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Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions (Q283-Q288):

NEW QUESTION # 283
You have a table 'CUSTOMERS' with columns 'CUSTOMER ID', 'FIRST NAME', 'LAST NAME, and 'EMAIL'. You need to transform this data into a semi-structured JSON format and store it in a VARIANT column named 'CUSTOMER DATA' in a table called 'CUSTOMER JSON'. The desired JSON structure should include a root element 'customer' containing 'id', 'name', and 'contact' fields. Which of the following SQL statements, used in conjunction with a CREATE TABLE and INSERT INTO statement for CUSTOMER JSON, correctly transforms the data?

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

Answer: D

Explanation:
The correct answer constructs the JSON structure using nested 'OBJECT_CONSTRUCT functions. Option A directly creates a Snowflake VARIANT, which can be inserted into the 'CUSTOMER_DATR column. While many other approaches exist that involve parsing or converting to and from string values, those approaches are unnecessary because OBJECT_CONSTRUCT supports the correct desired behavior directly.

NEW QUESTION # 284
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 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$URL as input, and extracts the desired attributes from the returned JSON variant. A stage must also be created to host external function code.
B. Create a pipe using 'COPY INTO' statement with 'FILE FORMAT = (TYPE = JSON)' and 'ON_ERROR = CONTINUE that loads the JSON files directly into a staging table. Create a view on top of the staging table to extract the required fields. The must have = TRUE' configured if JSON files are nested array. Use ' ILIKE in your view for the discount flag.
C. Create an external function that takes a string as input and returns a BOOLEAN whether that string contains 'discount. Create a view on top of metadata_table', and using 'SYSTEM$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.
D. 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'.
E. 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.

Answer: A,C

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 # 285
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. 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.
B. 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.
C. 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.
D. 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.
E. 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.

Answer: B

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 # 286
You have data residing in AWS S3 in Parquet format, which is updated daily with new columns being added occasionally. The data is rarely accessed, but when it is, it needs to be queried using SQL within Snowflake. You want to minimize storage costs within Snowflake while ensuring the data can be queried without requiring manual table schema updates every time a new column is added to the S3 data'. Which approach is MOST suitable?

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

Answer: A

Explanation:
Option B is the most suitable because external tables with AUTO REFRESH enabled and properly configured file format (Parquet in this case) support schema evolution automatically. When new columns are added to the S3 data, Snowflake will detect these changes and update the external table's metadata accordingly. This eliminates the need for manual schema updates and minimizes storage costs because the data remains in S3. AUTO_REFRESH needs a configured notification integration. Option A will require manual ALTER TABLE ADD COLUMN commands which are not scalable, option C introduces unnecessary complexity with Spark. Option D using COPY INTO does not support external storage locations. Option E requires manual refresh.

NEW QUESTION # 287
A financial institution is using Snowflake to store transaction data for millions of customers. The data is stored in a table named 'TRANSACTIONS with columns such as 'TRANSACTION ID, 'CUSTOMER ID', 'TRANSACTION DATE, 'TRANSACTION_AMOUNT, and 'MERCHANT CATEGORY'. Analysts are running complex analytical queries that often involve filtering transactions by 'TRANSACTION_DATE, 'MERCHANT CATEGORY' , and 'TRANSACTION_AMOUNT ranges. These queries are experiencing performance bottlenecks. The data team wants to leverage query acceleration service to improve performance without significantly altering the existing query patterns. Which of the following actions or combination of actions would be MOST beneficial, considering the constraints and the nature of the queries? (Select TWO)

A. Enable Automatic Clustering on the 'TRANSACTIONS' table, ordering the keys as 'TRANSACTION_DATE, 'MERCHANT_CATEGORY', 'CUSTOMER_ID. Then, enable query acceleration on the virtual warehouse.
B. Create materialized views pre-aggregating the transaction data by 'MERCHANT_CATEGORY and 'TRANSACTION_DATE, and enable query acceleration on the virtual warehouse.
C. Create separate virtual warehouses dedicated to reporting queries and ad-hoc queries respectively. Enable query acceleration only for the warehouse running reporting queries.
D. Enable Search Optimization Service for the 'TRANSACTIONS' table, specifically targeting the 'MERCHANT_CATEGORY column. Enable query acceleration on the virtual warehouse.
E. Increase the size of the virtual warehouse used for running the queries and enable query acceleration on the warehouse without further modifications.

Answer: A,D

Explanation:
Enabling Automatic Clustering on 'TRANSACTIONS with the specified key order ('TRANSACTION DATES, 'MERCHANT_CATEGORY , 'CUSTOMER_ID') aligns the data layout with common query patterns, allowing Snowflake to efficiently prune irrelevant data during query execution. This drastically improves query performance. Enabling Search Optimization on the 'MERCHANT_CATEGORY further enhances query performance by creating search access paths that enable faster lookups and filtering based on merchant category. Simply increasing the warehouse size (option A) may provide some improvement, but it's less targeted and potentially less cost-effective than optimizing the data organization. While dedicated warehouses (option C) can improve concurrency, they do not address the underlying performance bottleneck related to data access. Materialized views (option E) can be beneficial, but they require careful design and maintenance, and they might not be flexible enough for ad-hoc queries with varying filter conditions. Clustering and search optimization provide a more general and efficient solution in this scenario.

NEW QUESTION # 288
......

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Snowflake Pdf DEA-C02 Braindumps: SnowPro Advanced: Data Engineer (DEA-C02) - PrepPDF Help you Pass for Sure

100% Pass Quiz 2025 Snowflake DEA-C02: High-quality Pdf SnowPro Advanced: Data Engineer (DEA-C02) Braindumps

Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions (Q283-Q288):

DEA-C02 Study Guide & DEA-C02 Test Dumps & DEA-C02 Practice Test

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