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[General] DEA-C02 Cert - DEA-C02 New Dumps Ppt

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【General】 DEA-C02 Cert - DEA-C02 New Dumps Ppt

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Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions (Q83-Q88):NEW QUESTION # 83
You have a Snowflake table 'orders_raw' with a VARIANT column named 'order detailS that contains an array of order items represented as JSON objects. Each object has 'item id', 'quantity' , and 'price'. You need to calculate the total revenue for each order. Which SQL statement efficiently flattens the array and calculates the total revenue using LATERAL FLATTEN and appropriate casting?

  • A. Option B
  • B. Option A
  • C. Option C
  • D. Option D
  • E. Option E
Answer: E
Explanation:
Option E is the most efficient and correct. It uses 'LATERAL FLATTEN' to unnest the 'order_details' array. It then casts both the quantity' and 'price' fields to FLOAT, ensuring accurate calculations for total revenue. A, B and D are incorrect due to incorrect join syntax or function usage with lateral flatten, or improper datatypes. C doesn't properly flatten the array so it only accesses the first element.

NEW QUESTION # 84
You are designing a data pipeline using Snowpipe to ingest data from multiple S3 buckets into a single Snowflake table. Each S3 bucket represents a different data source and contains files in JSON format. You want to use Snowpipe's auto-ingest feature and a single Snowpipe object for all buckets to simplify management and reduce overhead. However, each data source has a different JSON schem a. How can you best achieve this goal while ensuring data is loaded correctly and efficiently into the target table?
  • A. Since Snowpipe cannot handle multiple schemas with a single pipe, pre-process the data in S3 using an AWS Lambda function to transform all files into a common schema before they are ingested by the Snowpipe.
  • B. Use a single Snowpipe and leverage Snowflake's ability to call a user-defined function (UDF) within the 'COPY INTO' statement to transform the data based on the S3 bucket path. The UDF can parse the bucket path and apply the appropriate JSON schema transformation.
  • C. Create a separate Snowpipe for each S3 bucket. Although this creates more Snowpipe objects, it allows you to specify a different FILE FORMAT and transformation logic for each data source.
  • D. Use a single Snowpipe with a generic FILE FORMAT that can handle all possible JSON schemas. Implement a VIEW on top of the target table to transform and restructure the data based on the source bucket.
  • E. Use a single Snowpipe and leverage Snowflake's VARIANT data type to store the raw JSON data. Create separate external tables, each pointing to a specific S3 bucket, and use SQL queries to transform and load the data into the target table.
Answer: B
Explanation:
The most efficient and manageable approach is to use a single Snowpipe with a UDF to handle schema variations. The UDF can inspect the S3 bucket path (available as metadata within the 'COPY INTO' statement) and apply the correct transformation logic for each data source. Creating separate Snowpipes (A) adds unnecessary overhead. Using a generic 'FILE FORMAT and a VIEW (B) might work for simple transformations, but it becomes complex with significant schema differences. Using VARIANT and external tables (C) defeats the purpose of Snowpipe. Pre-processing in S3 (E) adds complexity outside of Snowflake. UDF provides schema flexibility during ingest and leverages Snowpipe's capabilities directly.

NEW QUESTION # 85
You are tasked with ingesting data from an external stage into Snowflake. The data is in JSON format and compressed using GZIP. The JSON files contain nested arrays. You need to create a file format object that Snowflake can use to properly parse the dat a. Which of the following options represents the MOST efficient and correct file format definition to achieve this? Assume the stage is already created and accessible.

  • A. Option A
  • B. Option C
  • C. Option D
  • D. Option E
  • E. Option B
Answer: E
Explanation:
Option B is the most efficient and correct. The COMPRESSION = 'GZIP' parameter is necessary to handle the GZIP compression. 'STRIP OUTER ARRAY = TRUE is crucial when dealing with JSON files containing nested arrays or a single outer array, which is a common scenario for data coming from external sources. It allows Snowflake to correctly parse each element of the array as a separate row. Option A is incorrect because it doesn't address the potential outer array structure. Option C is incorrect because 'ENABLE OCTAL' is not relevant to the array structure issue. Option D is incorrect as it lacks the compression parameter. Option E is also suitable , but gzip is better performance than AUTO.

NEW QUESTION # 86
You have a Snowpark Python application that performs complex calculations on a large dataset stored in Snowflake. The application is currently running slowly. After profiling, you've identified that the UDFs you're using are the bottleneck. These UDFs perform custom data transformations using a third-party Python library which has a significant initialization overhead. Which of the following strategies would be MOST effective to optimize performance, minimizing both runtime and resource consumption?
  • A. Rewrite the UDFs in SQL using Snowflake's built-in functions to avoid the overhead of Python execution. If the library's functions aren't available, consider creating external functions using a cloud provider's serverless compute service.
  • B. Increase the size of the Snowflake warehouse being used for the Snowpark workload. This will provide more CPU and memory resources.
  • C. Convert the Snowpark Python application to a Snowpark Java application as Java generally offers better performance than Python.
  • D. Use Snowpark's 'pandas_udf with 'vectorized=True' and pre-initialize the third-party library within the UDF's execution context using a closure or similar technique for reuse across batches.
  • E. Implement UDF caching at the Snowflake level by setting the 'VOLATILE property to 'IMMUTABLE or 'STABLE' (if appropriate), and leverage the Snowflake query result cache.
Answer: D
Explanation:
Option C is the most effective. 'pandas_udf with 'vectorized=True' allows processing data in batches using pandas DataFrames, significantly reducing the overhead of invoking the UDF for each row. Pre-initializing the library within the UDF's closure avoids repeated initialization. Increasing warehouse size (A) might help but is not as targeted. UDF caching (B) only helps if the inputs are identical and doesn't address the initialization overhead. Rewriting in SQL (D) might not be feasible if the third-party library is essential. Converting to Java (E) could help, but optimizing the Python code first is generally a better starting point.

NEW QUESTION # 87
You are configuring a Snowflake Data Clean Room for two healthcare providers, 'ProviderA' and 'ProviderB', to analyze patient overlap without revealing Personally Identifiable Information (PII). Both providers have patient data in their respective Snowflake accounts, including a 'PATIENT ID' column that uniquely identifies each patient. You need to create a secure join that allows the providers to determine the number of shared patients while protecting the raw 'PATIENT ID' values. Which of the following approaches is the most secure and efficient way to achieve this using Snowflake features? Select TWO options.
  • A. Leverage Snowflake's differential privacy features to add noise to the patient ID data, share the modified dataset and perform a JOIN.
  • B. Create a hash of the 'PATIENT_ID' column in both ProviderA's and ProviderB's accounts using a consistent hashing algorithm (e.g., SHA256) and a secret salt known only to both providers. Share the hashed values through a secure view and perform a JOIN operation on the hashed values.
  • C. Share the raw 'PATIENT_ID' columns between ProviderA and ProviderB using secure data sharing, and then perform a JOIN operation in either ProviderA's or ProviderB's account.
  • D. Implement tokenization of the 'PATIENT_ID' column in both ProviderA's and ProviderB's accounts. Share the tokenized values through a secure view and perform a JOIN operation on the tokens. Use a third party to deanonymize the tokens afterwards.
  • E. Utilize Snowflake's Secure Aggregate functions (e.g., APPROX_COUNT_DISTINCT) on the 'PATIENT_ID' column without sharing the underlying data. Each provider calculates the approximate distinct count of patient IDs, and the results are compared to estimate the overlap.
Answer: B,D
Explanation:
Options B and C represents valid approach. B provides good utility and is consistent. C does the same using a third-party service, which also works. Option A exposes the raw PII data which is not acceptable. Option D only gets an approximate, not an exact figure. While useful, the other solutions are much better. Option E is incorrect, it sounds good, but is not real. Therefore the correct answer is B and C.

NEW QUESTION # 88
......
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