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| Section | Objectives |
|---|---|
| Topic 1: Data Transformation and Analysis | - Analytical workloads
|
| Topic 2: Snowflake Architecture and Data Platform Fundamentals | - Snowflake architecture concepts
|
| Topic 3: Security, Governance, and Data Sharing | - Data sharing and governance
|
| Topic 4: Data Modeling and Performance Optimization | - Performance tuning
|
| Topic 5: Data Loading and Unloading | - Data ingestion methods
|
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NEW QUESTION # 20
You have identified inconsistencies in the data type of the 'ORDER DATE' column across several tables within your Snowflake database. Some tables store it as DATE, while others store it as VARCHAR. You need to create a unified view that presents 'ORDER DATE' consistently as DATE, handling potential conversion errors gracefully. You have to use safe aggregate operations. Which of the following approaches provides the most robust and error-tolerant solution?
Answer: B
Explanation:
The most robust and error-tolerant solution is option E, using 'TRY CAST(ORDER DATE AS DATE)'. This function attempts to convert the 'ORDER_DATE' to a DATE data type, and if the conversion fails (e.g., the VARCHAR value cannot be parsed as a date), it returns NULL without raising an error. This ensures that the view creation and queries against it will not fail due to data type conversion issues. Option A, , is an older function, and ' TRY_CAST' is the preferred, more general function. Option B, 'IS_DATE is not a valid snowflake Function. Option C, ' TO DATE will fail the query if the data in column is not a valid date. Option D 'SAFE_CAST is not a valid snowflake function.
NEW QUESTION # 21
You are working with a table named 'PRODUCT DETAILS' that contains a 'PRICE column stored as a VARCHAR. The data in this column has inconsistencies, including leading/trailing spaces, currency symbols (e.g., '$', 'O'), and different decimal separators ('.' And ','). Additionally, some values are represented as 'N/A' or an empty string. You need to clean and validate this data to ensure the "PRICE' column can be safely converted to a NUMERIC data type. Choose the set of SQL transformations that will correctly clean the PRICE column. (Select all that apply)
Answer: A,C,D,E
Explanation:
This question requires multiple correct answers. Options A, B, C, and D are all necessary for cleaning the 'PRICE' column. Option A handles the 'N/A' and empty string values by setting them to NULL. Option B removes leading/trailing spaces using the 'TRIM' function. Option C removes currency symbols using 'REGEXP REPLACES. Option D standardizes the decimal separator by replacing commas with periods using REPLACE. Option E is not correct because it attempts to change the data type of the 'PRICE column, but the column is not yet cleaned to allow for successful conversion to a NUMERIC data type.
NEW QUESTION # 22
When performing a diagnostic analysis, which actions aid in collecting related data to identify anomalies? (Select all that apply)
Answer: D
Explanation:
Collecting data from various sources aids in comprehensive analysis, assisting in identifying anomalies.
NEW QUESTION # 23
What does the "SQL keyword" refer to in the context of adding filters to a worksheet?
Answer: B
Explanation:
In Snowsight (Snowflake's web interface), Data Analysts can create interactive dashboards and worksheets using Filters. When you define a filter (such as a Date Range or a list of Regions), you must assign it a SQL Keyword.
This "SQL Keyword" acts as a variable placeholder within your SQL code. For example, if you create a filter for "Customer Region" and set the SQL keyword to :my_region, you can then write a query like:
SELECT * FROM sales WHERE region = :my_region. When a user interacts with the UI and selects "North America" from the dropdown, Snowsight automatically injects "North America" into every instance where :
my_region appears in the worksheet's SQL.
Evaluating the Options:
* Option A is incorrect because the keyword is a label/variable, not the underlying function code.
* Option C and Option D are incorrect as they confuse the data source of the filter values with the reference name used in the SQL code.
* Option B is the correct answer. The SQL Keyword is specifically the identifier (prefixed with a colon in the code) that allows the analyst to link the UI element (the filter) to the execution logic of the query.
This is a fundamental skill for the Data Presentation and Data Visualization domain, ensuring reports are dynamic and user-friendly.
NEW QUESTION # 24
You need to create a dashboard for a logistics company to track delivery performance. The dashboard should display the following information: (1) Total number of deliveries per day, (2) Percentage of deliveries completed on time, (3) Average delivery time per city, (4) Number of deliveries exceeding the SLA (Service Level Agreement) by more than 1 hour. Which of the following chart combinations would be MOST effective to display this information in a clear and concise manner?
Answer: C
Explanation:
A Time series chart is best for showing trends over time (total deliveries per day). A scorecard provides a concise view of a key metric (percentage of on-time deliveries). A geographical map with color-coded cities effectively visualizes location-based data (average delivery time per city). A table provides detailed information on specific instances (deliveries exceeding SLA). Pie Charts are less ideal for percentage since it can be achieved better using Scorecard, heat grid not the best choice when geo is involved and it is less readable.
NEW QUESTION # 25
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