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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q23-Q28):

NEW QUESTION # 23
You have a table named USER ACTIVITY containing user interaction data'. The 'TIMESTAMP NTT column stores timestamps without time zone information, while the 'USER ID column stores IDs as VARCHAR. You need to identify users who have been active between a specific UTC time range, converting the 'TIMESTAMP NTT column to UTC. Furthermore, you want to categorize users based on the number of activities recorded. Which of the following SQL queries best achieves this, efficiently utilizing Snowflake's casting and data transformation capabilities?

  • A. Option C
  • B. Option D
  • C. Option A
  • D. Option B
  • E. Option E

Answer: B

Explanation:
Option D is best because: 1. It correctly addresses the time zone conversion. 'TIMESTAMP NTZ stores timestamps without time zone. Since the question asks for activities between a specific UTC time range, the 'TIMESTAMP_NTZ column needs to be converted to UTC for accurate comparison. 2. It correctly uses 'UTC', TIMESTAMP_NTZ)' to convert from current timezone to UTC, thus all the activities between given date range, that means all users' activity in current_timezone. It also considers Time Zone information is critical for date-related analysis. 3. It accurately categorizes users into 'Frequent' or 'Infrequent' based on the number of activities recorded through grouping by 'USER_ID. Option A converts from UTC to some other timezone, which means all dates and comparison will be in that TZ. Option B converts data that has to be in valid TIMESTAMP format which is redundant. Option C won't work because it does not convert data into TIMEZONE, so timezone conversion has to be done. Option E is incorrect because it is converting from UTC to the current timezone when we need to compare against a UTC range, so we should convert from current timezone to UTC.


NEW QUESTION # 24
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. 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.
  • 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. 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.
  • 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: A,B,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 # 25
You are tasked with analyzing website traffic data stored in a Snowflake table named 'page_views'. The table has columns 'user_id' (INT), 'page_url (VARCHAR), and 'view_time' (TIMESTAMP N T Z). You need to identify users who are likely bots based on an unusually high number of page views within a short period. Specifically, you want to flag users who have more than 100 page views within any 5-minute window Which of the following queries is the MOST efficient and accurate way to achieve this?

  • A.
  • B.
  • C.
  • D.
  • E.

Answer: E

Explanation:
Option C is the most accurate and efficient. It correctly calculates the number of views within a 5-minute window for each user using the 'DATE DIFF function and a window function. It then filters to only include users who exceed 100 views in any of those windows, and ensures each user is only counted once with the DISTINCT keyword. Option A is incorrect because it only looks at total page views across the entire dataset. Option B is syntactically incorrect and doesn't implement the time window. Option D is truncating by minute and then groups across that resulting in incorrect aggregation. The correct function is 'DATE DIFF, not 'DATE TRUNC and the logic for window is incorrect. Option E will result in errors as TUMBLE_START requires view_time to be timestamp_ltz or timestamp_tz data type.


NEW QUESTION # 26
A Snowflake data analyst needs to identify the top 5 most common errors occurring in a data loading pipeline over the past week. The error messages are stored in a table named 'LOAD ERRORS' with columns 'error_timestamp' (TIMESTAMP NTZ) and 'error message' (VARCHAR). Which SQL query provides the most efficient and accurate way to retrieve this information, ordered by frequency?

  • A. Option C
  • B. Option A
  • C. Option B
  • D. Option E
  • E. Option D

Answer: B

Explanation:
Option A is the most efficient and accurate. It uses 'DATEADD with to ensure the correct time frame is considered (including the current time), and 'GROUP BY and 'ORDER BY are used correctly to find the top 5 most frequent errors. Option B excludes errors happening today by using 'CURRENT_DATE(Y. Option C casts error_timestamp to DATE which can impact performance and might cause issues if the index is available on error_timestamp, but not DATE(error_timestamp). Option D only shows errors from the current week. Option E is incorrect. It returns error_timestamp which is a date, therefore its incorrect


NEW QUESTION # 27
You are responsible for a Snowflake data pipeline that loads data from various external sources into a data warehouse. You have implemented Snowpipe for continuous data ingestion from AWS S3. One of your data sources is known to occasionally produce corrupted data files. You need to design a resilient pipeline that can handle these corrupted files without halting the entire data loading process and allows for the identification and quarantine of these files for further investigation. Assume you have a table called 'RAW DATA' and you are using a file format 'MY CSV FORMAT. Select TWO actions which best address this situation:

  • A. Configure Snowpipe's 'ERROR INTEGRATION' to log details of files that fail during ingestion, including the error messages. Then, set ERROR-CONTINUE" on the Copy Statement. This requires an external stage for the error logs. In addition, create a separate task that queries the ERROR INTEGRATION external stage and moves the corrupt files into a separate quarantine bucket.
  • B. Implement a pre-processing step using a Snowflake task to validate the data in each file before it's loaded into the 'RAW_DATR table using a 'COPY INTO' statement. The Task would then move files failing validation to a quarantine bucket.
  • C. Set 'ON_ERROR = 'SKIP_FILE" in the 'COPY INTO' statement used by Snowpipe. This setting will skip entire files that contain errors, preventing them from being loaded into the table.
  • D.
  • E. Create a separate stream on the 'RAW DATA' table that filters for records with errors and then move these erroneous records into a quarantine table using a task. Implement ERROR=CONTINIJE on the Copy Statement.

Answer: A,B

Explanation:
Option B directly addresses the requirement for a resilient pipeline by utilizing 'ERROR INTEGRATION' to log errors and quarantine corrupt files. Setting ON_ERROR to CONTINUE avoids pipeline interruption, and creating a task to query the error stage and move files to a quarantine bucket achieves the desired outcome. Option C would be the 2nd best option because it provides for pre-processing and validation, which avoids loading the corrupted records into the RAW_DATA table in the first place. Option A only skips the file and doesn't log the errors or quarantine the file. Option D creates a stream on the RAW DATA table, but we would like to avoid writing erroneous records to the RAW DATA table in the first place. 'VALIDATE only checks the existing data, so it would not solve any problems in Data Load.


NEW QUESTION # 28
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