NEW SNOWFLAKE DAA-C01 TEST REVIEW & NEW DAA-C01 DUMPS FILES

New Snowflake DAA-C01 Test Review & New DAA-C01 Dumps Files

New Snowflake DAA-C01 Test Review & New DAA-C01 Dumps Files

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q147-Q152):

NEW QUESTION # 147
A retail company wants to visualize sales performance across different product categories and regions. The business stakeholders need to identify both overall sales trends and granular insights into the performance of specific products in specific regions. They require a dashboard that allows for easy comparison of sales across categories and regions, highlighting best and worst performers. Which combination of chart types would be MOST effective for this dashboard, considering scalability and the need to avoid over-plotting?

  • A. A heat grid showing sales by category and region, a time series chart for overall sales trends, and a treemap representing the contribution of each category to total sales.
  • B. A scatter plot comparing sales volume and profit margin for each product, a bar chart for sales by region, and a gauge chart indicating overall sales target achievement.
  • C. A stacked bar chart for sales by category, a line chart for overall sales trend over time, and a pie chart for regional sales distribution.
  • D. A geographical map visualizing sales by region with color-coded regions, a time series chart for overall sales trends, and a detail table for viewing sales by product categories.
  • E. A combination of bullet charts to show sales performance against targets for each region and category, a time series chart for overall sales trend, and a scatter plot showing discount vs quantity.

Answer: A

Explanation:
A heat grid effectively visualizes the relationship between two categorical variables (category and region) using color intensity, making it easy to identify high and low sales areas. A time series chart is appropriate for displaying trends over time. A treemap shows the proportional size of each category contributing to total sales. Stacked bar charts can become difficult to read with many categories and pie charts are not ideal for precise comparisons. Scatter plots are useful for correlation analysis (Sales vs Profit). A map would be good for high level visualization but not for specific numbers or precise details. Bullet charts are more suitable for target vs actual comparisons than a regional overview.


NEW QUESTION # 148
You are tasked with diagnosing a performance bottleneck in a daily ETL process that loads data into a Snowflake table called 'SALES DATA'. The ETL process has been running slower than usual for the past week. You suspect a change in the source data volume or distribution. Which of the following Snowflake features and SQL queries would be MOST helpful in identifying the root cause?

  • A. Run 'SELECT COUNT( ) FROM SALES DATA;' to check total record count and compare against historical values.
  • B. Use Snowflake's Query Profile feature to analyze the execution plan of the ETL queries and identify the stages consuming the most time.
  • C. Use Snowflake's Time Travel feature to compare the size and structure of the 'SALES DATA table at different points in time, specifically before and after the performance degradation started.
  • D. Query Snowflake's INFORMATION SCHEMA.QUERY HISTORY view to compare the execution times of recent ETL runs with historical averages, filtering by query ID or user.
  • E. Analyze the 'SALES DATA' table's clustering keys and statistics using 'SHOW TABLES LIKE 'SALES DATA';' and 'DESCRIBE TABLE SALES DATA;' to determine if the data distribution has changed significantly, potentially leading to inefficient query performance.

Answer: B,C,D,E

Explanation:
Options A, B, C, and D are all helpful in diagnosing the bottleneck. The query profile (A) pinpoints specific query stages, query history (B) reveals performance trends, table statistics (C) indicate data skewness, and Time Travel (D) allows for data structure comparisons. While E is useful as an initial check, it is not sufficient to determine the root cause alone.


NEW QUESTION # 149
You have a 'PRODUCT SALES table with columns 'PRODUCT ID, 'SALE DATE, 'UNITS SOLD, and 'PRICE PER UNIT. You need to perform a complex data transformation for reporting purposes. Specifically, you need to calculate the 7-day moving average of the total revenue CUNITS SOLD 'PRICE PER UNIT) for each product. However, you also need to handle cases where data might be missing for some dates. For those missing dates within a 7-day window, you want to impute a zero value for 'UNITS SOLD before calculating the moving average. Which of the following approaches is MOST efficient and accurate in Snowflake to achieve this, assuming you want to minimize code complexity and execution time? (Choose all that apply)

  • A. Using a User-Defined Function (UDF) written in Python to calculate the moving average with imputation.
  • B. Directly using a window function with the 'AVG()' aggregate function, without handling missing dates.
  • C. Using Snowflake's Time Travel feature to retrieve historical data and fill in the gaps before calculating the moving average.
  • D. Creating a stored procedure to loop through each product and calculate the moving average with imputation using procedural logic.
  • E. Using a series of Common Table Expressions (CTEs) to generate a date series, left join with the table, impute missing values with zero, and then apply a window function for the moving average calculation.

Answer: E

Explanation:
Option B (using CTEs and a left join) is the most efficient and accurate approach. Here's why: CTEs provide modularity: They break down the complex transformation into logical steps, making the code easier to read and maintain. Date series generation: A CTE can generate a complete date series within the relevant range. This ensures that all dates within the 7-day window are considered. Left Join and Imputation: Left joining the generated date series with the 'PRODUCT _ SALES' table allows you to identify missing dates. Then, you can use 'COALESCE or similar functions to impute zero values for 'UNITS_SOLD' for these missing dates. Window Function: Finally, you can apply the 'AVG()' window function over the revenue (UNITS_SOLD PRICE_PER_UNIT), ordered by date, to calculate the 7-day moving average. Here's why the other options are less suitable: A (UDF): While UDFs are powerful, they can introduce performance overhead compared to native SQL operations. Imputing missing values and calculating a moving average are tasks that can be efficiently handled with SQL. C (Direct Window Function): This is incorrect as this won't handle missing dates and will lead to inaccurate moving averages. D (Time Travel): Time Travel is useful for data recovery and auditing, not for imputing missing data in a moving average calculation. E (Stored Procedure): Stored procedures with procedural logic are generally less efficient than set-based SQL operations, especially in Snowflake's columnar architecture. They also increase code complexity.


NEW QUESTION # 150
You are tasked with ingesting clickstream data from a website into Snowflake for real-time analytics. The website generates approximately 100,000 events per minute. The business requires insights into user behavior with a maximum latency of 5 minutes. Which data collection strategy would be MOST appropriate to meet these requirements, considering both cost and near real-time needs?

  • A. Near real-time ingestion using Snowpipe with auto-ingest enabled and micro-batches of data.
  • B. Utilizing a third-party ETL tool to transfer data in hourly batches.
  • C. Scheduled task using a Python script to extract data from an API endpoint every 10 minutes.
  • D. Directly inserting data using JDBC from the web application.
  • E. Batch ingestion using Snowpipe with file sizes of IGB uploaded every 15 minutes.

Answer: A

Explanation:
Near real-time ingestion using Snowpipe with auto-ingest is the most appropriate choice. It provides low latency (minutes), scales well with high data volume, and is cost-effective compared to maintaining a custom solution or paying for a full-fledged ETL tool for this specific scenario. Batch processing introduces too much latency (15 minutes or more), scheduled tasks can become resource intensive and JDBC inserts directly from the application can create performance bottlenecks and security concerns.


NEW QUESTION # 151
A data analyst is tasked with loading data into a Snowflake table 'ORDERS' with the following structure: 'CREATE TABLE ORDERS ( ORDER ID INT, CUSTOMER ID INT, ORDER DATE DATE, TOTAL_AMOUNT The data analyst needs to ensure that 'ORDER ID' is unique and not null, 'CUSTOMER ID' references a valid customer in the 'CUSTOMERS' table (column name 'CUSTOMER ID'), and 'ORDER DATE' is not in the future. Which of the following combination of constraints is the most efficient and appropriate way to enforce these Fules in Snowflake? 'CREATE TABLE CUSTOMERS ( CUSTOMER ID INT PRIMARY KEY, CUSTOMER_NAME VARCHAR(255));'.

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

Answer: B

Explanation:
Option E is the most appropriate and efficient. Using a PRIMARY KEY implies an index which while beneficial for joins isn't necessary if simple uniqueness and not null are the primary requirement. A CHECK constraint 'ORDER_DATE <= is the best way to prevent future dates as TRIGGER is not available and view doesn't prevent data from being ingested.


NEW QUESTION # 152
......

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