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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q142-Q147):

NEW QUESTION # 142
A company stores sensor data, including timestamps (ts), sensor ID (sensor_id), and readings (reading_value), in a Snowflake table named 'sensor_data'. Due to sensor malfunctions, some readings are significantly higher or lower than expected (outliers). Which of the following approaches are suitable in Snowflake to calculate the average reading value for each sensor, EXCLUDING readings that fall outside of two standard deviations from the mean for that sensor?

A. Using a LATERAL FLATTEN function to transform reading values into an array, calculate the mean and standard deviation in a JavaScript UDF, then use ARRAY SLICE to remove outliers before calculating the average.
B. Using a QUALIFY clause with window functions to filter out the outlier readings based on their distance from the mean, prior to calculating the final average.
C. Calculating the mean and standard deviation for each sensor in a subquery, then joining the results with the original data and filtering based on the calculated values.
D. Using a HAVING clause after grouping by sensor_id to filter out groups where the range of reading_value exceeds a certain threshold.
E. Using window functions to calculate the mean and standard deviation for each sensor, then filtering the results to exclude outliers using a WHERE clause.

Answer: B,C,E

Explanation:
Options A, B and C provides valid ways to determine outliers. A is based on direct filtering based on standard deviation on the original table using window function. B uses Sub query approach and filtering. C allows to use QUALIFY clause with window functions for filtering before aggregation. D attempts to filter groups based on range which is not the intent of the original question to filter on a per reading basis if its an outlier or not. Option E, although technically possible, introduces significant complexity and performance overhead with the use of UDF and array manipulation for a task achievable with standard SQL.

NEW QUESTION # 143
You are designing a data ingestion pipeline for IoT sensor data'. Sensors transmit data in JSON format every 5 seconds. The volume of data is expected to grow exponentially. The business requires both real-time dashboards and historical trend analysis. Which of the following strategies should you employ to address these requirements, particularly focusing on optimizing for both ingestion frequency and cost?

A. Implement Snowpipe for initial data ingestion, complemented by Snowflake's clustering feature based on timestamp to optimize historical analysis queries. And employ a stream processing engine to perform time window pre-aggregation.
B. Use Snowpipe to ingest data into a raw landing table, and then use Snowflake tasks to transform and load the data into separate tables optimized for real-time dashboards and historical analysis.
C. Ingest data directly into a single Snowflake table using Snowpipe with JSON data type. Create separate materialized views for real-time dashboards and historical trend analysis.
D. Employ a combination of Snowpipe for near real-time data ingestion into a raw table, and then use Snowflake's Search Optimization Service for faster queries.
E. Utilize an external stream processing engine to pre-aggregate the data into time windows (e.g., I-minute, I-hour) before ingesting into Snowflake using Snowpipe.

Answer: A,B

Explanation:
This question has multiple correct answers. Option B and E addresses the need for optimizing both ingestion frequency and cost. Option B involves using Snowflake tasks for transformations, which allows for separating data for real-time and historical analysis. This optimizes query performance for both use cases. Using only Snowpipe directly into a single table is simple but doesn't provide optimization for the different query patterns, while the other options are less efficient or don't fully address the dual requirements. Option E complements using Snowpipe by using Snowflake clustering features and employing stream processing engine to perform pre-aggregation. Snowflake clustering optimize the speed of historical analysis, reducing cost of scanning large datasets.

NEW QUESTION # 144
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?

Answer: D

Explanation:
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.

NEW QUESTION # 145
A financial institution needs to collect stock ticker data for intraday trading analysis. The data source provides updates every second. They need to maintain a 5-minute rolling average of stock prices for each ticker. The system needs to be highly available and resilient to data source interruptions. Considering the need for near real-time analysis and potential data source instability, which combination of technologies and approaches would be MOST effective?

A. Storing the raw data into Snowflake using Snowpipe in micro-batches and creating a VIEW that performs the rolling average calculation on-demand.
B. Using a traditional ETL tool to extract, transform (calculate rolling average), and load the data into Snowflake in 15-minute intervals.
C. Using a scheduled task to query the API every minute and store the data directly into a Snowflake table with a materialized view calculating the rolling average.
D. Employing a stream processing framework (e.g., Apache Kafka) to ingest the data, perform the rolling average calculation using a tumbling window, and load the aggregated results into Snowflake.
E. Leveraging Snowflake's dynamic data masking and data classification capabilities to maintain data security and compliance while adhering to real-time data ingestion.

Answer: D

Explanation:
A stream processing framework like Kafka is ideal for handling high-velocity data streams. Kafka provides fault tolerance and the ability to perform real-time aggregations (rolling average with tumbling window). While Snowpipe can ingest the raw data quickly, calculating the rolling average on-demand (using a VIEW) may not meet the near real-time requirement and can be inefficient. A scheduled task might not be able to handle the volume and frequency of data. The key to answering this question is understanding the need for real-time aggregation AND resilience to potential data source outages, both of which Kafka elegantly addresses.

NEW QUESTION # 146
You are tasked with creating a Snowsight dashboard to monitor the daily sales performance of an e-commerce platform. The sales data is stored in a table named 'SALES_DATR with columns 'SALE_DATE' (DATE), 'PRODUCT_ID' ONT), 'SALES AMOUNT' (FLOAT), and 'CUSTOMER REGION' (VARCHAR). You need to display a trendline chart showing the total sales amount for each day over the last 30 days, and also a table showing the top 5 regions by total sales amount. Which of the following steps are most efficient to achieve this in Snowsight? (Select TWO)

A. Use the 'Notebook' feature in Snowsight to first perform the aggregations required to obtain the data for the visualizations, and then create visualization tiles by referencing the calculated dataframes. No direct SQL is used in the tiles.
B. Use two separate worksheets in Snowsight. In the first worksheet, create the time series chart. In the second worksheet, create the region-based sales table. Then, embed one worksheet inside the other to create a combined dashboard.
C. Create two separate tiles in the Snowsight dashboard. One tile should use a time series chart visualizing 'SELECT SALE_DATE, SUM(SALES_AMOUNT) FROM SALES_DATA WHERE SALE_DATE DATEADD(day, -30, GROUP BY SALE_DATE ORDER BY SALE_DATE;' and the other tile should use a table visualizing 'SELECT CUSTOMER REGION, SUM(SALES AMOUNT) FROM SALES DATA GROUP BY CUSTOMER REGION ORDER BY DESC LIMIT 5;'
D. Create a single tile using a combination chart in Snowsight. Use 'SALE DATE as the x-axis and 'SUM(SALES AMOUNT)' as the y-axis, specifying a trendline. Then, add a second series to display region-based sales in a separate panel below.
E. Create a single tile with two tabs. The first tab displays the trendline chart using a time series and the second tab displays the region-based sales table. Both tabs use the same underlying SQL query but filter the results differently.

Answer: A,C

Explanation:
Option A is correct because it directly and efficiently creates the desired visualizations in Snowsight using separate tiles. Option C is also correct. Using Snowsight's Notebook feature allows for pre-aggregation, which can be useful in complex scenarios where directly visualizing from the raw data would impact dashboard performance. Options B, D, and E are either not directly supported or less efficient methods within the current Snowsight dashboarding capabilities.

NEW QUESTION # 147
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