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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q114-Q119):

NEW QUESTION # 114
A financial institution stores transaction data with various attributes, including transaction ID, account ID, transaction type, amount, and timestamp. They need to build a dashboard to monitor transaction volumes and identify unusual patterns in real-time. The data is streaming continuously. Consider these requirements: near real-time analysis, complex pattern recognition, and the need to store the raw transaction data for auditing. Which of the following approaches is optimal, considering the need for both speed and historical data integrity?

A. Use Snowflake Streams and Tasks to incrementally update a star schema data model in near real-time, with separate tables for transactions, accounts, and transaction types. Store raw data in a separate, immutable table.
B. Use Snowflake's Data Marketplace to access pre-aggregated financial data, avoiding the need for any data modeling or transformation.
C. Directly query the raw streaming data using Snowflake's external tables, without any data transformation or modeling. Rely on Snowflake's query optimization for real-time analysis.
D. Create a single denormalized table, indexed on transaction ID and timestamp. Use this table for both real-time analysis and historical storage.
E. Create a highly flattened table and use Snowflake's materialized views to aggregate data in near real-time. Store raw data in the same flattened table.

Answer: A

Explanation:
Option B is the most suitable solution. Snowflake Streams and Tasks provide a mechanism for incrementally updating a data model (in this case, a star schema) based on changes in the source data. This enables near real-time updates to aggregated tables used for the dashboard. Storing the raw data in a separate, immutable table ensures data integrity for auditing purposes. Option A might work, but managing large flattened tables with constant updates can be challenging. Option C is not efficient for complex analysis. Option D lacks the structure needed for complex joins and aggregations. Option E is irrelevant as it proposes using external data instead of processing the institution's own data.

NEW QUESTION # 115
When performing forecasting, which factors are essential for accurate predictions? (Select all that apply)

A. Relying solely on historical data without considering external influences
B. Examining trends and anomalies in historical data
C. Incorporating statistical methods for prediction accuracy
D. Using basic arithmetic functions exclusively for forecasting

Answer: B,C

Explanation:
Incorporating statistical methods and considering trends/anomalies are crucial for accurate predictions in forecasting.

NEW QUESTION # 116
You have identified inconsistencies in the data type of the 'ORDER DATE' column across several tables within your Snowflake database. Some tables store it as DATE, while others store it as VARCHAR. You need to create a unified view that presents 'ORDER DATE' consistently as DATE, handling potential conversion errors gracefully. You have to use safe aggregate operations. Which of the following approaches provides the most robust and error-tolerant solution?

Answer: B

Explanation:
The most robust and error-tolerant solution is option E, using 'TRY CAST(ORDER DATE AS DATE)'. This function attempts to convert the 'ORDER_DATE' to a DATE data type, and if the conversion fails (e.g., the VARCHAR value cannot be parsed as a date), it returns NULL without raising an error. This ensures that the view creation and queries against it will not fail due to data type conversion issues. Option A, , is an older function, and ' TRY_CAST' is the preferred, more general function. Option B, 'IS_DATE is not a valid snowflake Function. Option C, ' TO DATE will fail the query if the data in column is not a valid date. Option D 'SAFE_CAST is not a valid snowflake function.

NEW QUESTION # 117
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 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.
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 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.
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 stacked bar chart for sales by category, a line chart for overall sales trend over time, and a pie chart for regional sales distribution.

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 # 118
You have identified a potentially valuable dataset on the Snowflake Marketplace that provides demographic information by zip code. Your company has customer data in a table 'CUSTOMERS' with columns 'CUSTOMER ID' ONT), 'ZIP CODE' (VARCHAR), and 'PURCHASE AMOUNT (NUMBER). Before subscribing to the Marketplace data, you want to preview the data to ensure it contains the necessary information and assess its quality. What is the recommended and MOST efficient way to preview the data from the Snowflake Marketplace listing?

A. Contact the data provider directly and request a sample dataset to be delivered to you as a CSV file. Then upload this CSV file to an internal stage and query it to preview the data.
B. Create a stored procedure that dynamically calls the Marketplace data listing API to fetch a JSON sample and parse the JSON data into a temporary table for previewing.
C. Subscribe to the free trial of the Snowflake Marketplace listing, create a database from the shared data, and then sample the demographic data using 'SELECT FROM .. LIMIT 100'.
D. Use the 'Get Data' button on the Marketplace listing and specify that you only want a small subset of the data, such as data for a specific geographic region. This will create a smaller, more manageable dataset for previewing.
E. Utilize the 'Preview Data' tab available directly within the Snowflake Marketplace listing. This tab typically provides a sample of the data, allowing you to inspect the data structure, content, and quality before subscribing.

Answer: E

Explanation:
Option D is the most recommended and efficient way. The 'Preview Data' tab within the Snowflake Marketplace listing is designed precisely for this purpose. It allows you to inspect a sample of the data directly within the Marketplace interface, without requiring you to subscribe or incur costs. Option A requires you to subscribe to the listing, even if it's a free trial, which is an unnecessary step for simply previewing the data. Option B is inefficient and requires manual effort to obtain and load the data. Option C is not a standard feature of the Snowflake Marketplace. Option E is overly complex and inefficient for a simple preview task.

NEW QUESTION # 119
......

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DAA-C01 Training Courses & Excellect DAA-C01 Pass Rate

Excellect DAA-C01 Pass Rate - Certification DAA-C01 Questions

Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q114-Q119):

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