BLG -

Ian Snow Ian Snow

0 Course Enrolled • 0 Course Completed

Biography

2025 DEA-C02: SnowPro Advanced: Data Engineer (DEA-C02) Perfect Exam Cost

BTW, DOWNLOAD part of TopExamCollection DEA-C02 dumps from Cloud Storage: https://drive.google.com/open?id=18eBRe0cuxDyK29vSkywZJu48tiNc1UBC

We also offer up to 365 days free DEA-C02 exam dumps updates. These free updates will help you study as per the DEA-C02 latest examination content. Our valued customers can also download a free demo of our SnowPro Advanced: Data Engineer (DEA-C02) DEA-C02 Exam Dumps before purchasing. We guarantee 100% satisfaction for our DEA-C02 practice material users, thus our SnowPro Advanced: Data Engineer (DEA-C02) DEA-C02 study material saves your time and money.

For years our team has built a top-ranking brand with mighty and main which bears a high reputation both at home and abroad. The sales volume of the DEA-C02 study materials we sell has far exceeded the same industry and favorable rate about our products is approximate to 100%. Why the clients speak highly of our DEA-C02 Study Materials? Our dedicated service, high quality and passing rate and diversified functions contribute greatly to the high prestige of our products.

>> DEA-C02 Exam Cost <<

Reliable Snowflake DEA-C02 Test Syllabus | DEA-C02 Exam Reviews

Hence, if you want to sharpen your skills, and get the SnowPro Advanced: Data Engineer (DEA-C02) (DEA-C02) certification done within the target period, it is important to get the best SnowPro Advanced: Data Engineer (DEA-C02) (DEA-C02) exam questions. You must try TopExamCollection SnowPro Advanced: Data Engineer (DEA-C02) (DEA-C02) practice exam that will help you get the Snowflake DEA-C02 certification.

Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions (Q240-Q245):

NEW QUESTION # 240
You have implemented external tokenization for a sensitive data column in Snowflake using a UDF that calls an external API. After some time, you discover that the external tokenization service is experiencing intermittent outages, causing queries using the tokenized column to fail. What is the BEST approach to mitigate this issue and maintain data availability while minimizing the risk of exposing the raw data?

A. Implement a try-catch block within the UDF. In the catch block, return a pre-defined static token value (same value always) instead of attempting to call the external tokenization service. You can't return the raw value.
B. Implement a masking policy on the column that returns the raw data when the tokenization UDF is unavailable, detected by catching exceptions within the policy logic.
C. Implement a try-catch block within the UDF. In the catch block, return a pre-defined, non-sensitive default value instead of attempting to call the external tokenization service. You can't return the raw value.
D. Modify the tokenization UDF to cache tokenization mappings locally within the Snowflake environment. When the external service is unavailable, the UDF can use the cached values.
E. Replicate the tokenized table to another Snowflake region and switch to the replica during outages of the primary region. The tokenization service is guaranteed to be available in at least one region.

Answer: C

Explanation:
Returning the raw data (option A) defeats the purpose of tokenization. Caching tokenization mappings locally (option C) introduces security risks and potential data synchronization issues. Replicating the table (option B) doesn't solve the immediate problem of the tokenization service outage; it only addresses regional disaster recovery. Returning a default, non-sensitive value (option D) maintains data integrity and avoids exposing sensitive data during outages. Returning the same static token (Option E) for all values could cause data corruption.

NEW QUESTION # 241
You are tasked with creating a development environment from a production database in Snowflake. The production database is named 'PROD DB' and contains several schemas, including 'CUSTOMER DATA' and 'PRODUCT DATA'. You want to create a clone of the 'PROD DB' database named 'DEV DB', but you only need the 'CUSTOMER DATA' schema for development purposes and all the data should be masked with a custom UDF 'MASK EMAIL' for 'email' column in 'CUSTOMER' table. The 'email' column is VARCHAR. Which of the following sequences of SOL statements would achieve this in Snowflake? Note: UDF MASK EMAIL already exists in the account.

Answer: D

Explanation:
Option B is the most appropriate solution. It clones the entire production database, drops the unnecessary schema, then clone table from PROD and after cloning, it uses masking policy on email column on the cloned DEV environment. Option A is incorrect because you cannot use MASK EMAIL while createing the table. Option C requires to drop and add column again, option D, using a view will not permanently mask data at the storage level. And Option E updates the table after cloning, which consumes resources and isn't as elegant as using a masking policy.

NEW QUESTION # 242
You are tasked with building a data pipeline that incrementally loads data from an external cloud storage location (AWS S3) into a Snowflake table named 'SALES DATA'. You want to optimize the pipeline for cost and performance. Which combination of Snowflake features and configurations would be MOST efficient and cost-effective for this scenario, assuming the data volume is substantial and constantly growing?

A. Create an external stage pointing to the S3 bucket. Create a Snowpipe with auto-ingest enabled, using an AWS SNS topic and SQS queue for event notifications. Configure the pipe with an error notification integration to monitor ingestion failures.
B. Use a Snowflake Task scheduled every 5 minutes to execute a COPY INTO command from S3, with no file format specified, assuming the data is CSV and auto-detection will work.
C. Employ a third-party ETL tool to extract data from S3, transform it, and load it into Snowflake using JDBC. Schedule the ETL process using the tool's built-in scheduler.
D. Use a Snowflake Task to regularly truncate and reload 'SALES DATA" from S3 using COPY INTO. This ensures data consistency.
E. Develop a custom Python script that uses the Snowflake Connector for Python to connect to Snowflake and execute a COPY INTO command. Schedule the script to run on an EC2 instance using cron.

Answer: A

Explanation:
Snowpipe with auto-ingest is the most efficient and cost-effective solution for continuously loading data into Snowflake from cloud storage. It leverages event notifications to trigger data loading as soon as new files are available, minimizing latency and compute costs. Option A lacks error handling and proper file format specification. Option C involves custom coding and infrastructure management. Option D introduces overhead and costs associated with a third-party ETL tool. Option E is inefficient as it truncates and reloads the entire table, losing any incremental loading benefits.

NEW QUESTION # 243
You have a Snowflake Task that is designed to transform and load data into a target table. The task relies on a Stream to detect changes in a source table. However, you notice that the task is intermittently failing with a 'Stream STALE' error, even though the data in the source table is continuously updated. What are the most likely root causes and the best combination of solutions to prevent this issue? (Select TWO)

A. The source table is being modified with DDL operations (e.g., ALTER TABLE ADD COLUMN), which are not supported by Streams. Use Table History to track schema changes and manually adjust the Stream's query if needed. Use 'COPY GRANTS' during the DDL.
B. The Stream has reached its maximum age (default 14 days) and expired. There is no way to recover data from an expired Stream. You need to recreate the Stream and reload the source table.
C. DML operations (e.g., UPDATE, DELETE) being performed on the source table are affecting rows older than the Stream's retention period. Reduce the stream's 'DATA RETENTION TIME IN DAYS' to match the oldest DML operation on the source table.
D. The Stream is not configured with 'SHOW INITIAL ROWS = TRUE, causing initial changes to be missed and eventually leading to staleness. Recreate the stream with this parameter set to TRUE.
E. The Task is not running frequently enough, causing the Stream to accumulate too many changes before being consumed, exceeding its retention period. Increase the task's execution frequency or increase the stream's 'DATA RETENTION TIME IN DAYS

Answer: A,E

Explanation:
A Stream becomes stale if its offset is beyond the retention period. If the task isn't running often enough (B), the Stream can exceed the retention period before being consumed. DDL operations (C) invalidate Streams. Option A is incorrect because SHOW INITIAL ROWS only impacts the first read, not staleness. D is partially incorrect. While Streams do have a maximum age, increasing the retention period or running the task more frequently is preferred. E is wrong because decreasing retention won't help prevent the error and only lead to data losses.

NEW QUESTION # 244
You are using Snowpipe with an external function to transform data as it is loaded into Snowflake. The Snowpipe is configured to load data from AWS SQS and S3. You observe that some messages are not being processed by the external function, and the data is not appearing in the target table. You have verified that the Snowpipe is enabled and the SQS queue is receiving notifications. Analyze the following potential causes and select all that apply:

A. The IAM role associated with the Snowflake stage does not have permission to invoke the external function. Verify that the role has the necessary permissions in AWS IAM.
B. The external function is experiencing timeouts or errors, causing it to reject some records. Review the external function logs and increase the timeout settings if necessary.
C. The data being loaded into Snowflake does not conform to the expected format for the external function. Validate the structure and content of the data before loading it into Snowflake.
D. The AWS Lambda function (or other external function) does not have sufficient memory or resources to process the incoming data volume, leading to function invocations being throttled and messages remaining unprocessed.
E. The Snowpipe configuration is missing a setting that allows the external function to access the data files in S3. Ensure that the storage integration is configured to allow access to the S3 location.

Answer: A,B,C,D

Explanation:
When using Snowpipe with external functions, several factors can cause messages to be dropped or unprocessed. The most common include external function errors or timeouts (A), permission issues between Snowflake and the external function (B), data format mismatches (C), and the external function lacking resources (E) leading to throttling. Option D is less likely, as the storage integration is primarily for COPY INTO and not direct Lambda function calls, assuming the Lambda function retrieves the data directly from S3 using the event data provided by SQS. The permissions issue B is still relevant as the lambda function will need access to the files in S3.

NEW QUESTION # 245
......

Customizable Snowflake DEA-C02 practice exams (desktop and web-based) of TopExamCollection are designed to give you the best learning experience. You can attempt these DEA-C02 practice tests multiple times till the best preparation for the SnowPro Advanced: Data Engineer (DEA-C02) (DEA-C02) test. On every take, our Snowflake DEA-C02 practice tests save your progress so you can view it to see and strengthen your weak concepts easily.

Reliable DEA-C02 Test Syllabus: https://www.topexamcollection.com/DEA-C02-vce-collection.html

DEA-C02 latest download demo is accessible for try before you purchase, If you choose our DEA-C02 test questions as your study tool, you will be glad to study for your exam and develop self-discipline, our DEA-C02 latest question adopt diversified teaching methods, and we can sure that you will have passion to learn by our products, Real Snowflake MCSA: SnowPro Advanced DEA-C02 Exam Questions with Experts Reviews.

With great outcomes of the passing rate upon to 98-100 percent, our Snowflake DEA-C02 test braindumps are totally the perfect one, For example, every company has an accounting system.

DEA-C02 latest download demo is accessible for try before you purchase, If you choose our DEA-C02 Test Questions as your study tool, you will be glad to study for your exam and develop self-discipline, our DEA-C02 latest question adopt diversified teaching methods, and we can sure that you will have passion to learn by our products.

100% Pass DEA-C02 - SnowPro Advanced: Data Engineer (DEA-C02) –High-quality Exam Cost

Real Snowflake MCSA: SnowPro Advanced DEA-C02 Exam Questions with Experts Reviews, Our DEA-C02 training materials are famous for high-quality, and we have a professional team to collect the first hand information for the exam.

And not only the content of the demos is the same with the three versions, but also the displays are the same with the according version of our DEA-C02 learning guide.

DOWNLOAD the newest TopExamCollection DEA-C02 PDF dumps from Cloud Storage for free: https://drive.google.com/open?id=18eBRe0cuxDyK29vSkywZJu48tiNc1UBC

Ian Snow Ian Snow

Biography

Links

Contact

Follow us on