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| Section | Objectives |
|---|---|
| Data Transformation and Analysis | - Analytical workloads
|
| Data Modeling and Performance Optimization | - Modeling approaches in Snowflake
|
| Data Loading and Unloading | - Data ingestion methods
|
| Snowflake Architecture and Data Platform Fundamentals | - Data platform fundamentals
|
| Security, Governance, and Data Sharing | - Access control and security
|
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44. Frage
You're analyzing a sales dataset in Snowflake. The dataset contains 'customer id', 'product id', 'sale date', and 'sale amount'. You've noticed that some 'sale_amount' values are negative, which is illogical. You also suspect duplicate records, indicated by identical 'customer_id', 'product_id', and 'sale_date'. Additionally, a business rule dictates that sale amounts must be less than $10,000. Which combination of Snowflake SQL statements is the MOST efficient and accurate way to address these data quality issues?
Antwort: B
Begründung:
Option E correctly handles all specified data quality issues. It replaces negative sale amounts with 0, enforces the business rule (sale_amount < 10000), and removes duplicate records based on 'customer_id', 'product_id', and 'sale_date' using the QUALIFY clause and ROW_NUMBER() window function, selecting only the first occurrence of each duplicate set. Options A and D do not handle negative sales amount correctly, Option B uses IFF(sale_amount < O, O, sale_amount) but do not handle boundary values of sale_amount < 10000, Option C considers sale amount between 0 and 10000.
45. Frage
A data analyst is tasked with optimizing a query that aggregates data from a table 'ORDERS' containing order details, including columns like 'ORDER ID', 'CUSTOMER ID, 'ORDER DATE, 'PRODUCT ID', and 'QUANTITY. The query calculates the total quantity of products ordered per customer and month. The current query is as follows: SELECT CUSTOMER ID, DATE TRUNC('MONTH', ORDER DATE) AS ORDER MONTH, SUM(QUANTITY) AS TOTAL QUANTITY FROM ORDERS GROUP BY CUSTOMER_ID, ORDER_MONTH ORDER BY CljSTOMER_lD, ORDER_MONTH; Deopite the 'ORDERS' table being relatively small (10 million rows), the query performance is slow. The analyst suspects a poorly chosen warehouse size. Which of the following actions, combined with monitoring query execution, would be MOST beneficial to determine the optimal warehouse size and improve query performance?
Antwort: D
Begründung:
The most beneficial approach is to start with the smallest warehouse size and incrementally increase it (B). This allows for observing the impact of warehouse size on query performance and cloud services usage. Increasing until the query time plateaus or cloud services usage increases significantly indicates the point of diminishing returns. Simply using the largest size (A) may be wasteful, and ignoring cloud services usage (C) can lead to cost overruns. Query history (D) may not be relevant if the query is significantly different. Setting a timeout (E) will not optimize the warehouse size.
46. Frage
Which of the following is a key step in data preparation?
Antwort: B
47. Frage
You have a CSV file loaded into a Snowflake table named 'raw data'. The file contains customer order data, but some rows have missing values in the 'order date' column. You need to create a new table, 'cleaned data' , that contains only valid records and handles missing 'order date' values by substituting them with the date '1900-01-01'. Which of the following approaches is the MOST efficient and correct way to achieve this using Snowflake features?
Antwort: E
Begründung:
Option E is the most efficient and correct. 'COALESCE' efficiently handles NULL replacement, and ensures the replacement value is the correct data type (DATE). It also explicitly selects all other columns. Option A only filters out rows with null order_date. Options B, C and D creates a new column, . It does not also implicitly take all columns, which would make this more appropriate.
48. Frage
You are tasked with optimizing a dashboard displaying sales performance across different regions. The dashboard queries a large table called 'SALES DATA, which is partitioned by 'SALE DATE. The dashboard loads very slowly. The data analyst team notices that queries for the dashboard often filter on a specific region and a date range. Which of the following steps can you take to improve the dashboard's performance? (Select TWO)
Antwort: B,D
Begründung:
Creating a materialized view (A) pre-aggregates the data, significantly reducing the amount of data that needs to be processed for each dashboard refresh. Reclustering on 'REGIONS and (D) physically organizes the data on disk based on these frequently used filter columns, improving query performance by minimizing the need to scan irrelevant partitions. Table clones (B) provide a separate copy of the data but don't inherently improve query performance. Search optimization (C) is useful for point lookups, but not as effective for date range filtering in this scenario. Increasing warehouse size (E) helps, but optimizing the data structure offers a more fundamental and cost-effective improvement.
49. Frage
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