John King John King's Profile Page

John King John King

0 Course Enrolled • 0 Course Completed

Biography

Latest Released Snowflake DEA-C02 Reliable Test Review: SnowPro Advanced: Data Engineer (DEA-C02)

As you know, many exam and tests depend on the skills as well as knowledge, our DEA-C02 practice materials are perfectly and exclusively devised for the exam and can satisfy your demands both. There are free demos for your reference with brief catalogue and outlines in them. Free demos are understandable materials as well as the newest information for your practice. Under coordinated synergy of all staff, our DEA-C02 practice materials achieved a higher level of perfection by keeping close attention with the trend of dynamic market.

With the rapid development of the world economy and frequent contacts between different countries, looking for a good job has become more and more difficult for all the people. So it is very necessary for you to get the DEA-C02 certification, in order to look for a good job, you have to increase your competitive advantage in the labor market and make yourself distinguished from other job-seekers. And our DEA-C02 Exam Questions are specially desiged for you as we can help you pass the DEA-C02 exam successfully with the least time and effort. Just come and buy our DEA-C02 practice guide!

>> DEA-C02 Reliable Test Review <<

Snowflake DEA-C02 Exam Dumps.zip | DEA-C02 Study Group

TestInsides is a website provide you with the best and valid DEA-C02 exam questions that elaborately compiled and highly efficiently, studying with our DEA-C02 study guide will cost you less time and energy, because we shouldn't waste our money on some unless things. The passing rate and the hit rate of our DEA-C02 Training Material are also very high, there are thousands of candidates choose to trust our website and they have passed the DEA-C02 exam. We provide with candidate so many guarantees that they can purchase our DEA-C02 study materials no worries.

Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions (Q324-Q329):

NEW QUESTION # 324
You have a Snowflake Stream named 'ORDERS STREAM' on an 'ORDERS' table, which is used to incrementally load data into a historical orders table named 'HISTORICAL ORDERS'. The data pipeline involves a series of tasks: 1) Consume changes from the 'ORDERS STREAM', 2) Apply transformations and data quality checks, and 3) Merge the changes into 'HISTORICAL ORDERS' using a MERGE statement. After a recent data load, you notice that the 'HISTORICAL ORDERS' table contains duplicate records for certain 'ORDER values. The MERGE statement uses 'ORDER ID' as the matching key. You have confirmed that the transformation logic is correct and idempotent. Examine the MERGE statement below. What could be causing the duplicates, given the context of Streams and incremental loading?

A. The 'ORDERS STREAM' is retaining historical data beyond the data retention period, causing older records to be re-processed.
B. The MERGE statement is not correctly handling updates and deletes from the stream. The 'WHEN NOT MATCHED' and 'WHEN MATCHED' clauses are not mutually exclusive, leading to potential insertions of duplicate rows.
C. The stream is not configured to capture DELETE operations from the ORDERS table, causing records that should have been removed in HISTORICAL ORDERS to remain.
D. Multiple tasks are concurrently consuming from the same 'ORDERS STREAM' without proper coordination, causing records to be processed multiple times.
E. The stream's or 'BEFORE clause is being used incorrectly, potentially rewinding the stream to an earlier point in time.

Answer: D

Explanation:
The most likely cause of duplicate records, given the correct transformation logic and idempotent behavior, is D (Concurrent consumption from the stream) . If multiple tasks or processes are consuming from the same stream without proper coordination , they can both read the same changes and apply them to the 'HISTORICAL ORDERS' table. Option A (Retention Period) would cause data loss , not duplication, because older changes are simply lost. Option B is unlikely if you're not explicitly using or 'BEFORE' and even if you were, a simple stream consumption would advance the offset. If that clause was incorrectly configured, it would still not cause duplicates. Option C is possible but less likely if the transformation logic has been verified. If so many records are malformed that MERGE is broken, more evidence than duplication would be expected. Duplication alone with idempotent transformation suggests consumption error. Option E is a factor that could lead to 'HISTORICAL ORDERS' diverging from 'ORDERS but not a cause duplication.

NEW QUESTION # 325
Which of the following statements are accurate regarding the differences between SQL UDFs and Java UDFs in Snowflake? (Select two)

A. Java UDFs are deprecated and should not be used; instead, SQL UDFs are recommended for all scenarios.
B. SQL UDFs can only be used for simple transformations and cannot execute external calls, while Java UDFs can perform complex logic and interact with external services via libraries.
C. Java UDFs always execute faster than SQL UDFs due to JVM optimizations.
D. SQL UDFs are defined using SQL code within Snowflake, whereas Java UDFs require uploading a JAR file containing the compiled Java code.
E. SQL UDFs and Java UDFs are interchangeable, and there is no performance difference between them.

Answer: B,D

Explanation:
SQL UDFs are suitable for simpler transformations within Snowflake and cannot make external calls. They are defined directly using SQL code. Java UDFs, on the other hand, offer more flexibility by allowing complex logic implementation, interaction with external services/libraries via JAR files, and custom code. Java UDFs are generally perform better when complex transformations are needed, where SQL UDFs can become cumbersome. Performance depends on the workload. Option B is wrong becuase SQL UDFs are more performant for simpler tasks. Option D is wrong becuase its highly dependant on workload, where options E is wrong as Java UDFs are very useful and not deprecated.

NEW QUESTION # 326
You are tasked with building a data pipeline to process image metadata stored in JSON format from a series of URLs. The JSON structure contains fields such as 'image_url', 'resolution', 'camera_model', and 'location' (latitude and longitude). Your goal is to create a Snowflake table that stores this metadata along with a thumbnail of each image. Given the constraints that you want to avoid downloading and storing the images directly in Snowflake, and that Snowflake's native functions for image processing are limited, which of the following approaches would be most efficient and scalable?

A. Store just the 'image_url' in snowflake. Develop a separate application using any programming language to pre generate the thumbnails and host those at publicly accessible URLs. Within Snowflake, create a view to generate the links for image and thumbnail using 'CONCAT.
B. Create a Snowflake stored procedure that iterates through each URL, downloads the JSON metadata using 'SYSTEM$URL_GET, extracts the image URL from the metadata, downloads the image using 'SYSTEM$URL_GET , generates a thumbnail using SQL scalar functions, and stores the metadata and thumbnail in a Snowflake table.
C. Create a Snowflake view that selects from a table containing the metadata URLs, using 'SYSTEM$URL GET to fetch the metadata. For each image URL found in the metadata, use a JavaScript UDF to generate a thumbnail. Embed the thumbnail into a VARCHAR column as a Base64 encoded string.
D. Create a Python-based external function that fetches the JSON metadata and image from their respective URLs. The external function uses libraries like PIL (Pillow) to generate a thumbnail of the image and returns the metadata along with the thumbnail's Base64 encoded string within a JSON object.
E. Create a Snowflake external table that points to an external stage which holds the JSON metadata files. Develop a spark process to fetch image URL, create thumbnails and store as base64 encoded strings in an external stage, create a view using the external table and generated thumbnails data

Answer: A,D

Explanation:
Option C is the most appropriate solution. By using an external function with Python and libraries like PIL, you can efficiently handle image processing tasks that are difficult or impossible to perform natively within Snowflake. The external function encapsulates the image processing logic, keeping the Snowflake SQL code cleaner. Option E is also a valid solution as it leverages external processing. Option A is not performant as it tries to download image in snowflake which is not the best way to process image. Option B is not recommended because using JavaScript UDFs for binary data (images) can be inefficient. External Tables as described in Option D require pre-processing of data and storage to an external stage. Option D doesn't use the 'SYSTEM$URL GET' function that this question is trying to assess.

NEW QUESTION # 327
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. 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.
B. 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.
C. 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.
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: C

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 # 328
You are designing a data pipeline in Snowflake that involves several tasks chained together. One of the tasks, 'task B' , depends on the successful completion of 'task A'. 'task_B' occasionally fails due to transient network issues. To ensure the pipeline's robustness, you need to implement a retry mechanism for 'task_B' without using external orchestration tools. What is the MOST efficient way to achieve this using native Snowflake features, while also limiting the number of retries to prevent infinite loops and excessive resource consumption? Assume the task definition for 'task_B' is as follows:

A. Leverage Snowflake's event tables like QUERY_HISTORY and TASK _ HISTORY in the ACCOUNT_USAGE schema joined with custom metadata tags to correlate specific transformation steps to execution times and resource usage. Also set up alerting based on defined performance thresholds.
B. Embed the retry logic directly within the stored procedure called by 'task_B'. The stored procedure should catch exceptions related to network issues, introduce a delay using 'SYSTEM$WAIT , and retry the main logic. Implement a loop with a maximum retry count.
C. Modify the task definition of 'task_B' to include a SQL statement that checks for the success of 'task_R in the TASK_HISTORY view before executing the main logic. If 'task_A' failed, use ' SYSTEM$WAIT to introduce a delay and then retry the main logic. Implement a counter to limit the number of retries.
D. Create a separate task, 'task_C', that is scheduled to run immediately after 'task will check the status of 'task_BS in the TASK HISTORY view. If 'task_B' failed, 'task_c' will re-enable 'task_B' and suspend itself. Use the parameter on 'task_B' to limit the number of retries.
E. Utilize Snowflake's external functions to call a retry service implemented in a cloud function (e.g., AWS Lambda or Azure Function). The external function will handle the retry logic and update the task status in Snowflake.

Answer: B

Explanation:
Option C is the most efficient and self-contained approach using native Snowflake features. Embedding the retry logic within the stored procedure called by 'task_ff allows for fine-grained control over the retry process, exception handling, and delay implementation. The retry count limit prevents infinite loops. Option A, while technically feasible, involves querying the TASK HISTORY view, which can be less efficient. Option B requires creating and managing an additional task. Option D introduces external dependencies, making the solution more complex. Option E does not address the retry mechanism.

NEW QUESTION # 329
......

These SnowPro Advanced: Data Engineer (DEA-C02) (DEA-C02) practice test questions are customizable and give real SnowPro Advanced: Data Engineer (DEA-C02) (DEA-C02) exam experience. Windows computers support desktop software. The web-based DEA-C02 Practice Exam is supported by all browsers and operating systems.

DEA-C02 Exam Dumps.zip: https://www.testinsides.top/DEA-C02-dumps-review.html

Actually, we should deal with the reviews of DEA-C02 exam dumps rationally, Most candidates can pass exams with our DEA-C02 actual test dumps, If you are interested in TestInsides's training program about Snowflake certification DEA-C02 exam, you can first on TestInsides to free download part of the exercises and answers about Snowflake certification DEA-C02 exam as a free try, Come on,and use DEA-C02 practice torrent,you can pass your Snowflake DEA-C02 actual test at first attempt.

Structured positions such as calendar spreads, ratios, vertical spreads, DEA-C02 and the like, are difficult to trade because stocks frequently cross several strike prices in a single month—sometimes in both directions.

DEA-C02 Exam Reliable Test Review- First-grade DEA-C02 Exam Dumps.zip Pass Success

By Andrew Couch, Actually, we should deal with the reviews of DEA-C02 Exam Dumps rationally, Most candidates can pass exams with our DEA-C02 actual test dumps.

If you are interested in TestInsides's training program about Snowflake certification DEA-C02 exam, you can first on TestInsides to free download part of the exercises and answers about Snowflake certification DEA-C02 exam as a free try.

Come on,and use DEA-C02 practice torrent,you can pass your Snowflake DEA-C02 actual test at first attempt, At TestInsides, get latest DEA-C02 exam dumps with 100% passing assurance.

John King John King

Biography

Ubax Academy

Support

QUick links