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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Perform Simple Data Transformations for Analysis | 15–20% | - Data cleansing, standardization, type conversion - Handling NULLs and structuring datasets - Views, materialized views, CTEs |
| Topic 2: Build and Troubleshoot Advanced SQL Queries | 20–25% | - Complex joins, subqueries, window functions - Query optimization and troubleshooting - Semi-structured data processing |
| Topic 3: Prepare and Present Data | 10–15% | - Align outputs with business requirements - Data visualization and reporting - Snowsight dashboards and sharing results |
| Topic 4: Prepare and Load Data | 15–20% | - File formats: CSV, JSON, Parquet, Avro - External tables and data validation - Data ingestion methods: COPY INTO, stages, Snowpipe |
| Topic 5: Use Built-in Functions and Create UDFs | 10–15% | - User-Defined Functions (UDFs) - Scalar, aggregate, table, system functions |
| Topic 6: Perform Predictive Analysis | 5–10% | - Using Snowflake ML and built-in analytics - Forecasting and predictive modeling |
| 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 # 17
You are tasked with creating a dashboard to visualize sales performance across different product categories and regions. The data is stored in a Snowflake table named with columns: 'SALE DATE (DATE), 'PRODUCT CATEGORY (VARCHAR), 'REGION' (VARCHAR), 'SALES_AMOUNT (NUMBER). The business stakeholders want to see a trend of monthly sales for the past year, a breakdown of sales by region, and a comparison of sales between product categories. Which of the following approaches would be MOST effective and efficient in Snowflake for generating the data needed for these visualizations, considering the need for dashboard responsiveness and minimal query cost?
Answer: C,E
Explanation:
Creating multiple materialized views (B) allows for pre-calculated aggregations tailored to each visualization, significantly improving dashboard responsiveness and reducing query costs. Using tasks and streams to transform the raw data into optimized summary tables (D) also pre-processes the data, providing similar benefits to materialized views, but allows for more complex transformations and incremental updates. A single complex query (A) can be slow and resource-intensive. Relying solely on caching (C) might not be sufficient for complex aggregations or large datasets. Exporting data to a completely new system adds overhead and is unnecessary given Snowflake's capabilities.
NEW QUESTION # 18
Consider the following Snowflake table schema and data: 'CREATE TABLE products (product_id INTEGER, product_name VARCHAR, properties VARIANT);' Data: 'INSERT INTO products VALUES (1, 'Laptop', "silver", "storage": "512GB", "price": 1200.00}'));' 'INSERT INTO products VALUES (2, 'Mouse', "wireless", "dpi": 1600, "price": 25.00}'));' 'INSERT INTO products VALUES (3, 'Keyboard', PARSE JSON('{"layout": "US", "backlit": true, "price": Which of the following SQL queries will return the 'product_name' and 'price' for all products where the 'price' is greater than 50, ensuring that the 'price' is treated as a numeric value for comparison? Select all that apply
Answer: C,E
Explanation:
Options B and E are correct. Option B explicitly casts 'properties:price' to a 'NUMBER data type before the comparison, ensuring that the comparison is performed numerically. Option E casts 'properties:price' to a data type and uses TRY_TO_NUMBER to handle potential errors gracefully. Option A is incorrect because Snowflake treats the value extracted from the VARIANT as a string and the string comparison will lead to incorrect results. Options C and D don't work without casting to VARCHAR
NEW QUESTION # 19
You are analyzing sales data in Snowflake to identify seasonal trends and patterns. You have a table 'SALES DATA with columns 'SALE DATE (DATE) and 'SALE_AMOUNT (NUMBER). Which of the following SQL queries and visualization techniques would be MOST effective in identifying and visualizing these seasonal trends? Assume the data spans several years.
Answer: E
Explanation:
Option C is the most effective because it combines weekly sales aggregation with a box plot analysis of monthly sales across multiple years. The weekly aggregation provides a granular view of sales trends, while the box plot effectively visualizes the distribution of sales for each month, allowing for easy identification of monthly seasonal patterns and outliers. Option A only shows monthly sales volume, not the distribution of sales within each month across years. Option B shows the yearly trend, not seasonal variations. Option D doesn't aggregate the data and hence can't show you the seasonality. Option E only shows the daily variance across weeks.
NEW QUESTION # 20
Which of the following statements regarding Secure Views and Materialized Views in Snowflake are CORRECT? (Choose two)
Answer: C,E
Explanation:
Secure Views hide query logic and data lineage, improving security. Materialized Views pre-compute and store data, improving performance. Secure Views can have a performance overhead as additional checks are performed. Materialized Views are automatically refreshed in Snowflake. Materialized views cannot be created with User-Defined Functions in the Select List or Where Clause. Row access policies are not supported on materialized views.
NEW QUESTION # 21
Consider the following SQL query:
Analyzing the Query Profile, you observe that the 'WHERE' clause is not effectively filtering the data'. Which of the following actions could improve performance, assuming the 'order date' column is NOT currently clustered or indexed?
Answer: B,C
Explanation:
Clustering on 'order_date' will physically organize the data on disk according to the date, allowing Snowflake to efficiently retrieve the relevant data based on the 'WHERE clause. Creating a materialized view pre-computes the result set based on the filter, so the query would only need to retrieve the top 10 from that smaller dataset. Secondary indexes are not supported in Snowflake. Partitioning is not a feature in Snowflake. Increasing the warehouse size might help with processing power, but it won't directly improve data access efficiency.
NEW QUESTION # 22
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