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DEA-C02 study guide & DEA-C02 training torrent & DEA-C02 free dumps

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Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions (Q31-Q36):

NEW QUESTION # 31
You are developing a JavaScript stored procedure in Snowflake using Snowpark to transform data'. The procedure needs to efficiently calculate the sum of 'amount' for each 'customer_id' in a large table named 'orders'. You want to avoid transferring large amounts of data to the client and utilize Snowpark's pushdown capabilities. Which of the following JavaScript code snippets is the MOST efficient and correct way to achieve this? Assume 'snowflake' binding is available.

Answer: C

Explanation:
Option B is the most efficient because it leverages Snowpark's 'groupBy' and 'sum' functions, pushing the aggregation computation to the Snowflake engine. This avoids transferring all the raw data to the client-side JavaScript environment, minimizing network traffic and improving performance. Options A, C and D are not leveraging snowpark dataframe. E uses deprecated methods

NEW QUESTION # 32
A data engineering team is building a real-time dashboard in Snowflake to monitor website traffic. The dashboard relies on a complex query that joins several large tables. The query execution time is consistently exceeding the acceptable threshold, impacting dashboard responsiveness. Historical data is stored in a separate table and rarely changes. You suspect caching is not being utilized effectively. Which of the following actions would BEST improve the performance of this dashboard and leverage Snowflake's caching features?

A. Increase the size of the virtual warehouse. A larger warehouse will have more resources to execute the query, and the results will be cached for a longer period.
B. Replace the complex query with a series of simpler queries. This will reduce the amount of data that needs to be processed at any one time.
C. Create a materialized view that pre-computes the results of the complex query. Snowflake will automatically refresh the materialized view when the underlying data changes.
D. Use 'RESULT_SCAN' to cache the query result in the user session for subsequent queries. This is especially effective for large datasets that don't change frequently.
E. Materialize the historical data into a separate table that utilizes clustering and indexing for faster query performance. Refresh this table periodically.

Answer: C

Explanation:
Materialized views are the best option in this scenario. They pre-compute the results of the complex query and store them in a separate table. Snowflake automatically refreshes the materialized view when the underlying data changes, ensuring that the dashboard always displays the most up-to-date information. While increasing the virtual warehouse size (D) can help initially, it's a more expensive and less targeted solution. 'RESULT_SCAN' (A) is session-specific and not suitable for persistent caching for a dashboard accessed by multiple users. Materializing the historical data (B) might help, but it doesn't address the core issue of the complex query. Breaking the query into smaller parts (E) might not be efficient and can introduce complexity.

NEW QUESTION # 33
You are building a data pipeline that involves ingesting data from AWS S3 into Snowflake using Snowpipe. The data arrives in small files frequently, and you are experiencing performance issues with delayed data availability. You need to optimize the pipeline for near real-time ingestion. Which combination of strategies will MOST effectively address this scenario?

A. Manually refresh the Snowpipe using the 'ALTER PIPE ... REFRESH' command every few minutes to force ingestion of new data.
B. Configure S3 event notifications to trigger Snowpipe only when a sufficient number of files have arrived in the S3 bucket, using a serverless function (like AWS Lambda) to manage the file count threshold.
C. Enable auto-ingest on the Snowpipe and increase the frequency of S3 event notifications to Snowflake. Combine this with clustering the target table on a relevant column to optimize query performance after loading.
D. Increase the warehouse size used for the Snowpipe load process and adjust the 'MAX FILE SIZE' parameter on the pipe definition to match the size of the incoming files.
E. Implement a micro-batching process using a third-party tool (like Apache Spark) to aggregate the small files into larger batches before loading them into S3, then configure Snowpipe to ingest the larger files.

Answer: C

Explanation:
Enabling auto-ingest on the Snowpipe and increasing the frequency of S3 event notifications is the most effective way to handle frequent small files. Auto- ingest automatically loads files as soon as they are available, and frequent notifications ensure minimal delay. Clustering the target table improves query performance by organizing data based on a relevant column, leading to faster data retrieval. The option A related to MAX FILE SIZE is invalid since it does not exist in the context of Snowpipe. Options involving third-party tools add complexity and latency. Manually refreshing the pipe is not a scalable solution. Option C is not ideal because you want low latency, not delaying trigger. Option D provides the lowest latency at scale, while A, B, C and E would likely have a negative impact on latency, add extra steps, or are plain incorrect.

NEW QUESTION # 34
You have a table named 'sales_data' with columns 'region', 'product_category', and 'revenue'. You want to create an aggregation policy to prevent users without the 'FINANCE ADMIN' role from seeing revenue values aggregated across all regions. Instead, these users should only see revenue aggregated at the region level. The policy should return NULL for the 'revenue' column when aggregated across all regions by non-admin users. Which of the following SQL snippets correctly implements this aggregation policy?

A. Option C
B. Option D
C. Option A
D. Option B
E. Option E

Answer: D

Explanation:
Option B correctly uses the GROUPING() function to identify when the aggregation is being performed across all regions (GROUPING(region) = 1). It then returns NULL for non-FINANCE_ADMIN users in this scenario. Options A is incorrect because grouping(region) = 0 means region-level aggregation. Options C does not consider regions in the policy, so non-admin users will always see null revenue. Option D's syntax is incorrect. must be combined with GROUPING as in E to work correctly. Option E can also work but the CASE statement is clearer and easier to understand.

NEW QUESTION # 35
You are tasked with building a data pipeline that ingests JSON data from a series of publically accessible URLs. These URLs are provided as a list within a Snowflake table 'metadata_table', containing columns 'file_name' and 'file url'. Each JSON file contains information about products. You need to create a view that extracts product name, price, and a flag indicating whether the product description contains the word 'discount'. Which of the following approaches correctly implements this, optimizing for both performance and minimal code duplication, using external functions for text processing?

A. Create a stored procedure that iterates through 'metadata_table', downloads each JSON file using 'SYSTEM$URL GET, parses the JSON, extracts the required fields, and inserts the data into a target table. Then, create a view on top of the target table. Use 'LIKE '%discount%' to identify if a product description contains the word 'discount'.
B. Create an external function that takes a string as input and returns a BOOLEAN whether that string contains 'discount. Create a view on top of metadata_table', and using 'SYSTEM$URL_GET' fetch the content from 'file_url'. The JSON can then be parsed and the fields like price, name and description can be fetched. Use within the view to flag the presence of discount.
C. Create an external function that takes a URL as input and returns a BOOLEAN indicating if any error occured while processing the URL and the data. Create a stored procedure that iterates through 'metadata_table' , calls external function for each URL, reports error and then processes the data. A stage must also be created to host external function code.
D. Create an external function that takes a URL as input and returns a JSON variant containing the extracted product name, price, and discount flag (using 'LIKE Then, create a view that selects from calls the external function with 'SYSTEM$URL as input, and extracts the desired attributes from the returned JSON variant. A stage must also be created to host external function code.
E. Create a pipe using 'COPY INTO' statement with 'FILE FORMAT = (TYPE = JSON)' and 'ON_ERROR = CONTINUE that loads the JSON files directly into a staging table. Create a view on top of the staging table to extract the required fields. The must have = TRUE' configured if JSON files are nested array. Use ' ILIKE in your view for the discount flag.

Answer: B,D

Explanation:
Option B correctly leverages an external function to encapsulate the logic of fetching and processing the JSON data from the URL. The external function promotes code reusability and reduces complexity in the view. Option E is also correct as it demonstrates how to process fetched JSON data and use a UDF to enhance the transformation. Option A involves a procedural approach that is less efficient than using an external function or pipes. Option C does not directly work with data URLs and is geared more towards data residing within Snowflake storage. Option D is incorrect because it creates the external function just for identifying errors and creates a stored procedure just to process the data.

NEW QUESTION # 36
......

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