Because of the demand for people with the qualified skills about Snowflake SnowPro Advanced: Data Engineer (DEA-C02) certification and the relatively small supply, SnowPro Advanced: Data Engineer (DEA-C02) exam certification becomes the highest-paying certification on the list this year. While, it is a tough certification for passing, so most of IT candidates feel headache and do not know how to do with preparation. In fact, most people are ordinary person and hard workers. The only way for getting more fortune and living a better life is to work hard and grasp every chance as far as possible. Gaining the DEA-C02 SnowPro Advanced: Data Engineer (DEA-C02) exam certification may be one of their drams, which may make a big difference on their life. As a responsible IT exam provider, our SnowPro Advanced: Data Engineer (DEA-C02) exam prep training will solve your problem and bring you illumination.

Bearable cost

We have to admit that the SnowPro Advanced: Data Engineer (DEA-C02) exam certification is difficult to get, while the exam fees is very expensive. So, some people want to prepare the test just by their own study and with the help of some free resource. They do not want to spend more money on any extra study material. But the exam time is coming, you may not prepare well. Here, I think it is a good choice to pass the exam at the first time with help of the SnowPro Advanced: Data Engineer (DEA-C02) actual questions & answer rather than to take the test twice and spend more money, because the money spent on the SnowPro Advanced: Data Engineer (DEA-C02) exam dumps must be less than the actual exam fees. Besides, we have the money back guarantee that you will get the full refund if you fail the exam. Actually, you have no risk and no loss. Actually, the price of our Snowflake SnowPro Advanced: Data Engineer (DEA-C02) exam study guide is very reasonable and affordable which you can bear. In addition, we provide one year free update for you after payment. You don't spend extra money for the latest version. What a good thing.

At last, I want to say that our SnowPro Advanced SnowPro Advanced: Data Engineer (DEA-C02) actual test is the best choice for your 100% success.

Snowflake DEA-C02 braindumps Instant Download: Our system will send you the DEA-C02 braindumps file you purchase in mailbox in a minute after payment. (If not received within 12 hours, please contact us. Note: don't forget to check your spam.)

Customizable experience from SnowPro Advanced: Data Engineer (DEA-C02) test engine

Most IT candidates prefer to choose SnowPro Advanced: Data Engineer (DEA-C02) test engine rather than the pdf format dumps. After all, the pdf dumps have some limits for the people who want to study with high efficiency. DEA-C02 SnowPro Advanced: Data Engineer (DEA-C02) test engine is an exam test simulator with customizable criteria. The questions are occurred randomly which can test your strain capacity. Besides, score comparison and improvement check is available by SnowPro Advanced: Data Engineer (DEA-C02) test engine, that is to say, you will get score and after each test, then you can do the next study plan according to your weakness and strengths. Moreover, the SnowPro Advanced: Data Engineer (DEA-C02) test engine is very intelligent, allowing you to set the probability of occurrence of the wrong questions. Thus, you can do repetition training for the questions which is easy to be made mistakes. While the interface of the test can be set by yourself, so you can change it as you like, thus your test looks like no longer dull but interesting. In addition, the SnowPro Advanced SnowPro Advanced: Data Engineer (DEA-C02) test engine can be installed at every electronic device without any installation limit. You can install it on your phone, doing the simulate test during your spare time, such as on the subway, waiting for the bus, etc. Finally, I want to declare the safety of the SnowPro Advanced: Data Engineer (DEA-C02) test engine. SnowPro Advanced: Data Engineer (DEA-C02) test engine is tested and verified malware-free software, which you can rely on to download and installation.

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

1. A large e-commerce company stores clickstream data in an AWS S3 bucket. The data is partitioned by date and consists of Parquet files. They need to analyze this data in Snowflake without physically moving it into Snowflake's internal storage. However, the data frequently changes, and they need to ensure queries reflect the latest updates to the files without significant latency. Which of the following approaches would be MOST suitable, considering cost, performance, and data freshness?

A) Create an external table using a Snowflake-managed catalog. Configure a Snowpipe to automatically refresh the metadata as new files are added to the S3 bucket.
B) Create a standard external table directly on the S3 bucket. Refresh the external table metadata using SALTER EXTERNAL TABLE ... REFRESH' on a daily schedule.
C) Create a standard external table with the 'AUTO REFRESH' parameter set to 'TRUE'. This will automatically refresh the metadata whenever changes are detected in S3.
D) Create a series of views on top of the S3 bucket using 'READ_PARQUET function, updating view definitions whenever underlying files change.
E) Create an Iceberg table backed by the S3 bucket. Snowflake will automatically manage the metadata and handle incremental updates efficiently.

2. Snowpark DataFrame 'employee_df' contains employee data, including 'employee_id', 'department', and 'salary'. You need to calculate the average salary for each department and also retrieve all the employee details along with the department average salary.
Which of the following approaches is the MOST efficient way to achieve this?

A) Use 'groupBV to get a dataframe containing average salary by department and then use a Python UDF to iterate through the 'employee_df and add the value to each row
B) Create a temporary table with average salaries per department, then join it back to the original DataFrame.
C) Use a correlated subquery within the SELECT statement to calculate the average salary for each department for each employee.
D) Use the 'window' function with 'avg' to compute the average salary per department and include it as a new column in the original DataFrame.
E) Create a separate DataFrame with average salaries per department, then join it back to the original DataFrame.

3. Consider a scenario where you have a large dataset of sensor readings stored in a Snowflake table called 'SENSOR DATA'. You need to build an external function to perform complex calculations on these readings using a custom Python library hosted on AWS Lambda'. The calculation requires significant computational resources, and you want to optimize the data transfer between Snowflake and the Lambda function. The following SQL is provided: CREATE OR REPLACE EXTERNAL FUNCTION ARRAY) RETURNS ARRAY VOLATILE MAX BATCH ROWS = 2000 RETURNS NULL ON NULL INPUT API INTEGRATION = aws_lambda_integration AS 'arn:aws:lambda:us-east-1:123456789012:function:sensorProcessor'; Which of the following options would further optimize the performance and reduce data transfer costs, assuming the underlying Lambda function is correctly configured and functional?

A) Rewrite the custom Python library in Java and create a Snowflake User-Defined Function (UDF) instead of using an external function.
B) Increase the 'MAX BATCH ROWS' parameter to the maximum allowed value to send larger batches of data to the external function. Ensure Lambda function memory is increased appropriately.
C) Compress the data before sending it to the external function and decompress it within the Lambda function. Update the Lambda function to compress the array of results before sending it back to Snowflake and use Snowflake+s functions to decompress it.
D) Reduce the number of columns passed to the external function by performing pre-aggregation or filtering on the data within Snowflake before calling the function.
E) Convert the input data to a binary format (e.g., using 'TO_BINARY and FROM_BINARY' functions in Snowflake) before sending it to the Lambda function, and decode it in Lambda to reduce the size of the data being transmitted.

4. You have a large Snowflake table 'WEB EVENTS that stores website event data'. This table is clustered on the 'EVENT TIMESTAMP column. You've noticed that certain queries filtering on a specific 'USER ID' are slow, even though 'EVENT TIMESTAMP clustering should be helping. You decide to investigate further Which of the following actions would be MOST effective in diagnosing whether the clustering on 'EVENT TIMESTAMP is actually benefiting these slow queries?

A) Query the 'QUERY_HISTORY view to see the execution time of the slow query and compare it to the average execution time of similar queries without a 'USER filter.
B) Run 'SYSTEM$ESTIMATE QUERY COST to estimate the query cost to see if the clustering is impacting the cost.
C) Use the SYSTEM$CLUSTERING_INFORMATIOW function to get the 'average_overlaps' for the table and 'EVENT_TIMESTAMP' column. A low value indicates good clustering.
D) Execute 'SHOW TABLES' and check the 'clustering_key' column to ensure that the table is indeed clustered on 'EVENT _ TIMESTAMP'.
E) Run ' EXPLAIN' on the slow query and examine the 'partitionsTotal' and 'partitionsScanned' values. A significant difference indicates effective clustering.

5. Your team is implementing Snowpipe Streaming to ingest data from multiple IoT devices into a Snowflake table. Each device sends data continuously, and it's crucial to minimize latency. However, you observe that some records are occasionally missing in the target table. You have verified that the devices are sending all data and the client-side application using Snowpipe Streaming is functioning correctly. Which of the following is the MOST likely cause of the missing records, and how can you address it?

A) Duplicate records are being dropped due to Snowflake's internal deduplication mechanism; configure the table to allow duplicates.
B) The Sequence Token is not being managed correctly, leading to potential gaps or out-of-order delivery; ensure correct implementation of the Sequence Token mechanism in your ingestion code.
C) The Snowpipe Streaming client is exceeding its buffer capacity, leading to data loss; increase the buffer size on the client-side.
D) The Snowflake virtual warehouse is not scaled up sufficiently to handle the ingestion rate; increase the virtual warehouse size.
E) There is a delay in Snowflake's metadata refresh causing some new tables to be temporarily unavailable, retry after some time or manually refresh metadata.

Solutions: