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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Perform Simple Data Transformations for Analysis | 15–20% | - Views, materialized views, CTEs - Data cleansing, standardization, type conversion - Handling NULLs and structuring datasets |
| Topic 2: Build and Troubleshoot Advanced SQL Queries | 20–25% | - Query optimization and troubleshooting - Semi-structured data processing - Complex joins, subqueries, window functions |
| Topic 3: Perform Descriptive and Diagnostic Analysis | 10–15% | - Exploratory and ad-hoc analysis - Statistical summarization and trend analysis - Anomaly detection and root cause 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% | - Snowsight dashboards and sharing results - Data visualization and reporting - Align outputs with business requirements |
| Topic 6: Use Built-in Functions and Create UDFs | 10–15% | - User-Defined Functions (UDFs) - Scalar, aggregate, table, system functions |
| Topic 7: Prepare and Load Data | 15–20% | - Data ingestion methods: COPY INTO, stages, Snowpipe - File formats: CSV, JSON, Parquet, Avro - External tables and data validation |
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NEW QUESTION # 60
You are tasked with cleaning a 'customer_orders' table in Snowflake. The table contains columns like 'order_id', 'customer_id', 'order_date', and 'order_amount'. You notice that some 'order_amount' values are negative (representing returns), but you need to analyze total sales. Additionally, some 'order_date' values are in the future. Which of the following SQL transformations would BEST address these data quality issues to ensure accurate sales analysis?
Answer: D
Explanation:
Option C correctly addresses both issues. ensures that all amounts are positive, effectively treating returns as positive contributions to sales for the purpose of this specific analysis. 'order_date <= filters out future dates. Options A and D also exclude negative order amounts which is incorrect, whereas options B and E do not make all amounts positive for sales amount. The use of CTE is also correct. The question is testing the candidate on how to select the best transformations for given data challenges.
NEW QUESTION # 61
You're tasked with building a data model in Snowflake for a retail company. The company has data on products ('PRODUCTS), sales transactions ('SALES), and customer demographics ('CUSTOMERS). You need to design a star schema to support efficient analysis of sales performance by product category and customer segment. Which of the following statements accurately describes the recommended table design and relationships within the star schema for this scenario?
Answer: C,D
Explanation:
Option B accurately describes a star schema design with a central fact table (SALES FACT) and dimension tables for products and customers. The fact table contains the numerical measures, and the dimension tables provide the context for analysis. Option D correctly describes Snowflake design, which is an extension of Star schema. Option A is generally not recommended because it leads to data redundancy and potential inconsistencies. Option C is not scalable or maintainable as the product range expands. Option E would result in slower query performance compared to a star schema design.
NEW QUESTION # 62
A retail company is using Snowflake to store their daily sales data'. They want to predict sales for the next week, accounting for weekly seasonality and a promotional campaign running on specific days. The sales data is in a table called 'SALES DATA' with columns 'SALE DATE' (DATE) and 'SALES AMOUNT' (NUMBER). Which of the following SQL statements is the MOST efficient and accurate way to achieve this using Snowflake's forecasting features, assuming a confidence interval of 95%?
Answer: E
Explanation:
Option E is the most correct. Option A uses 'timeLimit' in generator which needs additional conditions to be satisfied. Option E utilizes 'TIMESTAMP_INPUT = TRUE to correctly interpret the 'SALE_DATE column. It accurately specifies the forecast horizon (7 days) and confidence interval (0.95), and generates the future dates for forecasting using 'TABLE(GENERATOR(rowcount => 7))' to ensure the generation of 7 days for forecasting. Option D has OVER (ORDER BY NULL)' which is generally less performant than seq4().
NEW QUESTION # 63
This query is run:
SQL
SELECT
customer.id,
ANY_VALUE(customer.name),
SUM(orders.value)
FROM customer
JOIN orders ON customer.id = orders.customer_id
GROUP BY customer.id;
What is the effect of ANY_VALUE in this syntax?
Answer: A
Explanation:
The ANY_VALUE function is an aggregate function used in Snowflake to bypass the requirement that all non-aggregated columns in a SELECT list must appear in the GROUP BY clause. In the provided query, the data is grouped by customer.id. Standard SQL would require customer.name to also be in the GROUP BY clause, even if every ID only has one name associated with it.
By using ANY_VALUE(customer.name), the analyst tells Snowflake to simply pick a value from the group.
The core characteristic of this function is that it is non-deterministic. This means that if there are multiple different names for a single customer.id, Snowflake does not guarantee which one will be returned; it returns
"any" value it finds most efficient to retrieve during processing. In most data modeling scenarios where a 1:1 relationship exists between an ID and a Name, ANY_VALUE is a performance-optimized alternative to using MIN() or MAX(), as it requires less computational overhead to identify a single representative value.
Evaluating the Options:
* Option A is incorrect because ANY_VALUE only returns NULL if all values in the group are NULL.
* Option C is incorrect because it describes the MIN() function, which is deterministic.
* Option D is incorrect as it describes a median calculation (like MEDIAN()), which is a specific mathematical operation, not a "pick any" operation.
* Option B is the 100% correct answer. It accurately defines the function's purpose: returning an arbitrary value from the group, acknowledging that the specific result is non-deterministic.
NEW QUESTION # 64
In Snowsight, what is the significance of creating diverse chart types (e.g., bar charts, scatter plots, heat grids) for data visualization?
Answer: D
Explanation:
Diverse chart types offer varied data representation, facilitating better analysis in Snowsight.
NEW QUESTION # 65
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