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| Section | Weight | Objectives |
|---|---|---|
| Use Built-in Functions and Create UDFs | 10–15% | - Scalar, aggregate, table, system functions - User-Defined Functions (UDFs) |
| 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% | - Using Snowflake ML and built-in analytics - Forecasting and predictive modeling |
| Perform Descriptive and Diagnostic Analysis | 10–15% | - Exploratory and ad-hoc analysis - Anomaly detection and root cause analysis - Statistical summarization and trend analysis |
| Prepare and Present Data | 10–15% | - Snowsight dashboards and sharing results - Align outputs with business requirements - Data visualization and reporting |
| Prepare and Load Data | 15–20% | - External tables and data validation - File formats: CSV, JSON, Parquet, Avro - Data ingestion methods: COPY INTO, stages, Snowpipe |
| 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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NEW QUESTION # 24
You are working with a table 'ORDERS' containing order data, and a table 'CUSTOMER SEGMENTS containing customer segment information. The 'ORDERS' table has columns 'ORDER ID', 'CUSTOMER ID, and 'ORDER AMOUNT'. The 'CUSTOMER SEGMENTS' table has columns 'CUSTOMER ID', 'SEGMENT ID', and 'SEGMENT NAME'. You need to create a query that enriches the 'ORDERS' table with the customer segment information. However, a customer can belong to multiple segments. You want to include all segments a customer belongs to in the enriched data, resulting in potentially multiple rows per order if the customer is in multiple segments. The output should include 'ORDER ID, 'ORDER AMOUNT, 'SEGMENT ID', and 'SEGMENT NAMES. Which SQL statement would correctly enrich the ORDERS table without losing any order information, even if customers belong to multiple segments?
Answer: A
Explanation:
An 'INNER JOIN' is the correct choice here. It will return all matching rows between the 'ORDERS' and tables based on the 'CUSTOMER ID. If a customer belongs to multiple segments, each segment will be returned in a separate row associated with the order. "LEFT JOIN' includes rows from the left table even without a match in the right table which is not needed. 'GROUP BY' in the INNER JOIN' will combine multiple segments with 'ORDER_IDS into one row, which is not needed as all segment names are expected. The subquery approach would only return one segment name per order. 'RIGHT JOIN' include all customer segments on the right which is also not needed.
NEW QUESTION # 25
When sharing views across accounts, which function should be used on the row access policy for the secure views to give users access to rows in a base table?
Answer: A
Explanation:
In the context of Snowflake Data Sharing, the security model changes because the users and roles accessing the data do not exist in the provider's account. When a provider shares a Secure View that is protected by a Row Access Policy (RAP), standard functions like CURRENT_ROLE or CURRENT_USER are often insufficient or return different values than expected because they reflect the consumer's local environment.
To effectively filter data for different consumers within a single shared table, the CURRENT_ACCOUNT() function is the primary tool. This function returns the unique account identifier of the system currently accessing the data. By including CURRENT_ACCOUNT() in the row access policy logic (e.g., WHERE shared_account_id = CURRENT_ACCOUNT()), the data provider can ensure that each consumer account only sees the specific subset of rows intended for them.
Evaluating the Options:
* Option A and D: CURRENT_ROLE and CURRENT_AVAILABLE_ROLES are generally used for internal account security. When data is shared, the roles in the consumer account do not map to the provider's roles, making these functions unreliable for cross-account row-level filtering.
* Option C: CURRENT_USER is likewise restricted to the local account's user metadata.
* Option B: Is the 100% correct answer. It is the specific system function designed to identify the consumer account in a data-sharing relationship, allowing for robust multi-tenant data protection. This is a vital skill for the Data Governance and Security domain, ensuring that "Secure Data Sharing" remains truly secure.
NEW QUESTION # 26
A marketing company is analyzing customer purchase data stored in Snowflake to understand which customer demographics are most likely to purchase a newly launched product. The 'CUSTOMERS table has columns: 'customer_id', 'age' , 'gender' , 'location' , and 'household income'. The 'PURCHASES' table has columns: 'customer_id', 'purchase_date', and 'product id'. Which SQL query would most effectively identify the top three age groups with the highest purchase rate for the new product (product_id = 'NEW PRODUCT')?
Answer: B
Explanation:
Option D is the most effective because it calculates the purchase rate by dividing the count of distinct customers who purchased the new product by the total number of customers in each age group. This provides a normalized purchase rate for each age group, allowing for a fair comparison regardless of the size of each group. Options A and C only provide the raw count of purchases or unique purchasers, which doesn't account for the size of each age group. Option B uses 'SUM(CASE...V , which can work, but is less efficient than using 'COUNT(DISTINCTV in combination with a left join to account for all customers, even those who didn't make any purchases. Option E would produce an incorrect rate as it is dividing a customer's count by the distinct count of each customer, resulting in a rate close to 1.0 for each customer that made a purchase.
NEW QUESTION # 27
Why are materialized views valuable in data analysis compared to regular views?
Answer: D
Explanation:
Materialized views provide precomputed snapshots, improving query performance.
NEW QUESTION # 28
A marketing team wants to understand the impact of their campaigns on website traffic and conversions. You have the following tables in Snowflake: sCAMPAlGN PERFORMANCE: 'CAMPAIGN ONT), (DATE), 'CLICKS' (INT), 'IMPRESSIONS' (INT), 'COST (NUMBER) 'DATE' (DATE), 'PAGE_VIEWS' ONT), (INT) 'CONVERSIONS' 'DATE (DATE), ONT), (NUMBER) Which SQL query and visualization technique would be most suitable for identifying the correlation between campaign spend and website conversions over time, allowing the team to quickly identify campaigns with a high return on investment (ROI)?
Answer: A
Explanation:
Option D is the most effective. A line chart plotting cost, unique visitors, and conversion count over time allows for visual identification of trends and correlations. Displaying ROI on the same chart provides a direct measure of campaign effectiveness. The CTE helps organize the ROI calculation. Option B, while useful, doesn't explicitly show the temporal relationship. Options A and C offer limited insights into ROI. Exporting and using Excel (E) is not scalable or efficient for dynamic analysis.
NEW QUESTION # 29
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