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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Perform Simple Data Transformations for Analysis | 15–20% | - Views, materialized views, CTEs - Handling NULLs and structuring datasets - Data cleansing, standardization, type conversion |
| Topic 2: Prepare and Present Data | 10–15% | - Snowsight dashboards and sharing results - Align outputs with business requirements - Data visualization and reporting |
| Topic 3: Use Built-in Functions and Create UDFs | 10–15% | - Scalar, aggregate, table, system functions - User-Defined Functions (UDFs) |
| Topic 4: Prepare and Load Data | 15–20% | - Data ingestion methods: COPY INTO, stages, Snowpipe - External tables and data validation - File formats: CSV, JSON, Parquet, Avro |
| Topic 5: Perform Descriptive and Diagnostic Analysis | 10–15% | - Exploratory and ad-hoc analysis - Statistical summarization and trend analysis - Anomaly detection and root cause analysis |
| Topic 6: Perform Predictive Analysis | 5–10% | - Using Snowflake ML and built-in analytics - Forecasting and predictive modeling |
| Topic 7: Build and Troubleshoot Advanced SQL Queries | 20–25% | - Semi-structured data processing - Query optimization and troubleshooting - Complex joins, subqueries, window functions |
1. A Data Analyst needs to generate a graphic that will identify any natural clusters in a data set containing details about completed orders. Which Snowsight chart should be used?
A) Heat grid
B) Line chart
C) Scatter plot
D) Bar chart
2. What will the following query return?
SELECT * FROM testtable SAMPLE BLOCK (0.012) REPEATABLE (99992);
A) A sample of a table in which each block of rows has a 1.2% probability of being included in the sample where repeated elements are allowed.
B) A sample of a table in which each block of rows has a 0.012% probability of being included in the sample, with the seed set to 99992.
C) A sample containing 99992 records of a table in which each block of rows has a 0.012% probability of being included in the sample.
D) A sample of a table in which each block of rows has a 1.2% probability of being included in the sample, with the seed set to 99992.
3. A Data Analyst creates and populates the following table:
create or replace table aggr(v int) as select * from values (1), (2), (3), (4); The Analyst then executes this query:
select percentile_disc(0.60) within group (order by v desc) from aggr;
What will be the result?
A) 3
B) 2
C) 4
D) 1
4. A Data Analyst needs to write a query that will return all projects from a project table and all employees from an employee table. What type of join should be used in this query?
A) Cross join
B) Full outer join
C) Left outer join
D) Inner join
5. There are two similarly-structured and sized tables, Table_a and Table_b, in a schema with data populated in both tables. A Data Analyst is running queries as part of a preliminary analysis of the data to check the MAX value of a numeric column named num which is present in both the tables:
* Query 1: SELECT MAX(num) FROM Table_a;
* Query 2: SELECT MAX(num) FROM Table_b;
After running the queries, the Analyst observed that Query 2 ran significantly slower than Query 1. Why is this occurring?
A) A multi-cluster warehouse was used to run Query 1.
B) Table_b has more rows than Table_a.
C) Table_b has a row-access policy defined.
D) The USE_CACHED_RESULT was set to FALSE before running Query 2.
Solutions:
| Question # 1 Answer: C | Question # 2 Answer: B | Question # 3 Answer: B | Question # 4 Answer: B | Question # 5 Answer: C |
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