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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q13-Q18):

NEW QUESTION # 13
A retail company has data about their products, sales, and inventory. They need a dashboard to visualize key metrics, including total sales, average order value, inventory levels, and product performance across different regions. The data is stored in the following tables: 'PRODUCTS (PRODUCT ID, PRODUCT NAME, CATEGORY, PRICE) 'SALES' (SALE_ID, PRODUCT_ID, SALE_DATE, QUANTITY, REGION) 'INVENTORY (PRODUCT ID, REGION, QUANTITY ON_HAND) Which of the following strategies will result in an efficient dashboard that allows users to quickly filter and drill down into the data by region, product category, and time period while minimizing query execution time? (Select all that apply.)

A. Create a single, wide denormalized table containing all the necessary data from the 'PRODUCTS, 'SALES, and 'INVENTORY tables using JOINs. Build the dashboard directly on this table.
B. Implement dynamic data masking policies to filter out sensitive data from the base tables, ensuring data governance.
C. Utilize Snowflake's search optimization service on relevant columns (e.g., PRODUCT ID, REGION) in the base tables and use standard JOINs and aggregations within views used by the dashboard.
D. Create materialized views that pre-aggregate sales data by region, product category, and time period (e.g., daily, weekly, monthly). Join these materialized views with product and inventory data in the dashboard queries.
E. Create separate views for sales, inventory, and product information, then use the dashboard tool to join these views and perform aggregations.

Answer: C,D

Explanation:
Search optimization (C) can significantly speed up queries on large tables by creating a search index on frequently used filter columns. Materialized views (D) are also beneficial because they pre-aggregate the data, reducing the amount of computation required at query time. Creating a single, wide denormalized table (A) can lead to data redundancy and increased storage costs. Joining separate views in the dashboard tool (B) can be inefficient, as the joins are performed at query time. Data masking policies (E) are important for security but don't directly optimize query performance for dashboards.

NEW QUESTION # 14
You are tasked with creating a dashboard to monitor the performance of different marketing channels (e.g., email, social media, paid advertising). The data includes daily spend, impressions, clicks, and conversions for each channel. Which approach would BEST allow you to visualize the return on investment (ROI) for each channel over time, identify channels with diminishing returns, and enable stakeholders to easily compare channel performance?

A. Develop an interactive dashboard in Looker Studio, utilizing calculated fields to derive ROI for each channel (e.g., conversions / spend). Use a combination of line charts, bar charts (ROI per channel), and scatter plots (spend vs. conversions) with trendlines. Implement drill-down capabilities to view daily performance metrics.
B. Create separate line charts for each channel showing spend, impressions, clicks, and conversions over time, using a static reporting tool like SSRS.
C. Create a static report in Tableau using only aggregate measures to calculate the total ROI for each channel and display it in a table.
D. Use Snowflake's built-in charting capabilities to create a series of pie charts showing the percentage of total spend allocated to each channel.
E. Export the data to Excel and create a pivot table summarizing spend and conversions for each channel. Generate a simple bar chart showing total ROI for each channel.

Answer: A

Explanation:
Option B is the most suitable because it uses an interactive dashboard (Looker Studio) with calculated fields to derive ROI. The combination of line charts, bar charts, and scatter plots provides a comprehensive view of channel performance over time. Trendlines in the scatter plots help identify diminishing returns. Drill-down capabilities allow for detailed analysis. Option A creates separate charts, making comparison difficult. Option C is limited to summary data. Option D focuses on spend allocation, not ROI. Option E provides only a static view of total ROI.

NEW QUESTION # 15
When evaluating and selecting data for building dashboards, what factors should be considered for ensuring data relevance and usefulness? (Select all that apply)

A. Ignoring data complexities for simplicity in visualization
B. Filtering data based on irrelevant attributes
C. Including all available data for comprehensive visualization
D. Evaluating data based on business requirements

Answer: B,D

Explanation:
To ensure relevant and useful dashboards, data must be evaluated based on business requirements and filtered for irrelevant attributes.

NEW QUESTION # 16
You're designing a data pipeline in Snowflake to process order data'. The raw order data, including customer information, is stored in a JSON format within a single 'RAW ORDERS table. Due to privacy regulations, you need to mask the customer's email addresses before loading the data into a 'CLEANED ORDERS' table, while maintaining referential integrity. Furthermore, you want to track the data lineage (which raw order resulted in which cleaned order) in a separate 'ORDER LINEAGE' table. Which of the following approaches achieves these requirements effectively and efficiently? (Select TWO)

A. Implement a stored procedure that reads data from 'RAW ORDERS, masks the email using SHA256, inserts the cleaned data into 'CLEANED ORDERS', and simultaneously inserts lineage information into 'ORDER LINEAGE. Schedule this stored procedure to run periodically.
B. Create a masking policy on the 'email' column of the 'RAW_ORDERS' table. Then, create a task to copy all data from 'RAW_ORDERS to 'CLEANED_ORDERS. Finally, create a separate task that runs after the copying task to populate the 'ORDER_LINEAGE table with relevant mappings.
C. Use a Snowpipe to load data into 'RAW ORDERS. Create a stream on 'RAW ORDERS'. Create a task chained to the stream that reads the new records from the stream, masks the email, inserts the cleaned data into 'CLEANED_ORDERS' table and inserts lineage information into the 'ORDER _ LINEAGE table.
D. Create a view on the 'RAW ORDERS table that masks the email address. Load the raw order data into 'RAW ORDERS table using Snowpipe and a stream. Use a task chained to the stream to ingest the view into 'CLEANED_ORDERS and populate lineage information into 'ORDER_LINEAGE table.
E. Create a masking policy on the 'email' column in 'CLEANED ORDERS'. Create a stream on RAW ORDERS, then create a task to insert data from 'RAW ORDERS' into 'CLEANED ORDERS' and to INSERT the order to 'ORDER LINEAGE

Answer: A,C

Explanation:
Option B: A stored procedure provides the most control over the masking process and lineage tracking, ensuring data is masked during the transfer. It allows you to perform all operations (masking, inserting into 'CLEANED ORDERS, and inserting into 'ORDER LINEAGE) within a single transaction. Option C: Snowpipe loading data to RAW_ORDERS, using streams to capture the data, and scheduling the tasks to read from the stream, while masking and adding lineage information provides automation and efficiency. Option A is incorrect, as it copies all data using a task into 'CLEANED_ORDERS and then masks the email on RAW_ORDERS after data is already in 'CLEANED_ORDERS'. Option D is incorrect as masking policy is on rather than Option E requires a view to mask email address and is not an effective approach to apply a masking policy. Also, its not the right procedure to load view into a table.

NEW QUESTION # 17
Data clustering is an example of which type of data analysis technique?

A. Descriptive analysis
B. Exploratory analysis
C. Prescriptive analysis
D. Predictive analysis

Answer: D

NEW QUESTION # 18
......

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