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【General】 Exam DAA-C01 Flashcards - DAA-C01 Valid Test Discount

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q159-Q164):NEW QUESTION # 159
A Snowflake table 'USER ACTIVITY' contains a column 'ACTIVITY TIMESTAMP' of type VARCHAR, which stores timestamps in different formats, including ISO 8601 (e.g., '2023-10-27 Unix epoch seconds (e.g., '1698400800'), and milliseconds (e.g., '1698400800000'). You need to convert all these timestamps into a consistent TIMESTAMP NTZ data type in Snowflake. Which approach offers the most efficient and accurate solution for converting these diverse timestamp formats into a unified TIMESTAMP NTZ?
  • A. Use a UDF (User-Defined Function) that intelligently detects the timestamp format and applies the appropriate conversion function CTO TIMESTAMP , etc.).
  • B. Utilize Snowflake's automatic type conversion capabilities by simply changing the column's data type to TIMESTAMP_NTZ using ALTER TABL
  • C. First convert epoch milliseconds to epoch seconds by dividing by 1000. Then use a single 'TO TIMESTAMP' function assuming default ISO 8601 format.
  • D. Create separate temporary tables for each timestamp format using 'TO_TIMESTAMP' , then UNION ALL them together.
  • E. Use a series of 'CASE' statements with ' TO_TIMESTAMP' and multiple format strings to parse each format individually.
Answer: A
Explanation:
Option C provides the most efficient and accurate solution. While 'CASE' statements (Option A) are viable, they can become complex and less maintainable with many formats. Creating temporary tables (Option B) is inefficient. Dividing milliseconds and assuming ISO 8601 (Option D) will fail for true ISO 8601 formats and might cause loss of precision. Snowflake's automatic type conversion (Option E) won't handle the mixed formats. A UDF (Option C) encapsulates the logic for format detection and conversion, promoting code reusability and maintainability. It allows you to use different Snowflake functions within the UDF to parse different formats, ensuring accurate conversion to a unified TIMESTAMP NTZ data type.

NEW QUESTION # 160
When dealing with JSON data in Snowflake and utilizing built-in functions for traversing, flattening, and nesting, what complexities might arise? (Select all that apply)
  • A. Difficulties in parsing and querying nested JSON objects
  • B. Limitations in processing specific JSON data sizes
  • C. Challenges in handling specific data types only
  • D. Complexity in handling nested JSON structures
Answer: A,D
Explanation:
Handling nested JSON structures and complexities in parsing and querying nested JSON objects might pose challenges when manipulating JSON data in Snowflake using built-in functions.

NEW QUESTION # 161
You are tasked with creating a dashboard to visualize sales performance across different product categories and regions. The data is stored in a Snowflake table named with columns: 'SALE DATE (DATE), 'PRODUCT CATEGORY (VARCHAR), 'REGION' (VARCHAR), 'SALES_AMOUNT (NUMBER). The business stakeholders want to see a trend of monthly sales for the past year, a breakdown of sales by region, and a comparison of sales between product categories. Which of the following approaches would be MOST effective and efficient in Snowflake for generating the data needed for these visualizations, considering the need for dashboard responsiveness and minimal query cost?
  • A. Use Snowflake's caching mechanism extensively to cache the results of individual queries within the dashboard, ensuring that subsequent requests for the same data are served from the cache. Rely on the dashboard's internal caching capabilities.
  • B. Create a single, complex SQL query that joins the 'SALES DATA' table with itself multiple times, using window functions and subqueries to calculate all the required aggregations and breakdowns within the query. The dashboard will directly query this view.
  • C. Export the data to an external data warehouse for visualization purpose to avoid overhead of Snowflake visualization tool.
  • D. Create multiple materialized views, each specifically designed for one of the dashboard visualizations (monthly sales trend, sales by region, sales by product category). Refresh these materialized views regularly.
  • E. Create a data pipeline that transforms the raw 'SALES_DATX into summary tables using tasks and streams. These summary tables are optimized for the specific queries required by the dashboard visualizations.
Answer: D,E
Explanation:
Creating multiple materialized views (B) allows for pre-calculated aggregations tailored to each visualization, significantly improving dashboard responsiveness and reducing query costs. Using tasks and streams to transform the raw data into optimized summary tables (D) also pre-processes the data, providing similar benefits to materialized views, but allows for more complex transformations and incremental updates. A single complex query (A) can be slow and resource-intensive. Relying solely on caching (C) might not be sufficient for complex aggregations or large datasets. Exporting data to a completely new system adds overhead and is unnecessary given Snowflake's capabilities.

NEW QUESTION # 162
You are tasked with loading JSON files into Snowflake using Snowsight. The JSON files are semi-structured and contain nested arrays and objects. You want to flatten the JSON structure during the load process to facilitate easier querying. Which of the following Snowsight-integrated features or approaches are suitable for flattening the JSON data during the load process? Select all that apply.
  • A. Use Snowsight's 'Load Data' wizard along with Snowflake's built-in functions like 'GET_PATH' and operator within the computed column expressions during the load process to extract and load specific elements from the JSON data into individual columns.
  • B. Create a Snowpipe that automatically loads new JSON files from a stage into a Snowflake table. Within the Snowpipe definition, define a transformation that uses 'LATERAL FLATTEN' to flatten the JSON data before loading it into the target table.
  • C. Load the JSON data into a staging table as raw JSON strings. Then, write a custom Python User-Defined Function (UDF) that parses the JSON strings and returns a flattened representation of the data. Use this UDF in a 'CREATE TABLE AS SELECT statement to load the flattened data into a new table.
  • D. Load the JSON data into a VARIANT column in Snowflake. Then, use SQL with 'LATERAL FLATTEN' to query and extract data from the nested structures, creating a view to represent the flattened data.
  • E. Use Snowsight's 'Load Data' wizard to load the JSON files directly into relational tables by defining mappings between JSON elements and table columns. Snowsight automatically flattens the JSON structure based on the defined mappings.
Answer: A,B,D
Explanation:
Options A, C, and E are all suitable approaches for flattening JSON data. Option A leverages 'LATERAL FLATTEN' in SQL, which is a common and efficient way to flatten JSON structures after loading into a VARIANT column. Option C utilizes Snowpipe with a transformation including 'LATERAL FLATTEN' , enabling automatic flattening during ingestion. Option E uses computed columns in Snowsight to extract JSON elements during loading using 'GET_PATH' or the operator. Option B is incorrect; Snowsight's 'Load Data' wizard doesn't automatically flatten JSON during the initial load. It mainly handles the basic load of the JSON file into a VARIANT column, not automatic flattening. Option D is a valid but less performant alternative; UDFs can be slower than native Snowflake SQL functions for large datasets.

NEW QUESTION # 163
A financial institution needs to categorize transactions as 'High Risk', 'Medium Risk', or 'Low Risk' based on a complex set of rules involving transaction amount, location, merchant type, and customer history. You are using Snowflake to implement this classification. Which of the following approaches would provide the MOST flexible and scalable solution for defining and managing these risk classification rules, and then applying them to incoming transaction data?
  • A. Using Snowflake's Python User-Defined Functions (UDFs) to encapsulate the risk classification logic, allowing for more complex calculations and the use of external libraries if needed.
  • B. Implementing the classification logic directly within a single, large SQL query using nested 'CASE' statements and complex conditional logic.
  • C. Creating a series of Snowflake Tasks that execute SQL queries to categorize transactions based on individual rules, chaining these tasks together in a specific sequence.
  • D. Leveraging Snowflake's external functions to call a rules engine deployed on a separate server (e.g., a Drools rules engine running on AWS EC2) to perform the risk classification.
  • E. Storing the risk classification rules in a separate Snowflake table and using a dynamic SQL query to generate and execute the classification logic based on the data in the rules table.
Answer: A,D
Explanation:
Options C and E are the most flexible and scalable. Option C enables the creation of complex logic with Snowflake UDF, while option E uses external functions for connecting to separate rule engines. Option A would become unwieldy and difficult to maintain as the number of rules increases. Option B may not be suitable for real-time classification and may introduce unnecessary complexity. Option D, while offering some flexibility, can be challenging to implement and optimize for complex rules.

NEW QUESTION # 164
......
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