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DAA-C01 Exam Paper Pdf, DAA-C01 Clear Exam

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q24-Q29):

NEW QUESTION # 24
How do logging and monitoring solutions contribute to data processing solutions? (Select all that apply)

A. Respond to processing failures promptly
B. Automate data processing effectively
C. Provide insights into processing status
D. Ensure real-time data processing

Answer: A,C

Explanation:
Logging and monitoring solutions help respond to processing failures promptly and provide insights into processing status.

NEW QUESTION # 25
How does incorporating visualizations in reports and dashboards aid in presenting data for business use analyses?

A. Visualizations enhance data comprehension for effective analysis.
B. Presenting data visually doesn't impact business use analyses.
C. It limits data presentation to textual formats only.
D. Visualizations complicate data representation, hindering analysis.

Answer: A

Explanation:
Visualizations enhance data comprehension, aiding effective analysis in business use scenarios.

NEW QUESTION # 26
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 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.
B. 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.
C. 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.
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: B,C

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 # 27
You are tasked with optimizing a dashboard displaying sales performance across different regions. The dashboard queries a large table called 'SALES DATA, which is partitioned by 'SALE DATE. The dashboard loads very slowly. The data analyst team notices that queries for the dashboard often filter on a specific region and a date range. Which of the following steps can you take to improve the dashboard's performance? (Select TWO)

A. Create a materialized view that pre-aggregates the data by region and date. The materialized view should be incrementally updated.
B. Create a table clone of 'SALES_DATX and point the dashboard to the clone instead.
C. Create a search optimization policy on the 'SALES DATA' table for the 'REGION' column.
D. Increase the size of the warehouse used to query the 'SALES DATA' table.
E. Recluster the SALES DATA' table on 'REGION' and 'SALE DATE.

Answer: A,E

Explanation:
Creating a materialized view (A) pre-aggregates the data, significantly reducing the amount of data that needs to be processed for each dashboard refresh. Reclustering on 'REGIONS and (D) physically organizes the data on disk based on these frequently used filter columns, improving query performance by minimizing the need to scan irrelevant partitions. Table clones (B) provide a separate copy of the data but don't inherently improve query performance. Search optimization (C) is useful for point lookups, but not as effective for date range filtering in this scenario. Increasing warehouse size (E) helps, but optimizing the data structure offers a more fundamental and cost-effective improvement.

NEW QUESTION # 28
You're tasked with creating a Snowsight dashboard to monitor the performance of different ETL pipelines. The dashboard needs to display the average run time and the number of errors for each pipeline over the last 7 days. The data is stored in a table called 'ETL LOGS' with columns 'end_time', and 'error_flag' (boolean). You need to present this information in a way that users can easily compare the performance of different pipelines. Which of the following SQL queries, used as the basis for a Snowsight tile, would be MOST appropriate for this dashboard?

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

Answer: D

Explanation:
Option A is the most accurate. It correctly calculates the average run time in seconds using start_time, end_time)' and the error count using a conditional aggregation 'SUM(CASE WHEN error_flag THEN 1 ELSE 0 END)'. Option B is incorrect because - start_timey will not return runtime in seconds, it returns a fractional number of days. Option C uses IFF and datediff which is acceptable. Option D only counts the total logs and doesn't filter if there is an error. Option E is also technically correct by converting the boolean to a number to sum. Option A uses standard SQL which might be more preferable.

NEW QUESTION # 29
......

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