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| Section | Weight | Objectives |
|---|---|---|
| Prepare and Load Data | 15–20% | - External tables and data validation - File formats: CSV, JSON, Parquet, Avro - Data ingestion methods: COPY INTO, stages, Snowpipe |
| Prepare and Present Data | 10–15% | - Snowsight dashboards and sharing results - Data visualization and reporting - Align outputs with business requirements |
| Perform Predictive Analysis | 5–10% | - Forecasting and predictive modeling - Using Snowflake ML and built-in analytics |
| Perform Descriptive and Diagnostic Analysis | 10–15% | - Statistical summarization and trend analysis - Exploratory and ad-hoc analysis - Anomaly detection and root cause analysis |
| Use Built-in Functions and Create UDFs | 10–15% | - Scalar, aggregate, table, system functions - User-Defined Functions (UDFs) |
| Perform Simple Data Transformations for Analysis | 15–20% | - Handling NULLs and structuring datasets - Views, materialized views, CTEs - Data cleansing, standardization, type conversion |
| Build and Troubleshoot Advanced SQL Queries | 20–25% | - Semi-structured data processing - Complex joins, subqueries, window functions - Query optimization and troubleshooting |
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NEW QUESTION # 29
How does operationalizing data contribute to maintaining reports and dashboards for business requirements?
Answer: C
Explanation:
Operationalizing data ensures consistent and efficient usage of reports and dashboards.
NEW QUESTION # 30
A data analyst is performing an exploratory analysis on sales data and observes a highly skewed distribution for the 'sales_amount' column. Which of the following data transformation techniques are appropriate to mitigate the impact of the skewness and make the data more suitable for modeling and analysis? (Select all that apply)
Answer: B,C,E
Explanation:
Log transformation and Box-Cox transformation are common techniques used to reduce skewness in data. Winsorization helps to reduce the impact of outliers, which can contribute to skewness. Standardization and Min-Max scaling are primarily used for feature scaling and do not directly address skewness.
NEW QUESTION # 31
A Data Analyst wants to transform query results. Which transformation option will incur compute costs?
Answer: C
Explanation:
In the Snowflake Snowsight interface, it is critical to distinguish between UI-level formatting and engine- level processing. Snowsight provides several client-side features that allow an analyst to change how data is displayed without re-executing the underlying SQL query or utilizing virtual warehouse credits.
Client-Side (No Compute Cost):
Formatting options such as adding thousand separators (Option A), adjusting the visible decimal precision (Option C), or changing the display format of dates and timestamps (Option D) are typically handled by the Snowsight web interface itself. These transformations are applied to the data that has already been retrieved into the browser's local result cache. Because they do not require the virtual warehouse to scan micro- partitions or perform new calculations, they do not incur additional compute costs.
Engine-Level (Incurs Compute Cost):
Sorting a column (Option B) is fundamentally different. While Snowsight allows you to click a column header to sort, this action frequently triggers a re-query or a secondary processing step if the entire result set is not already fully cached in the browser's memory. When you use "column options" to perform operations like sorting, filtering, or grouping on large datasets, Snowflake often has to leverage the virtual warehouse to reorganize the data. In the context of the Snowflake Data Analyst exam, sorting is identified as a transformation that requires active compute resources because the engine must evaluate the entire dataset to determine the new order of records.
Furthermore, even if a small result set is cached, complex sorting across large volumes of data necessitates warehouse involvement to ensure accuracy and handle "spilling" to local or remote storage if the sort operation exceeds available memory. Therefore, while visual "masks" are free, structural data reorganization like sorting is a compute-intensive task.
NEW QUESTION # 32
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: D
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 # 33
You are building a real-time dashboard to monitor website traffic and user behavior for an e-commerce company. The data includes page views, clicks, add-to-carts, and purchases, streamed continuously into Snowflake. You need to visualize the conversion funnel (page views -> clicks -> add-to-carts -> purchases) in real-time and identify drop-off points. Given the following table schema: "'sql CREATE OR REPLACE TABLE website_events ( event_timestamp TIMESTAMP NTZ, event_type VARCHAR(50), user_id VARCHAR(IOO), page_url VARCHAR(255) ); Which approach, including code snippets, would be the MOST efficient and scalable way to achieve this real-time conversion funnel visualization, taking into account the high volume of streaming data?
Answer: E
Explanation:
Option C is the most efficient and scalable. A Snowflake Stream allows you to track changes to the 'website_events' table in real- time. A Snowpipe enables continuous data ingestion. A materialized view pre-calculates the conversion funnel metrics, significantly improving query performance compared to querying the base table directly, especially with high data volumes. Connecting a real-time dashboarding tool to the materialized view provides a real-time view of the funnel. Option A involves periodic querying, which is less real-time and less efficient. Option B suggests direct connection with a BI tool without pre-aggregating the Data, resulting into dashboard performance issue. Option D introduces unnecessary complexity with external message queues and stream processing frameworks. Exporting data to Python dataframe is not scalable for large data volumes.
NEW QUESTION # 34
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