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最新的 SnowPro Advanced DEA-C02 免費考試真題:

1. You are setting up a Kafka connector to load data from a Kafka topic into a Snowflake table. You want to use Snowflake's automatic schema evolution feature to handle potential schema changes in the Kafka topic. Which of the following is the correct approach to enable and configure automatic schema evolution using the Kafka Connector for Snowflake?

A) Set the 'snowflake.data.field.name' property to the name of the column in the Snowflake table where the JSON data will be stored as a VARIANT, and set 'snowflake.enable.schematization' to 'true'.
B) Automatic schema evolution is not directly supported by the Kafka Connector for Snowflake. You must manually manage schema changes in Snowflake.
C) Set 'snowflake.ingest.file.name' to an existing file in a stage.
D) Set the 'value.converter.schemas.enable' to 'true' and provide Avro schemas and also, configure the Snowflake table with appropriate data types for each field. Schema Evolution is not supported by the Kafka Connector for Snowflake.
E) Set the property to 'true' and the 'snowflake.ingest.stage' to an existing stage.

2. You are developing a Snowpark Python application that needs to process data from a Kafka topic. The data is structured as Avro records. You want to leverage Snowpipe for ingestion and Snowpark DataFrames for transformation. What is the MOST efficient and scalable approach to integrate these components?

A) Create a Kafka connector that directly writes Avro data to a Snowflake table. Then, use Snowpark DataFrames to read and transform the data from that table.
B) Create external functions to pull the Avro data into a Snowflake stage and then read the data with Snowpark DataFrames for transformation.
C) Use Snowpipe to ingest the Avro data to a raw table stored as binary. Then, use a Snowpark Python UDF with an Avro deserialization library to convert the binary data to a Snowpark DataFrame.
D) Convert Avro data to JSON using a Kafka Streams application before ingestion. Use Snowpipe to ingest the JSON data to a VARIANT column and then process it using Snowpark DataFrames.
E) Configure Snowpipe to ingest the raw Avro data into a VARIANT column in a staging table. Utilize a Snowpark DataFrame with Snowflake's get_object field function on the variant to get an object by name, and create columns based on each field.

3. A data engineer is implementing a data governance policy that requires masking PII data in non-production environments. They have identified a column 'CUSTOMER EMAIL' that needs to be masked. They want to use dynamic data masking in Snowflake, but the 'CUSTOMER EMAIL' column is referenced in several views. Which of the following approaches is MOST appropriate and avoids breaking the existing views?

A) Create a masking policy directly on the 'CUSTOMER EMAIL' column in the base table. This will automatically apply the masking to all views referencing the column.
B) Create a masking policy on the base table but use a context function in the masking policy condition to check the database name. Mask the data only when the database name is the non-production database.
C) Create masking policies on each of the individual views that reference the 'CUSTOMER EMAIL' column, using the same masking function.
D) Create a masking policy on the base table, but exclude the role used by the views from the policy's condition. This will prevent masking for those specific views.
E) Create a separate view that applies the masking function to the 'CUSTOMER EMAIL' column. Replace all existing views with the new masked view.

4. A data engineer is tasked with creating a Snowpark Python UDF to perform sentiment analysis on customer reviews. The UDF, named 'analyze_sentiment' , takes a string as input and returns a string indicating the sentiment ('Positive', 'Negative', or 'Neutral'). The engineer wants to leverage a pre-trained machine learning model stored in a Snowflake stage called 'models'. Which of the following code snippets correctly registers and uses this UDF?

A) Option E
B) Option B
C) Option C
D) Option A
E) Option D

5. 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) 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.
B) 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.
C) 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.
D) 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.
E) 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.

問題與答案：