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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Prepare and Load Data | 15–20% | - File formats: CSV, JSON, Parquet, Avro - Data ingestion methods: COPY INTO, stages, Snowpipe - External tables and data validation |
| Topic 2: Perform Predictive Analysis | 5–10% | - Forecasting and predictive modeling - Using Snowflake ML and built-in analytics |
| 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: Prepare and Present Data | 10–15% | - Snowsight dashboards and sharing results - Data visualization and reporting - Align outputs with business requirements |
| Topic 5: Perform Simple Data Transformations for Analysis | 15–20% | - Views, materialized views, CTEs - Handling NULLs and structuring datasets - Data cleansing, standardization, type conversion |
| Topic 6: Use Built-in Functions and Create UDFs | 10–15% | - User-Defined Functions (UDFs) - Scalar, aggregate, table, system functions |
| 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 add address details based on a customer's latitude and longitude to a customer sales database. The Analyst found a free Worldwide Address Data listing on the Snowflake Marketplace. The ACCOUNTADMIN placed the data set into a new database called ADDRESS_DATA. The Data Analyst needs to join the ADDRESS_DATA.OPENADDRESS table with the ORDERS table which is stored in the GLOBAL_DWH database. The combined data set needs to be created as a view. How can this be achieved?
A) Ask the ACCOUNTADMIN to grant the Data Analyst the IMPORTED_PRIVILEGES on the ADDRESS_DATA database and then create a view in the ADDRESS_DATA database.
B) Create a new schema called ENRICHED in the ADDRESS_DATA database and create this view in the ENRICHED schema.
C) Create a view in the ADDRESS_DATA database.
D) Create a view in the GLOBAL_DWH database.
2. A Data Analyst is working with a table that has 1 record per day, with sales information. Which window function would calculate a 7-day moving average of sales, where SALES_DATE represents the date column?
A) AVG(SALES) OVER (ORDER BY SALES_DATE ROWS BETWEEN 6 PRECEDING AND
CURRENT ROW)
B) SUM(SALES) OVER (ORDER BY SALES_DATE ROWS BETWEEN 7 PRECEDING AND
CURRENT ROW)
C) AVG(SALES) OVER (ORDER BY SALES_DATE ROWS BETWEEN 7 PRECEDING AND
CURRENT ROW)
D) SUM(SALES) OVER (ORDER BY SALES_DATE ROWS BETWEEN 6 PRECEDING AND
CURRENT ROW)
3. Which aggregate functions can be used to provide values for a given bucket in Snowsight charts? (Select THREE).
A) Max
B) Average
C) Any Value
D) Mode
E) HLL
F) Standard Deviation
4. This query is run:
SQL
SELECT
customer.id,
ANY_VALUE(customer.name),
SUM(orders.value)
FROM customer
JOIN orders ON customer.id = orders.customer_id
GROUP BY customer.id;
What is the effect of ANY_VALUE in this syntax?
A) It will return the minimum value of those generated by the expression, with a deterministic result.
B) It will return some value of the expression from the group, with a non-deterministic result.
C) It will return a value equivalent to the median of those generated by the expression, which may be a non-deterministic result.
D) It will return an equivalent NULL value when the expression is evaluated.
5. A Data Analyst has a Parquet file stored in an Amazon S3 staging area. Which query will copy the data from the staged Parquet file into separate columns in the target table?
A) Option C
B) Option A
C) Option D
D) Option B
Solutions:
| Question # 1 Answer: D | Question # 2 Answer: A | Question # 3 Answer: A,B,C | Question # 4 Answer: B | Question # 5 Answer: A |
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