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Snowflake DAA-C01 Exam Syllabus Topics:

Section	Objectives
Topic 1: Data Loading and Unloading	- Data export 1. UNLOAD and external stages - Data ingestion methods 1. COPY INTO and bulk loading 2. Continuous ingestion and Snowpipe concepts
Topic 2: Security, Governance, and Data Sharing	- Access control and security 1. Role-based access control (RBAC) 2. Authentication and encryption concepts - Data sharing and governance 1. Secure data sharing 2. Data masking and policies
Topic 3: Snowflake Architecture and Data Platform Fundamentals	- Data platform fundamentals 1. Separation of storage and compute 2. Data lifecycle in Snowflake - Snowflake architecture concepts 1. Virtual warehouses and scaling 2. Cloud services layer, compute layer, storage layer
Topic 4: Data Transformation and Analysis	- Analytical workloads 1. Query optimization for analytics 2. Materialized views and caching - SQL-based transformations 1. Joins, aggregations, window functions 2. Semi-structured data (VARIANT, JSON, XML)
Topic 5: Data Modeling and Performance Optimization	- Performance tuning 1. Warehouse sizing and auto-suspend/auto-resume 2. Clustering and pruning techniques - Modeling approaches in Snowflake 1. Data normalization vs denormalization 2. Star and snowflake schemas

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q23-Q28):

NEW QUESTION # 23
In Snowsight, why is the utilization of different chart types crucial for effective data analysis and interpretation?

A. Different chart types offer diverse data representation.
B. Snowsight doesn't support multiple chart types.
C. It limits users' ability to visualize data accurately.
D. Multiple chart types in Snowsight hinder data interpretation.

Answer: A

Explanation:
Different chart types offer varied data representation, aiding analysis and interpretation in Snowsight.

NEW QUESTION # 24
You are performing an UPDATE operation on a large table 'CUSTOMER ORDERS with millions of rows. The update logic involves complex calculations based on data from another table 'PRODUCT PRICES. To minimize the impact on concurrent queries and ensure data consistency, which of the following strategies should you implement?

A. Create a new version of table with updated data and switch to new version.
B. Create a temporary table with the updated values, drop the original table, and rename the temporary table to the original table name.
C. Break the UPDATE operation into smaller batches using a WHERE clause with a limiting condition based on a primary key or date range and commit changes after each batch.
D. Execute the UPDATE statement directly without any special considerations, relying on Snowflake's default concurrency control.
E. Use a MERGE statement to update the 'CUSTOMER ORDERS' table based on the data from the 'PRODUCT PRICES' table.

Answer: C,E

Explanation:
Breaking the UPDATE into smaller batches minimizes lock contention and reduces the duration of the update transaction, thereby reducing the impact on concurrent queries. A MERGE statement is an efficient way to perform updates based on data from another table, optimizing the process compared to a simple UPDATE statement. Option A is not recommended for large tables due to potential lock contention. Option C is disruptive and might cause issues if concurrent queries are running against the table. Option E is not a standard approach.

NEW QUESTION # 25
You are tasked with creating a Snowsight dashboard to monitor the daily sales performance of an e-commerce platform. The sales data is stored in a table named 'SALES_DATR with columns 'SALE_DATE' (DATE), 'PRODUCT_ID' ONT), 'SALES AMOUNT' (FLOAT), and 'CUSTOMER REGION' (VARCHAR). You need to display a trendline chart showing the total sales amount for each day over the last 30 days, and also a table showing the top 5 regions by total sales amount. Which of the following steps are most efficient to achieve this in Snowsight? (Select TWO)

A. Use two separate worksheets in Snowsight. In the first worksheet, create the time series chart. In the second worksheet, create the region-based sales table. Then, embed one worksheet inside the other to create a combined dashboard.
B. Create a single tile with two tabs. The first tab displays the trendline chart using a time series and the second tab displays the region-based sales table. Both tabs use the same underlying SQL query but filter the results differently.
C. Use the 'Notebook' feature in Snowsight to first perform the aggregations required to obtain the data for the visualizations, and then create visualization tiles by referencing the calculated dataframes. No direct SQL is used in the tiles.
D. Create two separate tiles in the Snowsight dashboard. One tile should use a time series chart visualizing 'SELECT SALE_DATE, SUM(SALES_AMOUNT) FROM SALES_DATA WHERE SALE_DATE DATEADD(day, -30, GROUP BY SALE_DATE ORDER BY SALE_DATE;' and the other tile should use a table visualizing 'SELECT CUSTOMER REGION, SUM(SALES AMOUNT) FROM SALES DATA GROUP BY CUSTOMER REGION ORDER BY DESC LIMIT 5;'
E. Create a single tile using a combination chart in Snowsight. Use 'SALE DATE as the x-axis and 'SUM(SALES AMOUNT)' as the y-axis, specifying a trendline. Then, add a second series to display region-based sales in a separate panel below.

Answer: C,D

Explanation:
Option A is correct because it directly and efficiently creates the desired visualizations in Snowsight using separate tiles. Option C is also correct. Using Snowsight's Notebook feature allows for pre-aggregation, which can be useful in complex scenarios where directly visualizing from the raw data would impact dashboard performance. Options B, D, and E are either not directly supported or less efficient methods within the current Snowsight dashboarding capabilities.

NEW QUESTION # 26
A data pipeline is failing intermittently, with the error logs indicating 'Insufficient compute resources'. You are tasked with collecting data to diagnose the root cause. What combination of Snowflake features and data collection strategies would be MOST effective in identifying if warehouse auto-scaling or query performance is the primary contributor to the issue?

A. Create a new, larger warehouse and migrate the pipeline to it without analyzing the current warehouse's performance.
B. Increase the warehouse size significantly and observe if the pipeline failures cease.
C. Analyze the average query execution time for all queries run by the pipeline using the QUERY_HISTORY view and compare it to the warehouse's average execution time. Additionally, check for queries spilling to local disk (using WAREHOUSE LOAD) during failure periods.
D. Collect only error logs generated by the data pipeline to understand underlying problem.
E. Monitor the Snowflake warehouse resource monitor metrics (e.g., QUEUED LOAD BYTES, EXECUTION TIME) using the Snowflake web interface or view, focusing on periods coinciding with pipeline failures.

Answer: C,E

Explanation:
Options A and C provide a targeted approach. A allows tracking warehouse resource usage during the pipeline failures, identifying if the warehouse is reaching its limits. C helps identify slow-running queries contributing to resource exhaustion and detects queries using excessive local disk, indicating potential optimization opportunities. Increasing the warehouse size without analysis (B & D) is not cost-effective. Collecting only error logs (E) is not sufficient for a comprehensive diagnostic approach. It is too narrow and may not expose warehouse load issues and bottlenecks.

NEW QUESTION # 27
You're working with a Snowflake database containing sales transaction data'. The 'SALES' table includes columns 'transaction id' , product id', 'customer id', 'transaction_date', and 'sales amount'. The Bl team needs to analyze sales trends, identify top-selling products, and segment customers based on their purchase behavior. They also need to support ad-hoc queries with various filtering and aggregation criteria'. The data volume is significant, and query performance is a key concern. Consider the following Snowflake table definition (simplified):

Which of the following SQL queries, combined with appropriate data modeling techniques, will provide the BEST performance for analyzing monthly sales trends by product category, assuming a separate 'PRODUCTS table exists with 'product_id' and 'category' columns?

Answer: C

Explanation:
Option B provides the best performance because it uses a materialized view to pre- compute the monthly sales trends by product category. Subsequent queries to the materialized view will be significantly faster than re-calculating the aggregation each time. Option A performs the join and aggregation every time the query is executed. Option C uses a correlated subquery, which is generally less performant than a join. Option D uses , which might provide the same result but doesn't address the performance issue of recalculating the aggregation every time. Option E uses a temporary table which is unncessary because a materialized view can be created directly from the SALES and PRODUCTS table.

NEW QUESTION # 28
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