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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Prepare and Present Data | 10–15% | - Align outputs with business requirements - Snowsight dashboards and sharing results - Data visualization and reporting |
| Topic 2: Use Built-in Functions and Create UDFs | 10–15% | - User-Defined Functions (UDFs) - Scalar, aggregate, table, system functions |
| Topic 3: Perform Simple Data Transformations for Analysis | 15–20% | - Handling NULLs and structuring datasets - Data cleansing, standardization, type conversion - Views, materialized views, CTEs |
| Topic 4: Perform Predictive Analysis | 5–10% | - Forecasting and predictive modeling - Using Snowflake ML and built-in analytics |
| Topic 5: Build and Troubleshoot Advanced SQL Queries | 20–25% | - Semi-structured data processing - Complex joins, subqueries, window functions - Query optimization and troubleshooting |
| Topic 6: Perform Descriptive and Diagnostic Analysis | 10–15% | - Exploratory and ad-hoc analysis - Anomaly detection and root cause analysis - Statistical summarization and trend analysis |
| 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 |
>> Exam Snowflake DAA-C01 Reference <<
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NEW QUESTION # 41
When maintaining reports and dashboards, why is it crucial to configure subscriptions and updates?
Answer: C
Explanation:
Subscriptions and updates ensure timely information delivery, a crucial aspect of maintaining reports and dashboards.
NEW QUESTION # 42
A large fact table is partitioned by and clustered by 'customer _ id'. The table has the following columns: 'customer_id', and 'transaction_amount'. You need to optimize queries that frequently filter on a specific range of 'transaction_date' and then aggregate by 'customer _ id'. Given the existing partitioning and clustering, which of the following strategies will BEST improve query performance related to partition pruning and clustering?
Answer: A
Explanation:
Option B is the best strategy. Creating a materialized view that pre-aggregates the data by and 'transaction_date' addresses both aspects:Partition pruning is naturally leveraged because the materialized view will store aggregated data, allowing queries filtering on 'transaction_date' to use partition pruning during refresh and query. Clustering helps because the data within each partition (date) is clustered by 'customer_id' , making aggregations by customer efficient. Option A might not provide sufficient performance improvement if the aggregation by customer is still slow. Option C will improve query performance marginally but is not a good option with partition pruning, because the data is already partition on date. Option D reclustering too frequently can be costly and may not always result in significant performance gains. Option E can be a costly operation and also data migration may be hectic. Thus the best is to have materialized view.
NEW QUESTION # 43
You have a Snowflake table 'order details' with columns 'order id', 'customer id', 'order date', and 'order amount'. You need to calculate the 3-month moving average of 'order_amount' for each customer, but only for those customers who have placed at least 5 orders. Which of the following SQL statements will correctly achieve this? (Assume the current date is '2024-01-01 ')
Answer: C
Explanation:
Option E is the correct and most clear solution. It calculates the 3-month moving average, filters customers who have placed at least 5 orders, and leverages the power and clarity of Snowflake syntax. The QUALIFY clause effectively filters for customers with at least 5 orders. The 'RANGE BETWEEN INTERVAL '3 MONTH' PRECEDING AND CURRENT ROW accurately calculates the moving average over a 3- month window based on A, B and C calculate a simple moving average of the last 3 rows regardless of date, while D is syntactically invalid as HAVING cannot be used with window function in this way.
NEW QUESTION # 44
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?
Answer: D
Explanation:
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.
NEW QUESTION # 45
You are responsible for maintaining a dashboard that visualizes sales data'. The dashboard relies on a Snowflake view named 'SALES SUMMARY. Users have reported slow refresh times, especially when filtering by date. The 'SALES SUMMARY view is defined as follows:
Which of the following actions could significantly improve the dashboard's performance when filtering by date, assuming 'SALES_TRANSACTIONS' table is large?
Answer: B
Explanation:
Clustering the 'SALES_TRANSACTIONS table by the DATE column is the most effective solution. Snowflake uses micro- partitioning, and clustering helps to logically group similar data together within micro-partitions. When filtering by DATE, Snowflake can then efficiently prune micro-partitions that do not contain relevant data, significantly reducing the amount of data scanned. Materialized views (A) can help but need to be maintained and add complexity. Search optimization on DATE can help but it's not the intended use case, as DATE is a range based column (B). Ordering the results (C) doesn't affect query performance significantly. Partitioning is not directly supported in Snowflake. Clustering is the correct approach.
NEW QUESTION # 46
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