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Snowflake DEA-C02 Valid Exam Pattern - DEA-C02 Certification Dump

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Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions (Q267-Q272):

NEW QUESTION # 267
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. Automatic schema evolution is not directly supported by the Kafka Connector for Snowflake. You must manually manage schema changes in Snowflake.
B. Set the property to 'true' and the 'snowflake.ingest.stage' to an existing stage.
C. 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.
D. 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'.
E. Set 'snowflake.ingest.file.name' to an existing file in a stage.

Answer: A

Explanation:
The correct answer is E. Currently, the Snowflake Kafka connector does not directly support automatic schema evolution. You cannot configure the connector to automatically alter the Snowflake table schema based on changes in the Kafka topic's data structure. You must manually manage schema changes in the Snowflake table to align with the structure of the data being ingested from Kafka. Option D will simply throw errors as the configuration needed is not fully complete with data types. The connector does rely heavily on the VARIANT column and would not be able to evolve properly, and so, that function is not directly available.

NEW QUESTION # 268
You are ingesting data from an external stage (AWS S3) into a Snowflake table using Snowpipe. Data files are continuously being uploaded to the stage. After several hours, you notice that some data files are not being loaded. You check the Snowpipe error notifications and see 'net.snowflake.ingest.errors.FileSizeLimitExceededError'. You have already verified that the Snowpipe is correctly configured and the user has the necessary permissions. What are the MOST LIKELY reasons for this error and how can you resolve them?

A. The Snowpipe configuration is incorrect; specifically, the 'FILE FORMAT parameter is not correctly specified to handle the file type. Reconfigure the Snowpipe with the correct 'FILE FORMAT.
B. The data files are being uploaded to the stage faster than Snowpipe can process them. Increase the value of the 'MAX CONCURRENCY parameter in the Snowpipe definition.
C. The Snowpipe is encountering a transient network error. Reset the pipe using ALTER PIPE REFRESH;
D. The size of the data files in the stage exceeds the maximum allowed size for Snowpipe. Split the large files into smaller files before uploading to the stage.
E. Snowflake has reached its maximum allowable data storage capacity. Increase your Snowflake storage capacity to resolve this issue.

Answer: D

Explanation:
The 'net.snowflake.ingest.errors.FileSizeLimitExceededError' clearly indicates that the size of the data files being ingested is exceeding the maximum allowed size for Snowpipe. While Snowflake does have storage capacity limits, that is not the root cause of this specific error. Splitting the files into smaller sizes prior to uploading will allow Snowpipe to process the data without exceeding the size limit. While network errors can occur, the error message is specific to file size. Snowpipe does not use 'MAX CONCURRENCY parameter. It automatically adjusts the concurrency The file format issues will create different error.

NEW QUESTION # 269
You're designing a Snowpark Scala stored procedure that must execute a series of complex data quality checks on a Snowflake table.
These checks involve multiple steps, including validating data types, checking for null values, and verifying data consistency against external reference data'. You want to ensure that the stored procedure is resilient to errors, provides detailed logging, and can be easily monitored. Which of the following approaches would be the MOST robust and scalable for handling errors and logging within this Snowpark Scala stored procedure?

A. Wrap each data quality check in a try-catch block and use 'println' statements to log error messages to the Snowflake console.
B. Implement a custom logging framework within the Scala stored procedure that writes detailed logs to a dedicated Snowflake table. Use try-catch blocks to handle exceptions and log error details, including timestamps, error codes, and relevant data values. Use Snowflake's 'SYSTEM$LAST QUERY ID()' function to track query lineage.
C. Use Scala's 'Try' monad to handle exceptions, mapping successes to informational messages and failures to error messages. Log these messages using Snowflake's event tables.
D. Use Scala's 'Option' type to handle potential null values and exceptions. Return a string message indicating success or failure for each check. Log these messages using 'System.out.println'.
E. Rely on Snowflake's built-in error handling and logging mechanisms. If an error occurs, the stored procedure will automatically fail, and the error details can be retrieved from Snowflake's query history.

Answer: B

Explanation:
Option C provides the most robust and scalable solution. Writing logs to a dedicated Snowflake table allows for detailed analysis and monitoring of data quality checks. Using try-catch blocks provides granular error handling, and including timestamps and error codes in the logs facilitates troubleshooting. provides query lineage. Options A and B are insufficient because 'println' and 'System.out.println' log to the session's output but lack persistence and are difficult to analyze at scale. Option D relies on Snowflake's general error handling but lacks detailed custom logging. Option E relies on internal event tables which the developer does not have control or access to.

NEW QUESTION # 270
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. 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.
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. 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.
D. 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.
E. Leverage Snowflake's differential privacy features to add noise to the patient ID data, share the modified dataset and perform a JOIN.

Answer: B,C

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 # 271
You are designing a data sharing solution for a multi-tenant application where each tenant's data must be isolated. You have a 'sales' table with a 'tenant_id' column. You need to implement row-level security to ensure that each tenant can only access their own data when querying the shared table. Which of the following approaches, considering performance and security, is the MOST suitable for implementing this row-level filtering in Snowflake?

A. Use Snowflake's data masking policies to mask all data for tenants other than the one currently querying the table.
B. Implement a row access policy on the 'sales' table that filters data based on the 'tenant_id' column and the current role or user context.
C. Implement a user-defined function (UDF) that checks the current user's tenant ID and returns a boolean value indicating whether the row should be visible. Use this UDF in a WHERE clause in every query.
D. Create a separate VIEW for each tenant, filtering by 'tenant_id'. Grant each tenant access only to their respective view.
E. Create a scheduled task that duplicates the sales table into a new table for each tenant, filtering by the tenant_id.

Answer: B

Explanation:
Row access policies are the most efficient and scalable solution for row-level security in Snowflake. They are applied at the table level and automatically enforced for all queries, ensuring consistent data isolation. Creating separate views for each tenant is administratively burdensome and doesn't scale well. UDFs can impact performance. Data masking policies do not filter rows, they only redact data. Creating scheduled task create data duplication, its not ideal for data movements.

NEW QUESTION # 272
......

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