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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Perform Predictive Analysis | 5–10% | - Forecasting and predictive modeling - Using Snowflake ML and built-in analytics |
| Topic 2: Perform Simple Data Transformations for Analysis | 15–20% | - Handling NULLs and structuring datasets - Views, materialized views, CTEs - Data cleansing, standardization, type conversion |
| Topic 3: Prepare and Load Data | 15–20% | - External tables and data validation - Data ingestion methods: COPY INTO, stages, Snowpipe - File formats: CSV, JSON, Parquet, Avro |
| Topic 4: Use Built-in Functions and Create UDFs | 10–15% | - User-Defined Functions (UDFs) - Scalar, aggregate, table, system functions |
| Topic 5: Build and Troubleshoot Advanced SQL Queries | 20–25% | - Complex joins, subqueries, window functions - Semi-structured data processing - Query optimization and troubleshooting |
| Topic 6: Prepare and Present Data | 10–15% | - Align outputs with business requirements - Snowsight dashboards and sharing results - Data visualization and reporting |
| Topic 7: Perform Descriptive and Diagnostic Analysis | 10–15% | - Statistical summarization and trend analysis - Exploratory and ad-hoc analysis - Anomaly detection and root cause analysis |
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NEW QUESTION # 47
What complexities might arise when identifying and resolving data import errors in Snowflake?
(Select all that apply)
Answer: A,C,D
Explanation:
Identifying and resolving data import errors involves challenges related to identifying error sources, resolving inconsistencies, and analyzing logs for resolution, which might be complex depending on the nature of the errors.
NEW QUESTION # 48
You have a table named 'product_sales' with columns 'product_id', 'sale date' , and 'sales_amount'. You need to calculate a 7-day moving average of the sales amount for each product. However, you also need to handle cases where there might be missing 'sale_date' entries for a product. How can you best achieve this using Snowflake window functions and without creating temporary tables or stored procedures?
Answer: B
Explanation:
Option B is the most appropriate. using 'RANGE BETWEEN INTERVAL '6 day' PRECEDING AND CURRENT ROW' will calculate the average of sales amounts for the previous 6 days, regardless of whether there are missing days in the 'sale_date' column. This provides a true 7-day moving average. 'ROWS BETWEEN' (Option A) would count rows, not days, potentially including data from more than 7 days if there are multiple sales on a single day, or less than 7 days if there are missing days. 'EXCLUDE CURRENT ROW' (Option C) would exclude the current row's sales amount from the average. 'RANGE BETWEEN 6 PRECEDING AND CURRENT ROW' (Option D) would not be valid Snowflake syntax for dates. Option E will calculate cumulative average, also that WHERE clause will lead to error.
NEW QUESTION # 49
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?
Answer: C
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 # 50
Your company is using Snowflake to store customer transaction data'. You want to enrich this data with demographic information from a Snowflake Marketplace data provider. The provider offers a secure data share with a view called 'CUSTOMER DEMOGRAPHICS. You need to join the customer transaction data in your 'TRANSACTIONS' table with the demographic data from the 'CUSTOMER DEMOGRAPHICS' view. Which of the following SQL queries is the MOST efficient and secure way to achieve this, assuming you have already created a database from the share?
Answer: B
Explanation:
Option B is the most efficient and secure because it explicitly uses an INNER JOIN, ensuring that only matching records between the TRANSACTION table and the Customer Demographics view are included. Using INNER JOIN improves performance compared to implicit joins (Option A). Options C and E uses LEFT and FULL OUTER joins which might result in unnecessary nulls and impacting the perfromance. Option D will not work because the schema name is missing.
NEW QUESTION # 51
In Snowsight, what is the significance of creating diverse chart types (e.g., bar charts, scatter plots, heat grids) for data visualization?
Answer: A
Explanation:
Diverse chart types offer varied data representation, facilitating better analysis in Snowsight.
NEW QUESTION # 52
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