costs associated with a third-party ETL tool. Option E is inefficient as it truncates and reloads the entire table, losing any incremental loading benefits.

질문 # 281
You're designing a near real-time data pipeline for clickstream data using Snowpipe Streaming. The data volume is extremely high, with bursts exceeding 1 million events per second. Your team reports intermittent ingestion failures and latency spikes. Considering the constraints of Snowpipe Streaming, which of the following strategies would be MOST effective in mitigating these issues, assuming the data format is optimized and network latency is minimal?

A. Reduce the size of each micro-batch being sent to Snowpipe Streaming to minimize the impact of individual failures.
B. Implement client-side retry logic with exponential backoff and jitter to handle transient errors and avoid overwhelming the service.
C. Increase the number of Snowflake virtual warehouses to handle the increased load.
D. Implement a message queue (e.g., Kafka) in front of Snowpipe Streaming to buffer incoming events and smooth out the traffic spikes.
E. Switch from Snowpipe Streaming to Classic Snowpipe, as it is more resilient to high data volumes.

정답：B,D

설명：
B and C are correct. Implementing client-side retry logic with exponential backoff (B) prevents overwhelming the service during transient errors. Using a message queue like Kafka (C) buffers the data, smoothing out traffic spikes and providing better resilience. A is less effective as scaling warehouses won't directly address client-side issues like retry logic and buffering. D can help but is not as effective as a buffering mechanism or robust retry strategy. E is incorrect as Snowpipe Streaming is designed for lower latency than classic Snowpipe.

질문 # 282
You are designing a data pipeline to ingest streaming data from Kafka into Snowflake. The data contains nested JSON structures representing customer orders. You need to transform this data and load it into a flattened Snowflake table named 'ORDERS FLAT'. Given the complexities of real-time data processing and the need for custom logic to handle certain edge cases within the JSON payload, which approach provides the MOST efficient and maintainable solution for transforming and loading this streaming data into Snowflake?

A. Create a Python UDF that calls 'json.loads()' to parse the JSON within Snowflake and then use SQL commands with 'LATERAL FLATTEN' to navigate and extract the desired fields into a staging table. Afterward, use a separate SQL script to insert from staging to the final table 'ORDERS FLAT
B. Use Snowflake's built-in JSON parsing functions within a Snowpipe COPY INTO statement, combined with a 'CREATE VIEW' statement on top of the loaded data. The view will use 'LATERAL FLATTEN' to present the data in the desired flattened structure without physically transforming the underlying data.
C. Implement a custom external function (UDF) written in Java to parse and transform the JSON data before loading it into Snowflake. Configure Snowpipe to call this UDF during the data ingestion process. This UDF will flatten the JSON structure and return a tabular format directly insertable into 'ORDERS FLAT.
D. Utilize a third-party ETL tool (like Apache Spark) to consume the data from Kafka, perform the JSON flattening and transformation logic, and then use the Snowflake connector to load the data into the 'ORDERS FLAT' table in batch mode.
E. Use Snowflake's Snowpipe with a COPY INTO statement that utilizes the 'STRIP OUTER ARRAY option to handle the JSON array, combined with a series of SQL queries with 'LATERAL FLATTEN' functions to extract the nested data after loading into a VARIANT column.

정답：C

설명：
Option B offers the most efficient and maintainable solution. Using a Java UDF allows complex JSON parsing and transformation logic to be encapsulated and optimized. Calling the UDF directly from Snowpipe ensures efficient real-time processing during data ingestion. While other options can achieve the result, they often involve unnecessary steps or performance overhead (e.g., loading into a VARIANT column and then flattening with SQL, using external ETL tools for streaming ingestion, or creating views instead of physically transforming the data).

질문 # 283
You are loading JSON data into a Snowflake table with a 'VARIANT' column. The JSON data contains nested arrays with varying depths. You need to extract specific values from the nested arrays and load them into separate columns in your Snowflake table. Which approach would provide the BEST performance and flexibility?

A. Load the entire JSON into a 'VARIANT column and then use SQL with nested 'FLATTEN' functions to extract the desired values during query time.
B. Create a view with nested 'FLATTEN' functions to extract the values from the 'VARIANT column. The view serves as the source for further transformations.
C. Use a 'COPY' command with a 'TRANSFORM' clause that uses JavaScript UDFs to parse the JSON and extract the values during the load process. Load the extracted values directly into the target columns.
D. Use Snowpipe with auto-ingest, loading directly into the table with the 'VARIANT column. Define data quality checks with pre-load data transformation.
E. Use a stored procedure to parse the JSON data and insert values into the table row by row.

정답：C

설명：
Using a 'COPY command with a 'TRANSFORM' clause and JavaScript UDFs allows for efficient parsing and extraction of values during the load process. This minimizes the amount of data stored in the 'VARIANT column and avoids expensive query-time parsing. Stored procedures perform row by row operations which are inefficient. Using Flatten functions could be useful to denormalise json, but javascript parsing during load is better. Snowpipe and auto-ingest just move the challenge to a real-time streaming scenario, which may not be optimized for transforming data into a relational structure.

질문 # 284
......

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DEA-C02시험준비최신버전 시험기출문제

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