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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Transformation and Data Modeling | 22%-30% | - Transform data using SQL
|
| Topic 2: Data Ingestion and Data Preparation | 15%-20% | - Prepare data and load into Snowflake
|
| Topic 3: Data Analysis | 30%-32% | - Perform advanced analytics using SQL
|
| Topic 4: Data Presentation and Data Visualization | 28%-29% | - Integrate with BI tools
|
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NEW QUESTION # 49
When maintaining reports and dashboards, why is it essential to configure subscriptions and updates?
Answer: C
Explanation:
Subscriptions and updates ensure timely information delivery, a crucial aspect of maintaining reports and dashboards.
NEW QUESTION # 50
You are working with a table named 'PRODUCT DESCRIPTIONS that contains product descriptions in a 'description' (VARCHAR) column. You need to implement a solution to identify potentially sensitive information within these descriptions, specifically looking for mentions of credit card numbers or social security numbers (SSNs). You want to flag any description that contains either of these patterns. Which of the following Snowflake SQL snippets, leveraging scalar string functions and regular expressions, provides the most efficient and accurate way to achieve this? (Assume that valid credit card numbers are 16 digits and valid SSNs are in the format 'XXX-XX-XXXX'). Select all correct options.
Answer: A,B
Explanation:
Options A and C are both correct. Option A uses 'REGEXP_LIKE with two separate regular expressions to search for a 16-digit number (credit card) and a 'XXX-XX-XXXX' pattern (SSN). Option C uses a single 'REGEXP LIKE' function with an alternation to combine both patterns into one regular expression , which is generally more efficient. Option B relies on 'CONTAINS' which performs a simple substring search, not regular expression matching; this will not accurately identify the patterns. Option D uses 'STARTS WITH' which will only identify descriptions that begin with the specified keywords, missing most cases. Option E uses 'LIKE operator for the mentioned words in the sentence that can be 'Credit Card' or 'Social Security Number', but this isn't a search for Credit card numbers or SSNs.
NEW QUESTION # 51
A Data Analyst created a SQL statement that updates a table used for reporting. The Analyst now wants to automate the execution of that SQL.
The Analyst decides to use a task for this along with a stream called MYSTREAM on the source table so only they can update the table if there is new data.
Which statement will create a task that will execute when there is new data in MYSTREAM without having to be executed manually?
Answer: A
Explanation:
To automate a data pipeline in Snowflake using Tasks and Streams, an analyst must understand the relationship between scheduling and conditional execution. A task is an object used to execute a single SQL statement (or a call to a stored procedure) on a recurring basis. A stream, on the other hand, tracks Change Data Capture (CDC) metadata for a table, indicating whether new rows have been added, updated, or deleted.
The core requirement of this question is to ensure the task runs automatically but only if there is new data. In Snowflake, a task requires a SCHEDULE parameter to run automatically without manual intervention. The SCHEDULE defines the frequency (e.g., '5 MINUTE' or a CRON expression) at which the task "attempts" to run. However, to prevent wasting credits by running the task when no data has changed, the WHEN clause is used in conjunction with the system function SYSTEM$STREAM_HAS_DATA('stream_name').
Evaluating the Options based on the exhibit (image_8c9a13.png):
Option A is incorrect because it lacks a SCHEDULE. Without a schedule, the task is considered "standalone" and will never execute unless manually triggered by an EXECUTE TASK command.
Option B is incorrect because it incorrectly attempts to place the SYSTEM$STREAM_HAS_DATA function within the SCHEDULE parameter. The schedule must be a time-based interval or a cron string.
Option C is incorrect because 'STREAM' is not a valid schedule interval.
Option D is the 100% correct answer. It provides a valid time-based SCHEDULE ('2 minute') which enables the task to heart-beat automatically. It then correctly utilizes the WHEN clause with SYSTEM$STREAM_HAS_DATA('MYSTREAM'). This configuration ensures that every two minutes, Snowflake checks the stream; if the stream contains data, the task executes the SQL; if the stream is empty, the task skips the execution, saving compute resources. This pattern is the industry standard for building efficient, automated Data Transformation pipelines in the Snowflake Data Cloud.
NEW QUESTION # 52
You are tasked with analyzing website traffic patterns using Snowflake. The data is stored in a table 'WEB TRAFFIC' with columns 'VISIT DATE (DATE) and 'PAGE_VIEWS (NUMBER). You need to identify anomalies (unusually high or low traffic days) using the 'STDDEV POP function to calculate the standard deviation and flag days that fall outside a certain number of standard deviations from the mean. Which of the following SQL queries BEST implements this anomaly detection logic, flagging days with page views more than 2 standard deviations above the mean?
Answer: E
Explanation:
Option D correctly uses window functions to calculate the average and standard deviation across the entire dataset without grouping, then filters the rows where 'PAGE_VIEWS' are more than 2 standard deviations above the mean. Window functions (OVER ()) allow aggregate calculations without collapsing the rows, making it suitable for anomaly detection on a row-by-row basis. Option A and C incorrectly tries to use aggregate function without group by condition, and option B without CTE its creating ambiguity. Option E has logical issue.
NEW QUESTION # 53
You are analyzing sales data from different regions stored in a Snowflake table named 'sales_data'. The table includes columns: 'transaction_id' (VARCHAR), 'region' (VARCHAR), 'sale_date' (DATE), and 'sale_amount' (NUMBER). You discover the following data quality issues: The 'region' column contains inconsistent entries such as 'North', 'north', 'NOrth ', and ' South'. The 'sale_amount' column has some values that are stored as strings (e.g., '100.50') instead of numbers, causing errors in aggregation. There are duplicate records identified by the same 'transaction id'. Which set of SQL statements, executed in the given order, provides the MOST effective and efficient way to address these data quality issues in Snowflake?
Answer: A
Explanation:
Option E presents the most efficient and effective solution. It combines all three data cleaning steps into a single operation using a CTE. First, standardizes the region name with trim and lowercase. Second, remove duplicate records based on transaction ID. And most important, it correctly handles the 'sale_amount' conversion using TRY_TO_NUMBER inside the CTE to avoid errors and ensures accurate aggregations down stream. This approach minimizes the number of table scans and UPDATE operations, improving performance. Option A fails on how to remove duplicates correctly using TRY_TO_NUMBER to convert the sale amount correctly and data type changes are not possible via ALTER statements if strings are present. Options B, C, and D does not combine all in one single CTE operations and are slower.
NEW QUESTION # 54
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