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[General] Snowflake DAA-C01 Latest Braindumps Sheet - VCE DAA-C01 Exam Simulator

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【General】 Snowflake DAA-C01 Latest Braindumps Sheet - VCE DAA-C01 Exam Simulator

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q55-Q60):NEW QUESTION # 55
Data clustering is an example of which type of data analysis technique?
  • A. Descriptive analysis
  • B. Exploratory analysis
  • C. Predictive analysis
  • D. Prescriptive analysis
Answer: C

NEW QUESTION # 56
When building a Snowsight dashboard that will allow users to filter data within a worksheet, which Snowflake system filters should be used?
  • A. Include the :datebucket system filter in a WHERE clause, and include the :daterange system filter in a SELECT clause.
  • B. Include the :daterange system filter in a WHERE clause, and include the :datebucket system filter in a GROUP BY clause.
  • C. Include the :datebucket system filter in a WHERE clause, and include the :daterange system filter in a GROUP BY clause.
  • D. Include the :daterange system filter in a SELECT clause, and include the :datebucket system filter in a GROUP BY clause.
Answer: B
Explanation:
Snowsight provides special System Keywords that allow analysts to create dynamic, interactive dashboards without hard-coding dates. Two of the most critical keywords are :daterange and :datebucket.
The :daterange keyword is designed to filter the volume of data based on a time period selected by the user in the dashboard UI (e.g., "Last 30 days" or "Current Year"). Because it acts as a filter on the underlying data, it must be placed in the WHERE clause of the SQL statement (e.g., WHERE created_at = :daterange). This ensures that only the records within the selected timeframe are processed by the query.
The :datebucket keyword is used for time-series aggregation. It allows the user to dynamically change the granularity of the data-for example, switching a chart from "Daily" to "Monthly" views without rewriting the query. To achieve this, :datebucket is used inside a date-truncation function in the SELECT list and, crucially, must be included in the GROUP BY clause to correctly aggregate the metrics (e.g., GROUP BY 1 or GROUP BY :datebucket).
Evaluating the Options:
* Option A is incorrect because :datebucket is for grouping/truncation, not for filtering in a WHERE clause.
* Option B is incorrect because :daterange is a filter (returning a range), not a scalar value suitable for a SELECT list.
* Option C is incorrect for the same reasons as A and B.
* Option D is the 100% correct answer. It follows the standard Snowsight design pattern: :daterange restricts the data rows in the WHERE clause, while :datebucket defines the temporal aggregation level in the GROUP BY clause.

NEW QUESTION # 57
You have identified a potentially valuable dataset on the Snowflake Marketplace that provides demographic information by zip code. Your company has customer data in a table 'CUSTOMERS' with columns 'CUSTOMER ID' ONT), 'ZIP CODE' (VARCHAR), and 'PURCHASE AMOUNT (NUMBER). Before subscribing to the Marketplace data, you want to preview the data to ensure it contains the necessary information and assess its quality. What is the recommended and MOST efficient way to preview the data from the Snowflake Marketplace listing?
  • A. Subscribe to the free trial of the Snowflake Marketplace listing, create a database from the shared data, and then sample the demographic data using 'SELECT FROM .. LIMIT 100'.
  • B. Contact the data provider directly and request a sample dataset to be delivered to you as a CSV file. Then upload this CSV file to an internal stage and query it to preview the data.
  • C. Create a stored procedure that dynamically calls the Marketplace data listing API to fetch a JSON sample and parse the JSON data into a temporary table for previewing.
  • D. Utilize the 'Preview Data' tab available directly within the Snowflake Marketplace listing. This tab typically provides a sample of the data, allowing you to inspect the data structure, content, and quality before subscribing.
  • E. Use the 'Get Data' button on the Marketplace listing and specify that you only want a small subset of the data, such as data for a specific geographic region. This will create a smaller, more manageable dataset for previewing.
Answer: D
Explanation:
Option D is the most recommended and efficient way. The 'Preview Data' tab within the Snowflake Marketplace listing is designed precisely for this purpose. It allows you to inspect a sample of the data directly within the Marketplace interface, without requiring you to subscribe or incur costs. Option A requires you to subscribe to the listing, even if it's a free trial, which is an unnecessary step for simply previewing the data. Option B is inefficient and requires manual effort to obtain and load the data. Option C is not a standard feature of the Snowflake Marketplace. Option E is overly complex and inefficient for a simple preview task.

NEW QUESTION # 58
You are tasked with ingesting data from a REST API that provides daily sales reports in JSON format. The API has rate limits (100 requests per minute) and returns a large dataset (approximately 5GB per day). The data needs to be processed within 2 hours of its availability. You want to leverage Snowflake external functions and tasks. Which approach balances efficiency, cost, and adherence to rate limits?
  • A. Create a Snowflake task that triggers an external function which retrieves only the metadata (e.g., total records, page count) from the API. Then, create a dynamic number of child tasks, each responsible for retrieving a subset of the data, using the metadata to respect the API rate limits.
  • B. Create a single Snowflake task that calls an external function which iterates through all API pages sequentially in a single execution to retrieve and load all the data. Rely on Snowflake's automatic scaling to handle the load.
  • C. Create a Snowflake external function that directly connects to the API and loads data into a staging table. Use Snowpipe with auto-ingest to continuously load the data as it arrives. Ignore the API Rate limits; assume they will be handled by API itself.
  • D. Create a Snowflake task that calls an external function. This external function calls an intermediate service (e.g., AWS Lambda, Azure Function) which is responsible for fetching the data in batches, respecting the API rate limits, and storing the data in cloud storage. Snowpipe then loads the data from cloud storage into Snowflake.
  • E. Call API directly from a scheduled task without considering Rate Limit. Persist the data using COPY INTO command.
Answer: D
Explanation:
Option D is the most balanced approach. It leverages an intermediate service to handle the complexities of API interaction (rate limits, pagination), decouples the data retrieval from Snowflake compute, and uses Snowpipe for efficient bulk loading. This approach addresses I both the rate limits and processing time requirements effectively.

NEW QUESTION # 59
You are building a dashboard to monitor website traffic. You have the following requirements: 1. Display the number of unique visitors per day. 2. Allow users to filter the data by device type (desktop, mobile, tablet). 3. Show a trend line of unique visitors over time. 4. The dashboard must refresh every 15 minutes with the latest data,. 5. The dashboard must be performant even with a large volume of dat a. Given the following table definition:

Which of the following approaches would be the MOST efficient and scalable solution in Snowflake? Select all that apply.
  • A. Use a Snowflake stream to capture changes to the 'website_traffic' table. Create a task to process the stream every 15 minutes and update a summary table with the number of unique visitors per day and device type. The dashboard queries the summary table.
  • B. Use the dashboard tool's built-in data transformation capabilities to calculate the number of unique visitors per day and device type on the fly, directly from the 'website traffic' table.
  • C. Create a standard Snowflake view that calculates the number of unique visitors per day and device type. The dashboard queries the view directly, filtering by device type. No task or stream is used.
  • D. Create a materialized view to pre-aggregate the number of unique visitors per day and device type. Set up a Snowflake task to refresh the materialized view every 15 minutes. The dashboard queries the materialized view.
  • E. Create a stored procedure to calculate the number of unique visitors per day and device type. Schedule the stored procedure to run every 15 minutes and update a table. The dashboard queries this table.
Answer: A,D
Explanation:
Materialized views (option A) and Streams with tasks (Option B) are the most efficient options for handling large datasets and real- time updates. Materialized views pre-compute the aggregates, which significantly speeds up query performance. A stream and task combination provides an incremental data processing approach, only processing new data every 15 minutes. This prevents full table scans and improves efficiency. A standard view (option C) will perform the calculation every time it's queried, leading to poor performance with large datasets. Using the dashboard tool's transformation capabilities (option D) is generally less efficient than leveraging Snowflake's compute power. Stored procedures (option E) can work but are generally less efficient than materialized views in this scenario.

NEW QUESTION # 60
......
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