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How does incorporating visualizations in reports and dashboards aid in data comprehension for business use analyses?
A. Visualizations only impact data comprehension negatively.
B. It limits data presentation to text, hindering analysis.
C. Visualizations enhance data comprehension for effective analysis.
D. Presenting data visually doesn't impact business use analyses.
Answer: C
Explanation:
Visualizations enhance data comprehension, aiding effective analysis in business use scenarios.
NEW QUESTION # 171
You're working with a large dataset containing website user activity, including 'session_id', 'user_id', 'timestamp', and 'page_view'. You suspect bot activity is skewing your engagement metrics. Bots tend to have very short session durations and high page view counts within those sessions. Which of the following Snowflake SQL queries, used in combination, would be MOST effective in identifying and flagging potential bot sessions, considering performance on a large dataset? Assume you have access to Snowflake's statistical functions.
A.
B.
C.
D.
E.
Answer: E
Explanation:
Option E is most effective because it identifies bot sessions based on two distinct criteria: unusually high page view counts AND unusually short session durations, which are characteristic of bots. By combining these two conditions with standard deviations, it reduces the likelihood of false positives. All other answer choices only check rate and not the count or session duration .
NEW QUESTION # 172
You have a large CSV file containing customer transaction data that you need to load into Snowflake using Snowsight. The CSV file is located in an AWS S3 bucket. The file contains fields like 'transaction id', 'customer id', 'transaction date', and 'transaction amount. However, the 'transaction_date' column is in the format 'YYYYMMDD' and you need to convert it to Snowflake's DATE format ('YYYY-MM-DD') during the load process. Which of the following steps should you take in Snowsight to accomplish this efficiently and correctly?
A. Create a new table in Snowflake with the desired schema (including DATE data type for 'transaction_date'). Use Snowsight's 'Load Data' wizard to load the CSV file, selecting the appropriate file format options and using a computed column expression 'TO_DATE(transaction_date, 'YYYYMMDD')' for the 'transaction date' column.
B. Use Snowsight's 'Load Data' wizard to load the CSV file directly into a table with the required schema. After loading, execute an "UPDATE statement to convert the 'transaction_date' column using 'TO DATE(transaction_date, YYYYMMDD'V.
C. Load the data into a staging table with all columns as VARCHAR. Then, create a new table with the desired schema. Finally, use a 'CREATE TABLE AS SELECT (CTAS) statement with 'TO DATE(transaction_date, to transform and load the data from the staging table to the final table.
D. Create an external table pointing to the S3 bucket. Then, create a view on top of the external table with the 'TO_DATE(transaction_date, 'YYYYMMDD')' transformation applied. Finally, create a new table using 'CREATE TABLE AS SELECT from the view.
E. Load the CSV file into Snowflake without any transformation. Write a stored procedure to transform the 'transaction_date' column and schedule the stored procedure to run periodically.
Answer: A
Explanation:
Option A is the most efficient and correct approach. Snowsight's 'Load Data' wizard allows you to specify transformations during the load process using computed columns, which is more performant than loading into a staging table or updating after loading. Options B, C, D and E are functional but less efficient due to the extra steps involved. Using external tables for initial loading then CTAS can be good for exploration but not as direct as option A. Updates should generally be avoided on large datasets after loading when you have a chance to transform during load.
NEW QUESTION # 173
When identifying and accessing relevant data from the Snowflake Marketplace, what challenges might arise in correlating external datasets with available data?
A. Inability to access marketplace data
B. Data format compatibility issues
C. Limited external data sources
D. High data redundancy
Answer: B
Explanation:
Correlating external datasets might face challenges due to data format compatibility issues between different sources.
NEW QUESTION # 174
A data engineering team is implementing a complex data transformation pipeline using Snowflake tasks and streams. They need to monitor the execution of these tasks, track dependencies, and identify potential bottlenecks in near real-time. Which approach provides the MOST comprehensive solution for logging and monitoring the task execution and dependencies within Snowflake?
A. Utilizing Snowflake's Event Tables to capture TASK-related events, combined with an external monitoring tool (e.g., AWS CloudWatch, Azure Monitor) to visualize task dependencies and execution metrics.
B. Simply enabling email notifications for task failures in Snowflake.
C. Relying solely on Snowflake's task history view (TASK_HISTORY) and manually analyzing the execution times and statuses of each task.
D. Implementing a complex DAG management system outside of Snowflake and triggering Snowflake tasks from that system, using the external system's logging capabilities.
E. Creating custom logging tables within Snowflake to record task start and end times, along with status codes, and then querying these tables for monitoring purposes.
Answer: A
Explanation:
Option C provides the most comprehensive solution. Event tables provide a centralized and structured way to capture task-related events. Integrating with an external monitoring tool allows for visualization of task dependencies and near real-time monitoring capabilities. Option A is manual and time-consuming. Option B requires custom development and maintenance. Option D is overly complex. Option E only addresses failures, not overall monitoring.
NEW QUESTION # 175
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