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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Perform Simple Data Transformations for Analysis | 15–20% | - Handling NULLs and structuring datasets - Views, materialized views, CTEs - Data cleansing, standardization, type conversion |
| Topic 2: Perform Predictive Analysis | 5–10% | - Using Snowflake ML and built-in analytics - Forecasting and predictive modeling |
| 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: Build and Troubleshoot Advanced SQL Queries | 20–25% | - Complex joins, subqueries, window functions - Semi-structured data processing - Query optimization and troubleshooting |
| Topic 5: Prepare and Present Data | 10–15% | - Snowsight dashboards and sharing results - Align outputs with business requirements - Data visualization and reporting |
| Topic 6: Use Built-in Functions and Create UDFs | 10–15% | - Scalar, aggregate, table, system functions - User-Defined Functions (UDFs) |
| Topic 7: Prepare and Load Data | 15–20% | - File formats: CSV, JSON, Parquet, Avro - External tables and data validation - Data ingestion methods: COPY INTO, stages, Snowpipe |
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질문 # 39
You are tasked with improving the performance of queries against a very large table named 'SALES DATA, which contains sales transactions from multiple regions. The table is frequently queried based on 'REGION' , 'SALE DATE, and 'PRODUCT CATEGORY. The data in 'SALE DATE' ranges from 2020 to the present. You want to optimize query performance by leveraging Snowflake's clustering capabilities, but you also want to minimize the impact of data ingestion and maintenance. Considering the characteristics of the data and query patterns, which of the following strategies would be the most effective and cost-efficient approach for clustering 'SALES DATA'?
정답：B
설명：
Option A, clustering by 'REGION', 'SALE DATE', and 'PRODUCT CATEGORY' in that order is the most effective approach. Given the frequent filtering on these three columns, clustering on them ensures that Snowflake can efficiently prune the data and minimize the amount of data scanned during query execution. By monitoring clustering depth and rebuilding when it exceeds a threshold, you can maintain optimal performance over time as new data is ingested. Option B is not ideal because SALE_DATE alone might not be sufficient for all query patterns. Option C is not ideal because Snowflake does not automatically optimize 'PRODUCT_CATEGORY only because of stats. Option D, Materialized view might not be good because they are usually pre aggregated and in this scenario are required raw data. E is invalid because partition does not exist in snowflake.
질문 # 40
When cleaning data, what role does using clones play in specific use-cases?
정답：D
설명：
Clones are beneficial in isolating and resolving data anomalies without impacting the original dataset, facilitating safe data cleaning practices.
질문 # 41
This command was executed:
SQL
SELECT seq4(), uniform(1, 10, RANDOM(12))
FROM TABLE(GENERATOR(TIMELIMIT => NULL))
ORDER BY 1;
How many rows will be generated?
정답：B
설명：
The GENERATOR table function is used in Snowflake to produce a synthetic result set, typically for testing or creating dummy data. However, the GENERATOR function requires at least one of two parameters to be explicitly set to a positive value to produce any rows: ROWCOUNT or TIMELIMIT.
In the provided query, the user has set TIMELIMIT => NULL. In Snowflake's SQL implementation for the GENERATOR function, NULL is treated as a zero or an undefined limit. Furthermore, since the ROWCOUNT parameter is omitted, it defaults to zero. Because neither a positive duration nor a positive row count has been specified, the generator engine has no instruction on how many rows to build.
Evaluating the Results:
* Option A is a distractor; while one might think NULL means "no limit" (infinity), the GENERATOR function is designed to be finite and safe.
* Option B is incorrect; the number 12 inside the RANDOM(12) function is merely a seed value for the random number generator, ensuring the output is reproducible. It has no impact on the number of rows produced by the FROM clause.
* Option C is incorrect; 10 is just a boundary for the UNIFORM function.
* Option D is the 100% correct answer. Because the query specifies TIMELIMIT => NULL and fails to provide a ROWCOUNT, Snowflake will execute the query successfully but return a result set containing 0 rows. This is a common "trick" question in the Data Analysis domain to test an analyst's familiarity with table function parameters.
질문 # 42
When customizing data presentations in dashboards using filtering and editing techniques, what advantages do these methods offer? (Select all that apply)
정답：A,D
설명：
Customizing through filtering and editing enhances data accuracy and relevancy in dashboards.
질문 # 43
You have identified inconsistencies in the data type of the 'ORDER DATE' column across several tables within your Snowflake database. Some tables store it as DATE, while others store it as VARCHAR. You need to create a unified view that presents 'ORDER DATE' consistently as DATE, handling potential conversion errors gracefully. You have to use safe aggregate operations. Which of the following approaches provides the most robust and error-tolerant solution?
정답：D
설명：
The most robust and error-tolerant solution is option E, using 'TRY CAST(ORDER DATE AS DATE)'. This function attempts to convert the 'ORDER_DATE' to a DATE data type, and if the conversion fails (e.g., the VARCHAR value cannot be parsed as a date), it returns NULL without raising an error. This ensures that the view creation and queries against it will not fail due to data type conversion issues. Option A, , is an older function, and ' TRY_CAST' is the preferred, more general function. Option B, 'IS_DATE is not a valid snowflake Function. Option C, ' TO DATE will fail the query if the data in column is not a valid date. Option D 'SAFE_CAST is not a valid snowflake function.
질문 # 44
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