Reliable DEA-C02 Dumps Files - DEA-C02 Exam Cram Review

The DEA-C02 practice test of Easy4Engine is created and updated after feedback from thousands of professionals. Additionally, we also offer up to free DEA-C02 exam dumps updates. These free updates will help you study as per the Snowflake DEA-C02 latest examination content. Our valued customers can also download a free demo of our Snowflake DEA-C02 exam dumps before purchasing.

Now it is a society of abundant capable people, and there are still a lot of industry is lack of talent, such as the IT industry is quite lack of technical talents. Snowflake certification DEA-C02 exam is one of testing IT technology certification exams. Easy4Engine is a website which provide you a training about Snowflake Certification DEA-C02 Exam related technical knowledge.

>> Reliable DEA-C02 Dumps Files <<

Snowflake DEA-C02 Exam Cram Review & Passing DEA-C02 Score

The DEA-C02 certificate enjoys a high reputation among the labor market circle and is widely recognized as the proof of excellent talents and if you are one of them and you want to pass the test smoothly you can choose our DEA-C02 practice questions. Our DEA-C02 Study Materials concentrate the essence of exam materials and seize the focus information to let the learners master the key points. You will pass the exam for sure if you choose our DEA-C02 exam braindumps.

Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions (Q287-Q292):

NEW QUESTION # 287
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 '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.
B. Set 'snowflake.ingest.file.name' to an existing file in a stage.
C. Automatic schema evolution is not directly supported by the Kafka Connector for Snowflake. You must manually manage schema changes in Snowflake.
D. Set the property to 'true' and the 'snowflake.ingest.stage' to an existing stage.
E. 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'.

Answer: C

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 # 288
You are tasked with building an ETL pipeline that ingests JSON logs from an external system via the Snowflake REST API. The external system authenticates using OAuth 2.0 client credentials flow. The logs are voluminous, and you want to optimize for cost and performance. Which of the following approaches are MOST suitable for securely and efficiently ingesting the data?

A. Implement a custom API gateway using a serverless function (e.g., AWS Lambda, Azure Function) to handle authentication and batch the JSON logs before sending them to the Snowflake REST API. Write the API output to a Snowflake stage, then use COPY INTO to load into a final table.
B. Use Snowflake's Snowpipe with REST API by configuring the external system to directly push the logs to an external stage and configure Snowpipe to automatically ingest it.
C. Configure the ETL tool to write directly to Snowflake tables using JDBC/ODBC connection strings. Avoid the REST API due to its complexity.
D. Create a Snowflake external function that handles the API call and OAuth authentication. Use a stream on the external stage pointing to the external system's storage to trigger data loading into the final table.
E. Use the Snowflake REST API directly from your ETL tool, handling OAuth token management in the ETL tool. Load data into a staging table, then use COPY INTO with a transformation to the final table.

Answer: A,E

Explanation:
Options A and C are the most suitable. Option A involves direct integration and option C introduces batching and serverless function to improve performance and manage authentication. Option B is incorrect because external functions cannot directly trigger data loading based on external stage events. Option D bypasses the REST API requirement and does not address authentication. Option E avoids the REST API entirely, which is against the problem requirement.

NEW QUESTION # 289
You are designing a data sharing solution in Snowflake where a provider account shares a view with a consumer account. The view is based on a table that undergoes frequent DML operations (inserts, updates, deletes). The consumer account needs to see a consistent snapshot of the data, even during these DML operations. Which of the following strategies, or combination of strategies, would be MOST effective in ensuring data consistency from the consumer's perspective, and what considerations should be made?

A. A and B
B. Creating a stream on the base table in the provider account and building a view on top of the stream. This way, changes are only reflected when the stream is consumed, allowing for batch processing and controlled updates in the consumer account.
C. Creating a materialized view in the provider account and sharing that materialized view. This adds compute costs to the provider but ensures a consistent snapshot for the consumer account. The materialized view needs to be refreshed periodically, based on the rate of DML changes.
D. Using Snowflake's Time Travel feature by querying the view with a specific 'AT' or 'BEFORE' clause in the consumer account. The provider account needs to inform the consumer account of a specific timestamp that guarantees consistency, adding administrative overhead.
E. Creating a standard view in the provider account and relying on Snowflake's inherent transactional consistency. The consumer account will always see a consistent snapshot of the data as it existed at the beginning of their query execution. No additional configurations are necessary.

Answer: E

Explanation:
Snowflake's architecture inherently provides transactional consistency. When the consumer account queries the shared view, they will see a consistent snapshot of the data as it existed at the beginning of their query execution. No additional mechanisms like Time Travel (A) or materialized views (B) are strictly necessary to ensure consistency. While streams (D) can be useful for change data capture, they don't directly guarantee consistency for a standard view shared with a consumer. Time travel in this case would also require significant coordination overhead.

NEW QUESTION # 290
You are implementing a data pipeline in Snowpark that reads data from an external stage (e.g., AWS S3) and performs complex transformations, including joins with large Snowflake tables. You notice that the pipeline's performance is significantly slower than expected, despite having sufficient warehouse resources. Which of the following actions would MOST likely improve the performance of the Snowpark data pipeline?

A. Optimize the SQL joins within the Snowpark DataFrame operations by using broadcast joins when appropriate and ensuring correct join key data types.
B. Ensure that the external stage is properly configured with appropriate data formats (e.g., Parquet) and partitioning schemes that align with the join keys.
C. Persist the DataFrame representing the data from the external stage using 'df.cache()' before performing the joins.
D. Reduce the number of partitions in the DataFrame representing the data from the external stage using 'df.repartition(l )'.
E. Increase the warehouse size to the largest available option (e.g., X-Large or larger).

Answer: A,B,C

Explanation:
Options B, C, and E address key aspects of performance optimization: B: Optimizing joins is crucial for large datasets. Using broadcast joins where applicable (smaller table fits in memory) and ensuring compatible data types between join keys can significantly reduce data shuffling and improve join performance. C: Caching (persisting) the DataFrame from the external stage using avoids re-reading the data from S3 for each operation, especially if the data is accessed multiple times (e.g., in multiple joins). E: The configuration of the external stage is critical. Using columnar formats like Parquet enables efficient data scanning and filtering. Partitioning the data in S3 based on the join keys allows Snowflake to prune unnecessary data during the read, reducing the amount of data processed. Increasing the warehouse size (Option A) might help, but it's often more cost-effective to optimize the data pipeline first. Reducing the number of partitions to 1 (Option D) would likely hurt performance, as it eliminates parallelism.

NEW QUESTION # 291
A data engineer is tasked with creating a Listing to share a large dataset stored in Snowflake. The dataset contains sensitive Personally Identifiable Information (PII) that must be masked for certain consumer roles. The data engineer wants to use Snowflake's dynamic data masking policies within the Listing to achieve this. Which of the following approaches is the MOST secure and maintainable way to implement this requirement, assuming that the consumer roles are pre-defined and known?

A. Apply dynamic data masking policies directly to the base tables containing the PII and share these tables in the Listing. Policies should use the function to determine when to mask the data.
B. Create a view that applies conditional masking using 'CASE' statements based on the function and share the view in the Listing.
C. Create multiple versions of the shared tables, each with different masking applied. The data engineer must manually manage which version each consumer can access.
D. Implement an external function that masks the data based on the consumer's role and share this function in the Listing. Use this external function in a view shared through the listing.

Answer: A

Explanation:
Applying dynamic data masking policies directly to the base tables is the MOST secure and maintainable approach. This ensures that the masking is applied consistently across all access methods. Using the function within the policy allows for dynamic masking based on the consumer's role. Views can provide masking but doesn't give the same protection as the base tables can be accessed directly. Data masking policies are applied transparently and require less maintenance than views with complex CASE statements or UDFs. Also, the approach using Snowflake Secret Manager adds unnecessary complexity.

NEW QUESTION # 292
......

The price for DEA-C02 exam materials is reasonable, and no matter you are a student at school or an employee in the company, you can afford it. Besides, DEA-C02 exam materials are compiled by skilled professionals, and they are familiar with the exam center, therefore the quality can be guaranteed. DEA-C02 study guide offer you free demo to have a try before buying, so that you can have a better understanding of what you are going to buy. Free update for one year is also available, and in this way, you can get the latest information for the exam during your preparation. The update version for DEA-C02 Exam Dumps will be sent to your email address automatically.

DEA-C02 Exam Cram Review: https://www.easy4engine.com/DEA-C02-test-engine.html

The DEA-C02 exam prepare of our website is completed by experts who has a good understanding of real exams and have many years of experience writing DEA-C02 study materials, Snowflake Reliable DEA-C02 Dumps Files Such an easy and innovative study plan is amazingly beneficial for an ultimately brilliant success in exam, We have online and offline chat service stuff, and if you have any questions for DEA-C02 exam materials, you can consult us.

The Machinima app is a tool that you can use to improve your gaming Reliable DEA-C02 Dumps Files skills, To execute on a Continuous Design and Delivery methodology, you need to put some technology enablers in place.

The DEA-C02 Exam prepare of our website is completed by experts who has a good understanding of real exams and have many years of experience writing DEA-C02 study materials.

100% Pass Quiz 2025 Snowflake DEA-C02: SnowPro Advanced: Data Engineer (DEA-C02) Pass-Sure Reliable Dumps Files

Such an easy and innovative study plan is amazingly beneficial for an ultimately brilliant success in exam, We have online and offline chat service stuff, and if you have any questions for DEA-C02 exam materials, you can consult us.

You can get full refund or change other exam training DEA-C02 material if you want, Maybe you are the first time to buy our SnowPro Advanced: Data Engineer (DEA-C02) pdf vce dumps.