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| Section | Weight | Objectives |
|---|---|---|
| Data Ingestion and Data Preparation | 15%-20% | - Enrich data by identifying and accessing relevant data from the Snowflake Marketplace
|
| Data Presentation and Data Visualization | 28%-29% | - Integrate with BI tools
|
| Data Analysis | 30%-32% | - Perform advanced analytics using SQL
|
| Data Transformation and Data Modeling | 22%-30% | - Design data models
|
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NEW QUESTION # 14
You are tasked with enriching a customer dataset in Snowflake. The 'CUSTOMER DATA table contains customer IDs and country codes. You have a separate 'COUNTRY INFORMATION' table that contains country codes and corresponding currency codes. Both tables reside in the 'RAW DATA schema of the 'ANALYTICS DB' database. You need to create a view called ENRICHED CUSTOMER DATA' in the 'TRANSFORMED DATA' schema that joins these tables to add currency information to the customer data'. You want to optimize this view for performance. Which of the following approaches would be the MOST efficient and scalable, considering potential data volume increases?
Answer: B
Explanation:
Materialized views generally provide better performance than standard views for complex queries, especially with joins. Clustering on the 'COUNTRY_CODE column further enhances performance by physically organizing the data based on this column, making lookups more efficient. Using secure view wouldn't impact on performance. Regular view refreshment using scheduled task is not as efficient compared to materialized view. UDFs can introduce performance overhead, especially for large datasets.
NEW QUESTION # 15
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?
Answer: A
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 # 16
A data analyst is tasked with monitoring the performance of several Snowpipe data ingestion processes. They need to set up alerts based on the number of files failing to load within a specific timeframe. Which combination of Snowflake features and logging/monitoring practices would be MOST effective in achieving this?
Answer: C,E
Explanation:
Option A provides a robust solution by leveraging Snowflake's PIPE_USAGE_HISTORY to query load errors. An external monitoring tool allows for advanced alerting capabilities beyond Snowflake's native functionality. Option C offers another effective strategy using Snowflake's event tables for capturing errors and using a stored procedure for customizable alerting based on defined thresholds. Options B and D are less directly related to tracking file loading failures specifically. Option E is not a proactive monitoring approach.
NEW QUESTION # 17
You are tasked with loading data from a CSV file stored in AWS S3 into a Snowflake table named 'SALES DATA. The CSV file has a header row, is comma-delimited, and contains a date field (SALE DATE) in the format 'MM/DD/YYYY'. Some rows in the CSV contain invalid date values (e.g., '02/30/2024'). You need to create a file format, a stage, and a copy command to load the data, skipping rows with invalid dates and logging the errors for later analysis. Which of the following snippets correctly define the file format and COPY command to achieve this? Assume an external stage named already exists.
Answer: A
Explanation:
The correct answer is A. 'ON_ERROR = SKIP_FILE skips the entire file if any errors are encountered during loading. Other options like 'CONTINUE' might load invalid data and 'ABORT STATEMENT will stop the loading process entirely. The question specifically asks for skipping rows with invalid dates, while still loading the valid rows from the file. SKIP_FILE ignores the entire file and logs the error. However, this approach skips the entire file if even one row is invalid. A better approach would be to use VALIDATION MODE and potentially a transformation during load, but given the options presented, SKIP_FILE is the closest solution to the problem statement.
NEW QUESTION # 18
While reviewing the query profile for a complex data transformation pipeline, you notice a significant amount of time spent in the 'Join' operation between two large tables, 'transactions' and 'customers'. The join is performed on the 'customer _ id' column. Which of the following are potential strategies to optimize the join performance?
Answer: B,D
Explanation:
Mismatched data types can cause implicit type conversions, which can significantly degrade join performance. Clustering both tables on the join key ('customer_id') will ensure that rows with the same customer ID are located closer together on disk, reducing 1/0 and improving join efficiency. Broadcasting the smaller table is not directly controllable by the user in Snowflake and is handled automatically by the optimizer. 'LATERAL FLATTEN' is not a direct replacement for a join. Increasing the virtual warehouse size might improve overall processing time but doesn't address the specific inefficiency of the join operation.
NEW QUESTION # 19
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