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Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions (Q336-Q341):

NEW QUESTION # 336
You are developing a JavaScript UDF in Snowflake to perform complex data validation on incoming data'. The UDF needs to validate multiple fields against different criteria, including checking for null values, data type validation, and range checks. Furthermore, you need to return a JSON object containing the validation results for each field, indicating whether each field is valid or not and providing an error message if invalid. Which approach is the MOST efficient and maintainable way to structure your JavaScript UDF to achieve this?

A. Utilize a JavaScript library like Lodash or Underscore.js within the UDF to perform data manipulation and validation. Return a JSON string containing the validation results.
B. Directly embed SQL queries within the JavaScript UDF to perform data validation checks using Snowflake's built-in functions. Return a JSON string containing the validation results.
C. Define a JavaScript object containing validation rules and corresponding validation functions. Iterate through the object and apply the rules to the input data, collecting the validation results in a JSON object. This object is returned as a string.
D. Use a single, monolithic JavaScript function with nested if-else statements to handle all validation logic. Return a JSON string containing the validation results.
E. Create separate JavaScript functions for each validation check (e.g., 'isNull', 'isValidType', 'isWithinRange'). Call these functions from the main UDF and aggregate the results into a JSON object.

Answer: C

Explanation:
Option D provides the most maintainable and efficient approach. By defining validation rules and corresponding functions in a JavaScript object, you can easily add, modify, or remove validation rules without affecting the core logic of the UDF. This approach promotes code reusability and makes the UDF easier to understand and maintain. Options A leads to unmaintainable code. Options B can be better than A but is still less elegant than D. Option C, while potentially useful for certain tasks, adds unnecessary overhead. Option E is generally discouraged due to performance limitations and the complexity of embedding SQL within JavaScript.

NEW QUESTION # 337
You have configured a Kafka Connector to load JSON data into a Snowflake table named 'ORDERS. The JSON data contains nested structures. However, Snowflake is only receiving the top- level fields, and the nested fields are being ignored. Which configuration option within the Kafka Connector needs to be adjusted to correctly flatten and load the nested JSON data into Snowflake?

A. Enable the 'snowflake.ingest.stage' property and set it to a Snowflake internal stage.
B. Set the 'value.converter.schemas.enable' property to 'true'.
C. Configure the 'snowflake.data.field.name' property to specify the column in the Snowflake table where the entire JSON should be loaded as a VARIANT.
D. Apply the 'org.apache.kafka.connect.transforms.Flatten' transformation to the 'transforms' configuration.
E. Use the 'transforms' configuration with the 'org.apache.kafka.connect.transforms.ExtractField$Value' transformation to extract specific fields.

Answer: D

Explanation:
The correct answer is E. The 'org.apache.kafka.connect.transforms.Flatten' transformation is designed specifically for flattening nested JSON structures within Kafka Connect. By applying this transformation to the 'transforms' configuration of the Kafka Connector, you can instruct the connector to recursively flatten the nested JSON data before loading it into Snowflake, ensuring that all fields are accessible. Option A utilizes stages and is unrelated to nested structures. Option B enables schemas, which is useful for Avro, but does not inherently flatten nested structures. Option C allows for extraction but would require multiple transformations for each field and would be cumbersome. Option D will only load the full JSON but would not flatten it, failing the requirements.

NEW QUESTION # 338
You are responsible for monitoring the performance of a Snowflake data pipeline that loads data from S3 into a Snowflake table named 'SALES DATA. You notice that the COPY INTO command consistently takes longer than expected. You want to implement telemetry to proactively identify the root cause of the performance degradation. Which of the following methods, used together, provide the MOST comprehensive telemetry data for troubleshooting the COPY INTO performance?

A. Use Snowflake's partner connect integrations to monitor the virtual warehouse resource consumption and query the 'VALIDATE function to ensure data quality before loading.
B. Query the 'COPY HISTORY view in the 'INFORMATION SCHEMA' and enable Snowflake's query profiling for the COPY INTO statement.
C. Query the 'COPY_HISTORY view and the view in 'ACCOUNT_USAG Also, check the S3 bucket for throttling errors.
D. Query the 'COPY HISTORY view in the 'INFORMATION SCHEMA' and monitor CPU utilization of the virtual warehouse using the Snowflake web I-Jl.
E. Query the ' LOAD_HISTORY function and monitor the network latency between S3 and Snowflake using an external monitoring tool.

Answer: B,C

Explanation:
To comprehensively troubleshoot COPY INTO performance, you need data on the copy operation itself (COPY HISTORY), overall account and data validation. The COPY_HISTORY view provides details about each COPY INTO execution, including the file size, load time, and any errors encountered. Query profiling offers detailed insight into the internal operations of the COPY INTO command, revealing bottlenecks. Monitoring S3 for throttling ensures that the data source isn't limiting performance. Using helps correlate storage growth with load times. LOAD_HISTORY doesn't exist, 'VALIDATE function is for data validation not performance. While warehouse CPU utilization is useful, it doesn't provide the specific details needed to diagnose COPY INTO issues. External network monitoring is also less relevant than checking for S3 throttling and analyzing Snowflake's internal telemetry data.

NEW QUESTION # 339
You're building a data product on the Snowflake Marketplace that includes a view that aggregates data from a table containing Personally Identifiable Information (PII). You need to ensure that consumers of your data product CANNOT directly access the underlying PII data but can only see the aggregated results from the view. What is the MOST secure and recommended approach to achieve this?

A. Grant the 'SELECT privilege only on the to the share used for the Marketplace listing. Do not grant any privileges on the underlying PII table.
B. Create a stored procedure that returns the aggregated data, and grant EXECUTE privilege on the stored procedure to the share. The stored procedure SELECTs from the PII table.
C. Grant 'READ privilege on the internal stage containing the data files backing the PII table.
D. Grant the 'SELECT privilege directly on the underlying PII table to the share used for the Marketplace listing, along with the 'SELECT privilege on 'sensitive data view'.
E. Grant USAGE privilege on the database containing the PII table and to the share.

Answer: A

Explanation:
Granting only 'SELECT privilege on the (option B) ensures that consumers can only access the view and not the underlying PII data. Granting 'SELECT on the underlying table (option A) defeats the purpose of the view. Using a stored procedure (option C), while potentially masking the data access, is less performant and can still expose data if not carefully implemented. 'USAGE privilege (option D) only allows access to the database, not the data itself. 'READ' on the stage (option E) allows direct access to the raw data, which exposes the PII.

NEW QUESTION # 340
You're using Snowpark Python to transform data in a Snowflake table called 'employee_data' which includes columns , 'department, 'salary' , and 'performance_rating'. You need to identify the top 3 highest-paid employees within each department based on their salary, but only for departments where the average performance rating is above 4.0. Which of the following approaches using Snowpark efficiently combines window functions, filtering, and aggregations to achieve this?

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

Answer: C

Explanation:
Option E is the most efficient approach, as it calculates the average performance rating per department first and then joins the original DataFrame to only include departments with an average rating above 4.0 using 'left join'. The correct department and salary calculations, filter to only the top 3 with high performace review.

NEW QUESTION # 341
......

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