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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Prepare and Load Data | 15–20% | - Data ingestion methods: COPY INTO, stages, Snowpipe - File formats: CSV, JSON, Parquet, Avro - External tables and data validation |
| Topic 2: Use Built-in Functions and Create UDFs | 10–15% | - Scalar, aggregate, table, system functions - User-Defined Functions (UDFs) |
| Topic 3: Perform Descriptive and Diagnostic Analysis | 10–15% | - Statistical summarization and trend analysis - Anomaly detection and root cause analysis - Exploratory and ad-hoc analysis |
| Topic 4: Perform Predictive Analysis | 5–10% | - Using Snowflake ML and built-in analytics - Forecasting and predictive modeling |
| Topic 5: Prepare and Present Data | 10–15% | - Align outputs with business requirements - Data visualization and reporting - Snowsight dashboards and sharing results |
| Topic 6: Perform Simple Data Transformations for Analysis | 15–20% | - Views, materialized views, CTEs - Handling NULLs and structuring datasets - Data cleansing, standardization, type conversion |
| Topic 7: 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 # 55
A Data Analyst needs to create a custom filter called state in a Snowflake dashboard using a SQL query.
Which query will include this custom filter?
Answer: A
Explanation:
In Snowsight, Snowflake's modern web interface, Filters are used to make dashboards and worksheets interactive. When a Data Analyst creates a custom filter, they define a "SQL Keyword" that acts as a variable within their SQL code. This allows users to select a value from a dropdown or date picker in the UI, which then dynamically updates the query's results.
The standard syntax for referencing a filter variable in a Snowflake worksheet or dashboard is the colon prefix (:) followed by the keyword name. In this specific scenario, the keyword is state. Therefore, the query must use :state to tell Snowflake that this value should be supplied by the dashboard's filter UI rather than being a literal string or a column reference.
Evaluating the Options:
* Option A is the 100% correct syntax. It correctly uses the :state identifier to link the query logic to the dashboard filter.
* Option B is incorrect because the @ symbol is typically used in Snowflake to reference stages (e.g.,
@mystage), not dashboard filters.
* Option C is incorrect because state = state is a tautology that will always evaluate to true for non-null values; it does not reference an external filter.
* Option D is incorrect because 'state' is a string literal. The query would literally look for a state named
"state" rather than using the user's selection.
Using these filters effectively is a core requirement for the Data Presentation and Data Visualization domain, as it enables the creation of self-service analytics tools where non-technical users can explore data subsets without modifying SQL code.
NEW QUESTION # 56
How does performing data discovery through querying tables in Snowflake aid in data preparation?
Answer: B
Explanation:
Querying tables in Snowflake aids in understanding necessary data transformations for effective preparation.
NEW QUESTION # 57
You have a Snowsight dashboard that visualizes daily sales trends. Business users complain that the dashboard takes too long to load, especially when filtering by specific product categories. The underlying data resides in a large table partitioned by 'sale date'. Which of the following actions would BEST improve the dashboard's performance, assuming the filters are appropriately configured in the dashboard and the virtual warehouse size is already appropriately sized?
Answer: C
Explanation:
Creating a materialized view pre-aggregates the data, significantly reducing query execution time. The materialized view stores the result of a query, and Snowflake automatically refreshes it when the underlying data changes. Since the product categories are used as filters, pre- aggregating along these dimensions directly addresses the slow loading times. Increasing warehouse size (B) only helps if the compute resources are a bottleneck, which might not be the primary issue. Converting to Streamlit (C) changes the presentation layer but doesn't inherently improve data retrieval. Query Acceleration (D) can help, but only if it properly sized and configured. Session level caching (E) might only benefit the same user, but if multiple users are accessing the same dashboard, the best way would be through pre-aggregated results in a materialized view.
NEW QUESTION # 58
A data analyst is tasked with identifying the top 3 performing sales representatives in each region based on their total sales amount. The sales data is stored in a table named 'sales data" with columns 'region', 'sales_rep', and 'sales_amount'. Which Snowflake SQL statement(s) would efficiently achieve this?
Answer: A,C,D
Explanation:
Options A, B, and C correctly utilize window functions to rank sales representatives within each region. assigns a unique rank, assigns the same rank to ties and skips the subsequent ranks, and 'DENSE RANK()' assigns the same rank to ties but does not skip ranks. All three can be used with a 'WHERE clause to filter for the top 3. Option D incorrectly uses 'LAG' and doesn't achieve the desired ranking. Option E uses 'NTILE' which divides the rows into three groups; filtering 'tile = 1' might not always return the top three based on sales.
NEW QUESTION # 59
In diagnostic analysis, what significance do demographics and relationships hold when identifying anomalies? (Select all that apply)
Answer: A,C
Explanation:
Identifying demographic variations and considering relationships are crucial in identifying anomalies during diagnostic analysis.
NEW QUESTION # 60
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