VALID REAL DAA-C01 QUESTION - FIND SHORTCUT TO PASS DAA-C01 EXAM

Valid Real DAA-C01 Question - Find Shortcut to Pass DAA-C01 Exam

Valid Real DAA-C01 Question - Find Shortcut to Pass DAA-C01 Exam

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q75-Q80):

NEW QUESTION # 75
You have a Snowflake table containing order data'. You need to calculate the shipping cost for each order based on the order amount and the destination country. You decide to use a Java UDF for this calculation, as the logic is complex and involves external APIs (simulated here). The UDF should take the order amount (FLOAT) and destination country (VARCHAR) as input and return the calculated shipping cost (FLOAT). The Java code requires external JAR files to be imported. Which of the following options correctly defines and calls the Java UDF in Snowflake, assuming the necessary JAR file has been uploaded to a stage named 'my_stage'?

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

Answer: B

Explanation:
Option E is the most correct because the function definition does not require the definition of the class 'com.example.ShippingCalculator' within the function body. Since the jar file is defined within the imports section, snowflake does not need the explicit definition. Option A, C, and D requires the function and class definition which is already defined in the jar, and defining it again will lead to conflicts. Option B doesn't correctly define the class. All the rest of the options either try to define the Java code inline (which is incorrect when using IMPORTS) or have syntax errors in the UDF definition.


NEW QUESTION # 76
You are tasked with creating a dashboard to visualize sales performance across different product categories and regions. The data is stored in a Snowflake table named with columns: 'SALE DATE (DATE), 'PRODUCT CATEGORY (VARCHAR), 'REGION' (VARCHAR), 'SALES_AMOUNT (NUMBER). The business stakeholders want to see a trend of monthly sales for the past year, a breakdown of sales by region, and a comparison of sales between product categories. Which of the following approaches would be MOST effective and efficient in Snowflake for generating the data needed for these visualizations, considering the need for dashboard responsiveness and minimal query cost?

  • A. Create a data pipeline that transforms the raw 'SALES_DATX into summary tables using tasks and streams. These summary tables are optimized for the specific queries required by the dashboard visualizations.
  • B. Create multiple materialized views, each specifically designed for one of the dashboard visualizations (monthly sales trend, sales by region, sales by product category). Refresh these materialized views regularly.
  • C. Create a single, complex SQL query that joins the 'SALES DATA' table with itself multiple times, using window functions and subqueries to calculate all the required aggregations and breakdowns within the query. The dashboard will directly query this view.
  • D. Use Snowflake's caching mechanism extensively to cache the results of individual queries within the dashboard, ensuring that subsequent requests for the same data are served from the cache. Rely on the dashboard's internal caching capabilities.
  • E. Export the data to an external data warehouse for visualization purpose to avoid overhead of Snowflake visualization tool.

Answer: A,B

Explanation:
Creating multiple materialized views (B) allows for pre-calculated aggregations tailored to each visualization, significantly improving dashboard responsiveness and reducing query costs. Using tasks and streams to transform the raw data into optimized summary tables (D) also pre-processes the data, providing similar benefits to materialized views, but allows for more complex transformations and incremental updates. A single complex query (A) can be slow and resource-intensive. Relying solely on caching (C) might not be sufficient for complex aggregations or large datasets. Exporting data to a completely new system adds overhead and is unnecessary given Snowflake's capabilities.


NEW QUESTION # 77
You are using Snowpipe to continuously load data from an external stage (AWS S3) into a Snowflake table named 'RAW DATA. You notice that the pipe is frequently encountering errors due to invalid data formats in the incoming files. You need to implement a robust error handling mechanism that captures the problematic records for further analysis without halting the pipe's operation. Which of the following approaches is the MOST effective and Snowflake-recommended method to achieve this?

  • A. Utilize Snowpipe's 'VALIDATION_MODE' parameter set to to identify and handle invalid records. This requires modification of the COPY INTO statement to redirect errors to an error table.
  • B. Configure Snowpipe's 'ON_ERROR parameter to 'CONTINUE' and rely on the 'SYSTEM$PIPE_STATUS' function to identify files with errors. Then, manually query those files for problematic records.
  • C. Implement Snowpipe's 'ERROR _ INTEGRATION' object, configuring it to automatically log error records to a designated stage location in JSON format for later analysis. This requires updating the pipe definition.
  • D. Implement a custom error logging table and modify the Snowpipe's COPY INTO statement to insert error records into this table using a stored procedure called upon failure.
  • E. Disable the Snowpipe and manually load data using a COPY INTO statement with the 'ON_ERROR = 'SKIP_FILE" option, then manually inspect the skipped files.

Answer: C

Explanation:
Snowflake's 'ERROR INTEGRATION' feature, when configured with a pipe, automatically logs details of records that fail during ingestion to a specified stage. This provides a structured and readily accessible log of errors without interrupting the data loading process. Option A is not a native feature. Option B, while potentially usable, doesn't directly integrate with pipes as the PRIMARY mechanism. Option C involves more manual intervention and doesn't offer structured error logging. Option E defeats the purpose of automated loading via Snowpipe.


NEW QUESTION # 78
A data analyst is tasked with optimizing a query that aggregates data from a table 'ORDERS' containing order details, including columns like 'ORDER ID', 'CUSTOMER ID, 'ORDER DATE, 'PRODUCT ID', and 'QUANTITY. The query calculates the total quantity of products ordered per customer and month. The current query is as follows: SELECT CUSTOMER ID, DATE TRUNC('MONTH', ORDER DATE) AS ORDER MONTH, SUM(QUANTITY) AS TOTAL QUANTITY FROM ORDERS GROUP BY CUSTOMER_ID, ORDER_MONTH ORDER BY CljSTOMER_lD, ORDER_MONTH; Deopite the 'ORDERS' table being relatively small (10 million rows), the query performance is slow. The analyst suspects a poorly chosen warehouse size. Which of the following actions, combined with monitoring query execution, would be MOST beneficial to determine the optimal warehouse size and improve query performance?

  • A. Set the parameter to a low value to prevent long-running queries and force Snowflake to automatically optimize the warehouse size.
  • B. Run the query multiple times with different warehouse sizes, recording the execution time for each size. Choose the warehouse size with the lowest execution time, regardless of cloud services usage.
  • C. Increase the warehouse size to the largest available size and monitor query execution time and cloud services usage. If cloud services usage is high, decrease the warehouse size until a balance is achieved.
  • D. Use the query history to determine the average execution time for similar queries and choose a warehouse size that is slightly larger than the one used for those queries.
  • E. Start with the smallest warehouse size and incrementally increase the size, monitoring query execution time and cloud services usage. Stop increasing the size when query time plateaus or cloud services usage increases significantly.

Answer: E

Explanation:
The most beneficial approach is to start with the smallest warehouse size and incrementally increase it (B). This allows for observing the impact of warehouse size on query performance and cloud services usage. Increasing until the query time plateaus or cloud services usage increases significantly indicates the point of diminishing returns. Simply using the largest size (A) may be wasteful, and ignoring cloud services usage (C) can lead to cost overruns. Query history (D) may not be relevant if the query is significantly different. Setting a timeout (E) will not optimize the warehouse size.


NEW QUESTION # 79
Consider a table 'sales data' with columns 'product id', 'sale date', and 'revenue'. You need to calculate the cumulative revenue for each product over time, but only for the top 10 products by total revenue. What is the most efficient way to achieve this in Snowflake?

  • A. Use 'SUM(revenue) OVER (PARTITION BY product_id ORDER BY sale_datey to calculate the cumulative revenue for all products, then filter the results to include only the top 10 products based on their final cumulative revenue.
  • B. Create a temporary table to store the total revenue for each product, then select the top 10 from the temporary table. Join this result with 'sales_data' and apply ' SUM(revenue) OVER (PARTITION BY product_id ORDER BY sale_datey for the cumulative revenue calculation.
  • C. Use the aggregation to identify top 10 'product_id' and calculate the cumilative revenue using SUM(revenue) OVER (PARTITION BY product_id ORDER BY sale_date)'.
  • D. Use a subquery to find the top 10 'product_id' based on total revenue, then join this subquery with 'sales_data' and use 'SUM(revenue) OVER (PARTITION BY product_id ORDER BY sale_date)' to calculate the cumulative revenue.
  • E. Use a window function to rank products by total revenue, then filter for the top 10 ranks using a 'QUALIFY' clause. Finally, calculate cumulative revenue using 'SUM(revenue) OVER (PARTITION BY product_id ORDER BY sale_datey.

Answer: E

Explanation:
Option B is the most efficient. and 'QUALIFY allow filtering and ranking within a single query, and efficiently calculates the cumulative revenue. Option A requires a join which can be less performant. Option C involves creating a temporary table which adds overhead. Option D calculates cumulative revenue for all products before filtering, which is unnecessary work. Option E could be considered, however APPROX TOP_N is approximate and the requirement asks for an exact calculation.


NEW QUESTION # 80
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