NEW DAA-C01 TEST SYLLABUS, RELIABLE DAA-C01 EXAM PATTERN

New DAA-C01 Test Syllabus, Reliable DAA-C01 Exam Pattern

New DAA-C01 Test Syllabus, Reliable DAA-C01 Exam Pattern

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Tags: New DAA-C01 Test Syllabus, Reliable DAA-C01 Exam Pattern, DAA-C01 Reliable Source, DAA-C01 Practice Exams Free, DAA-C01 Exam Objectives

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q256-Q261):

NEW QUESTION # 256
You observe a 'Spilling to Local Storage' event in the Query Profile of a complex aggregation query. This indicates that the intermediate results are exceeding the memory capacity of the virtual warehouse. Which of the following actions would be MOST effective in mitigating this issue and improving the query performance?

  • A. Increase the value of the 'MAX MEMORY USAGE parameter for the session.
  • B. Optimize the query to reduce the amount of data processed before the aggregation.
  • C. Reduce the number of distinct values in the grouping columns.
  • D. Implement result caching to avoid re-computation.
  • E. Increase the virtual warehouse size to provide more memory resources.

Answer: B,C,E

Explanation:
Spilling to local storage indicates memory pressure. Increasing the virtual warehouse size provides more memory. Reducing the number of distinct values in grouping columns reduces the size of intermediate aggregation results. Optimizing the query to reduce data volume before aggregation also reduces memory footprint. ' is not a user-configurable parameter in Snowflake. Result caching won't help with the memory issue during the initial execution.


NEW QUESTION # 257
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 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.
  • B. 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.
  • 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 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.
  • E. 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.

Answer: B

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 # 258
You are responsible for collecting server log data from multiple geographically distributed data centers. The logs are generated at a high velocity and variety of formats (JSON, CSV, plain text). The requirement is to ensure minimal data loss and efficient ingestion into Snowflake, while also handling potential schema variations across different log sources. Which of the following is the MOST robust and scalable solution, considering potential schema drift and data volume?

  • A. Use a centralized file server to collect logs and then use Snowpipe with schema detection enabled on a single variant column in Snowflake.
  • B. Utilize a message queue (e.g., Kafka) to collect logs from all data centers and create an external table pointing to the message queue. Use Snowflake streams to ingest the data from the message queue into Snowflake.
  • C. Write a custom Python script to pull logs from each data center, transform them into a consistent CSV format, and then upload the CSV files to Snowflake using Snowpipe.
  • D. Employ a distributed log aggregation system (e.g., Fluentd or Logstash) to standardize the log format and then use Snowpipe to ingest the data into Snowflake.
  • E. Configure each data center to directly stream logs to Snowflake using the Snowflake JDBC driver.

Answer: D

Explanation:
A distributed log aggregation system (Fluentd/Logstash) is the best choice here. These systems are designed for handling high- velocity, varied log formats, and schema variations. They can buffer data to prevent data loss and transform the data into a consistent format before ingestion into Snowflake. Snowpipe provides efficient data loading from cloud storage. This combination provides scalability, reliability, and flexibility. Message queues require more configuration overhead and external tables can be slower for querying. Custom scripts are less scalable and harder to maintain. Direct streaming using JDBC is not recommended for high-volume data.


NEW QUESTION # 259
You are designing a data warehouse for a retail company. The "SALES table stores transaction data and includes columns like 'TRANSACTION ID', 'PRODUCT ID', 'CUSTOMER ID, 'SALE DATE', and 'SALE AMOUNT'. The 'PRODUCT ID' references the 'PRODUCTS table, and 'CUSTOMER references the 'CUSTOMERS' table. Which of the following strategies represent the MOST optimal approach to define primary keys in this scenario, considering Snowflake's best practices and the need for efficient query performance, assuming 'TRANSACTION ID' is globally unique?

  • A. Define 'TRANSACTION as the primary key on the 'SALES' table, 'PRODUCT ID as the primary key on the 'PRODUCTS' table, and 'CUSTOMER ID' as the primary key on the 'CUSTOMERS' table.
  • B. Define ' TRANSACTION_ID' as the primary key on the 'SALES' table. Define 'PRODUCT_ID' as the primary key on the 'PRODUCTS' table, and 'CUSTOMER ID as the primary key on the 'CUSTOMERS table. Then, create unique indexes on 'PRODUCT ID in 'SALES referencing 'PRODUCTS and in referencing 'CUSTOMERS'.
  • C. Define as the primary key on the 'SALES' table. Do not define primary keys on 'PRODUCTS' or 'CUSTOMERS'
  • D. Define a composite primary key on the 'SALES' table consisting of 'TRANSACTION D', 'PRODUCT ID, and 'CUSTOMER ID. Define 'PRODUCT ID' as the primary key on the 'PRODUCTS' table, and 'CUSTOMER_ID' as the primary key on the 'CUSTOMERS' table.
  • E. Do not define primary keys on any of the tables. Rely solely on Snowflake's internal optimizations.

Answer: A

Explanation:
While Snowflake does not enforce primary key constraints, defining them provides valuable metadata for the query optimizer. Since TRANSACTION_ID' is unique in 'SALES', it is a suitable primary key. Defining primary keys on 'PRODUCTS' and 'CUSTOMERS' is also appropriate for their respective tables. Creating unique indexes is redundant if primary keys are defined. Skipping primary key definitions entirely can hinder optimization. A composite key in 'SALES' is unnecessary as 'TRANSACTION_ID is already globally unique.


NEW QUESTION # 260
Consider a scenario where you have a table 'CUSTOMER ORDERS' with columns 'CUSTOMER ID', 'ORDER DATE' , 'ORDER TOTAL' , and 'PRODUCT CATEGORY'. You want to create a materialized view that calculates the sum of order totals for each customer, grouped by product category, and refreshed automatically on a daily basis. However, you are also concerned about minimizing the cost of materialized view maintenance. Which of the following strategies would be MOST cost-effective while still providing reasonably up-to-date data?

  • A. Create a materialized view and set the refresh schedule to 'ON CHANGE' with a clustering key on
  • B. Create a materialized view and schedule a daily refresh at a time of low system activity.
  • C. Create a standard view with the same aggregation logic and optimize the underlying table using clustering.
  • D. Create a materialized view without specifying a refresh schedule, and manually refresh it whenever the report is run.
  • E. Create a materialized view and set the refresh schedule to 'ON CHANGE'.

Answer: B

Explanation:
Scheduling a daily refresh allows the materialized view to be updated regularly without incurring the overhead of 'ON CHANGE' refreshes, which can be very costly if the underlying table is frequently updated. Manual refreshes would not provide up-to-date data automatically. 'ON CHANGE' without further optimization can be extremely expensive. A standard view wouldn't provide the performance benefits of a materialized view. Clustering on CUSTOMER ID might improve performance but would not address the refresh cost directly.


NEW QUESTION # 261
......

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