REVIEW KEY CONCEPTS WITH DEA-C02 EXAM-PREPARATION QUESTIONS

Review Key Concepts With DEA-C02 Exam-Preparation Questions

Review Key Concepts With DEA-C02 Exam-Preparation Questions

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Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions (Q127-Q132):

NEW QUESTION # 127
You are responsible for optimizing query performance on a Snowflake table called 'WEB EVENTS, which contains clickstream data'. The table has the following structure: CREATE TABLE WEB EVENTS ( event_id VARCHAR(36), user_id INT, event_time TIMESTAMP NTZ, event_type VARCHAR(50), page_url VARCHAR(255), device_type VARCHAR(50) Users frequently run queries that filter the 'WEB EVENTS table based on a combination of 'event_type', and a date range derived from 'event_time' You observe that these queries are consistently slow Which of the following strategies would be MOST effective in improving the performance of these frequently executed queries?

  • A. Create a materialized view that pre-aggregates data by 'event_type' , 'device_type' , and day (derived from 'event_time').
  • B. Add a column to the 'WEB EVENTS' table for the date part of 'event_time' and create a clustering key using the new date column along with and device_type' .
  • C. Create a clustering key with the following order: 'event_type' , 'device_type' , 'event_time' .
  • D. Create a search optimization service on the 'page_url' column.
  • E. Create a clustering key on 'event_time' .

Answer: A,C

Explanation:
Options C and D are the most effective. A materialized view (C) would pre-compute the frequently requested aggregations, significantly reducing query execution time. Creating a clustering key on 'event_type', and 'event_time' (D) would optimize data organization for the common filter criteria, improving micro-partition pruning. Clustering only on 'event_time' (B) might help with date range filtering but won't address the 'event_type' and 'device_type' filters. Search optimization service (A) is designed for point lookups on string values and is not appropriate for this scenario. Adding a separate column for the date part of the event (E) is redundant and unnecessary. Snowflake can extract date parts from a timestamp column.


NEW QUESTION # 128
You are developing a data pipeline in Snowflake that uses SQL UDFs for data transformation. You need to define a UDF that calculates the Haversine distance between two geographical points (latitude and longitude). Performance is critical. Which of the following approaches would result in the most efficient UDF implementation, considering Snowflake's execution model?

  • A. Create an External Function (using AWS Lambda or Azure Functions) to calculate the Haversine distance. This allows for offloading the computation to a separate compute environment.
  • B. Create a Java UDF that calculates the Haversine distance, leveraging optimized mathematical libraries. This allows for potentially faster execution due to lower- level optimizations.
  • C. Create a SQL UDF leveraging Snowflake's VECTORIZED keyword, hoping to automatically leverage SIMD instructions, without any code changes to mathematical calculation inside the UDF
  • D. Create a SQL UDF that pre-calculates the RADIANS for latitude and longitude only once and stores them in a temporary table, using those values for subsequent distance calculations within the same session.
  • E. Create a SQL UDF that directly calculates the Haversine distance using Snowflake's built-in mathematical functions (SIN, COS, ACOS, RADIANS). This is straightforward and easy to implement.

Answer: E

Explanation:
SQL UDFs are generally the most efficient for simple calculations within Snowflake because they are executed within the Snowflake engine, minimizing data movement and overhead. While Java UDFs (option B) can offer optimizations, the overhead of invoking the Java environment often outweighs the benefits for this type of calculation. External Functions (option C) introduce significant latency due to network communication. Option D provides temporary performance improvements for the specific session, but is not the most efficient general solution. Vectorized keyword doesn't exists in snowflake to create UDFs, Hence it won't allow compilation. This questions emphasis on understanding the trade-offs between different UDF types and their performance implications within the Snowflake architecture.


NEW QUESTION # 129
You are working with a Snowflake table 'customer_data' which contains customer information stored in a VARIANT column named raw_info'. The 'raw_info' JSON structure includes nested addresses, and preferences. Your task is to extract the city from the first address in the 'addresses' array, and the customer's preferred communication method from the 'preferences' object. Some customers might not have addresses or preferences defined. Select the two SQL snippets that correctly and efficiently extract this data, handling missing fields gracefully and providing appropriate type casting. Address array is in the format 'addresses: [ { 'city': '...', 'state': ' '},

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

Answer: A,D

Explanation:
Options D and E correctly extract the required data while handling potential null values and type conversions. Option D uses TRY_TO_VARCHAR which returns NULL if the cast fails or the value is missing. Option E uses ' IFF in conjunction with ' IS_ARRAY , 'ARRAY SIZE', and 'IS OBJECT to check for the existence and validity of the 'addresses' array and 'preferences' object, and then uses TRY_TO_VARCHAR for safe type conversion, making it very robust. Option A will throw an error if the 'addresses' array or 'preferences' object is missing. B is wrong as its where condition will filter the results. C does not handle null and type conversion.


NEW QUESTION # 130
You are tasked with creating a SQL UDF in Snowflake to mask sensitive customer data (email addresses) before it's used in a reporting dashboard. The masking should replace all characters before the '@' symbol with asterisks, preserving the domain part. For example, '[email protected]' should become ' @example.com'. Which of the following SQL UDF definitions correctly implements this masking logic, while minimizing the impact on Snowflake compute resources?

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

Answer: D

Explanation:
Option A correctly uses LPAD and SPLIT PART to replace the username portion of the email with asterisks while retaining the domain. It efficiently avoids unnecessary calculations or regular expressions. Option B is incorrect because it simply replaces everything before the @ with ' @', which is not dynamic. Option C's logic is flawed and would not mask correctly. Option D provides additional casing for when an email doesn't have '@' symbol, which is technically correct for handling unexpected input, but is not needed for the explicit requirement. Option E has incorrect syntax. This question assesses understanding of string manipulation functions and their optimal usage within a UDF.


NEW QUESTION # 131
You are tasked with implementing row-level security (RLS) on a 'SALES' table to restrict access based on the 'REGION' column. Users with the 'NORTH REGION ROLE should only see data where 'REGION = 'NORTH". You've created a row access policy named north_region_policy'. After applying the policy to the 'SALES table, users with the 'NORTH REGION ROLE are still seeing all rows.
Which of the following is the MOST likely reason for this and how can it be corrected?

  • A. The ' does not have the USAGE privilege on the database and schema containing the 'SALES' table. Grant the USAGE privilege to the role.
  • B. The user has not logged out and back in since the role was granted to them. Force the user to re-authenticate.
  • C. The is not enabled. Execute 'ALTER ROW ACCESS POLICY ON SALES SET ENABLED = TRUE;'
  • D. The policy needs to be explicitly refreshed. Execute 'REFRESH ROW ACCESS POLICY north_region_policy ON SALES;'
  • E. The policy function within is not using the correct context function to determine the user's role. It should use 'CURRENT ROLE()' instead of 'CURRENT_USER()'

Answer: A

Explanation:
Row access policies require the role to have USAGE privilege on the database and schema. Without this privilege, the policy cannot be enforced. The other options, while potentially relevant in other scenarios, are not the most likely cause for the described issue. Row access policies are automatically enabled when applied and the correct context function would be CURRENT_ROLE(). A refresh command is not required.


NEW QUESTION # 132
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