DEA-C02 무료 덤프문제 온라인 액세스

You are developing a data pipeline that uses Snowpipe Streaming to ingest JSON data into a Snowflake table. Some JSON documents contain nested arrays and complex structures. You need to flatten the JSON structure during ingestion to simplify querying. Consider the following JSON document: { "order id": 123, "customer": { "id": "cust123", "name": "John Doe", "address": { "street": "123 Main St", "city": "Anytown" } }, "items": [ {"product_id": "prodl", "quantity": 2}, {"product_id": "prod2", "quantity": 1} ] } Which approach would you use within the 'COPY INTO' statement of your Snowpipe to flatten this JSON structure during ingestion?

• A.Use the ' FLATTEN()' table function directly within the 'COPY INTO' statement to expand the 'items' array and extract nested fields. For nested objects, use dot notation directly in the SELECT list (e.g., 'customer.name').
• B.Use JavaScript UDFs within the 'COPY INTO' statement to recursively flatten the JSON structure.
• C.Create a separate transformation pipeline using Snowflake Tasks to flatten the data after it is ingested into the table.
• D.Snowpipe and the 'COPY INTO' command automatically flattens JSON data during ingestion; no additional steps are required.
• E.Pre-process the JSON documents before loading them into the stage using a custom script to flatten the structure.

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

You're configuring a Kafka connector to load data into Snowflake. You need to ensure that only records with a specific 'event_type' field equal to 'payment_processed' are loaded into the 'PAYMENT TABLE in Snowflake. Given the following connector configuration snippet, which additional property must be added to the 'transforms' section to achieve this filtering?

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

A data engineering team is running a series of complex analytical queries against a large Snowflake table. They notice that query performance is inconsistent, with some queries running much slower than others. After investigation, they determine that the queries are not properly leveraging the data clustering. Which of the following actions could improve the query performance related to the data clustering? Select all that apply.

• A.Run 'SHOW TABLES LIKE ' ' and review the 'clustering_information' column to understand the current clustering depth and benefit.
• B.Execute SYSTEM$CLUSTERING INFORMATION( ' )' y to assess the impact of clustering on query performance and determine if re-clustering is needed.
• C.Run 'ALTER TABLE CLUSTER BY (columnl, column2Y to explicitly define a clustering key on the table.
• D.Increase the virtual warehouse size to improve the performance of all queries, regardless of clustering.
• E.Run 'ALTER TABLE DROP CLUSTERING KEY to remove the existing clustering key and allow Snowflake to automatically cluster the data.

You are designing a data pipeline that uses the Snowflake SQLAPI to execute a series of complex SQL queries. These queries involve multiple joins, aggregations, and user-defined functions (UDFs). You need to ensure that the pipeline is resilient to transient network errors and can handle a large volume of concurrent requests. Which of the following strategies would you implement to enhance the reliability and performance of your pipeline?

• A.Increase the timeout value for the SQL API requests to accommodate potentially long-running queries.
• B.Bundle all the SQL queries into a single, large SQL statement to reduce the number of API calls.
• C.Implement exponential backoff and retry logic in your client application to handle transient errors when calling the SQL API.
• D.Disable query result caching in Snowflake to ensure that the pipeline always retrieves the latest data.
• E.Use the SQL API's asynchronous execution mode and poll for query status to handle long-running queries without blocking.