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| Section | Weight | Objectives |
|---|---|---|
| Perform Descriptive and Diagnostic Analysis | 10–15% | - Statistical summarization and trend analysis - Exploratory and ad-hoc analysis - Anomaly detection and root cause analysis |
| Prepare and Load Data | 15–20% | - External tables and data validation - Data ingestion methods: COPY INTO, stages, Snowpipe - File formats: CSV, JSON, Parquet, Avro |
| Prepare and Present Data | 10–15% | - Snowsight dashboards and sharing results - Data visualization and reporting - Align outputs with business requirements |
| Build and Troubleshoot Advanced SQL Queries | 20–25% | - Query optimization and troubleshooting - Complex joins, subqueries, window functions - Semi-structured data processing |
| Perform Predictive Analysis | 5–10% | - Forecasting and predictive modeling - Using Snowflake ML and built-in analytics |
| 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% | - Data cleansing, standardization, type conversion - Handling NULLs and structuring datasets - Views, materialized views, CTEs |
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問題 #10
Your organization stores clickstream data in Parquet files in an external stage 's3://your-bucket/clickstreamP. The data includes nested JSON structures representing user activity. You need to create a Snowflake table to query this data efficiently, extracting specific fields from the nested JSON. The challenge is to optimize query performance by leveraging Parquet's columnar storage and schema evolution capabilities. Which of the following approaches offers the BEST combination of performance and flexibility for querying the data in Snowflake, considering potential schema changes in the Parquet files over time?
答案：A
解題說明：
Materialized Views offer the best performance as they pre-compute and store the results, leveraging Snowflake's caching. They also adapt to schema changes in underlying Parquet files (within limits). External tables alone can be slow because of on-the-fly processing. Loading into VARIANT loses the advantage of Parquet's columnar structure. Predefined columns are rigid and don't handle schema evolution well. Creating standard views on external tables also does not provide the pre-computed benefits of the materialized views.
問題 #11
How do logging and monitoring solutions contribute to data processing solutions? (Select all that apply)
答案：B,D
解題說明：
Logging and monitoring solutions help respond to processing failures promptly and provide insights into processing status.
問題 #12
Given the following data:
This SELECT statement is executed:
What will be the result?
答案：C
解題說明：
To determine the correct result of the query, a Data Analyst must evaluate the nested functions according to standard SQL order of operations while correctly identifying how Snowflake handles NULL values in aggregate calculations.
Step 1: Evaluating AVG(GRADE)
The AVG() function calculates the arithmetic mean of the non-null values in a column. Based on the provided table data, the grades are: 4, 1, 2, 1, and 2. The sixth row contains a NULL grade. In Snowflake, as per ANSI SQL standards, aggregate functions ignore NULL values entirely. They are not treated as zeros, nor do they cause the function to return NULL (unless the entire column is null).
* Sum of non-null values: $4 + 1 + 2 + 1 + 2 = 10$
* Count of non-null values: $5$
* Result: $10 / 5 = 2.0$
Step 2: Evaluating CEIL(..., 2)
The CEIL (or CEILING) function is typically used to return the smallest integer value that is greater than or equal to the input. However, Snowflake's CEIL function also supports an optional second argument for scale (precision).
* The input is the result from the previous step: 2.0.
* The scale argument is 2.
* The function CEIL(2.0, 2) attempts to round the value up to the nearest value with two decimal places.
Since 2.0 is already a whole number and has no fractional component beyond the specified scale, the ceiling remains 2.0.
Evaluating the Options:
* Option A is incorrect as it represents a massive scaling error.
* Option C and D are incorrect because they result from mathematical errors or incorrectly including the NULL row in the denominator (e.g., $10 / 6 = 1.66$, then rounded up).
* Option B is the 100% correct answer. It accurately reflects the result of the AVG function ($2.0$) followed by the CEIL operation, which preserves the value as it is already at the target ceiling. This question tests precision in both mathematical logic and the technical nuances of Snowflake-specific SQL functions.
問題 #13
In Snowflake, how does the implementation of primary keys for tables contribute to data integrity?
答案：A
解題說明：
Primary keys enforce uniqueness in table rows, ensuring data integrity by preventing duplicate entries within a table.
問題 #14
You are analyzing sales data for a retail company. The 'sales' table contains columns 'product id' (INT), 'sale date' (DATE), and 'sale_amount' (NUMBER). You need to calculate the percentage contribution of each product's sales to the total sales on each day. You want the result to include 'sale date', 'product_id', 'sale_amount', 'daily total' , and 'percentage_contribution'. Which of the following Snowflake queries achieves this correctly?
答案：B,E
解題說明：
Options A and D are correct. Option A uses a window function OVER (PARTITION BY to calculate the total sales for each day. This is efficient because it calculates the total sales for each day in a single pass. Option D uses a subquery to calculate the total sales for each day and then joins this result back to the original table. Option B calculates a single total over the entire table, not by day. Option C calculates a single total sales amount over the entire table and assigns it to every row, which is incorrect. Option E orders the sales amount by the date but doesnt correctly partition by 'sale_date' leading to cumulative sum instead.
問題 #15
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