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| Section | Objectives |
|---|---|
| Security, Governance, and Data Sharing | - Access control and security
|
| Data Transformation and Analysis | - SQL-based transformations
|
| Data Modeling and Performance Optimization | - Performance tuning
|
| Snowflake Architecture and Data Platform Fundamentals | - Snowflake architecture concepts
|
| Data Loading and Unloading | - Data ingestion methods
|
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NEW QUESTION # 48
An organization needs to provide daily summaries of key metrics to its executive team via email. They currently have a complex SQL query that generates the report, but the query takes several minutes to run. The executive team needs the data before 8 AM daily. The data warehouse is also used for other reporting and analytics tasks throughout the day. Given these constraints, what is the MOST efficient and reliable approach to operationalize this report?
Answer: D
Explanation:
Creating a materialized view (B) is the most efficient approach. Materialized views pre-compute the results, so the nightly task refreshes the pre-calculated data. This ensures fast report generation before 8 AM, even with a complex underlying query. Running the complex query directly (A) might exceed the time limit. Using a third-party ETL tool (C) adds unnecessary complexity. Snowpipe (D) is for continuous data ingestion, not report generation. Data Marketplace (E) is unlikely to perfectly match the custom needs.
NEW QUESTION # 49
A data analyst is tasked with optimizing a query that aggregates data from a table 'ORDERS' containing order details, including columns like 'ORDER ID', 'CUSTOMER ID, 'ORDER DATE, 'PRODUCT ID', and 'QUANTITY. The query calculates the total quantity of products ordered per customer and month. The current query is as follows: SELECT CUSTOMER ID, DATE TRUNC('MONTH', ORDER DATE) AS ORDER MONTH, SUM(QUANTITY) AS TOTAL QUANTITY FROM ORDERS GROUP BY CUSTOMER_ID, ORDER_MONTH ORDER BY CljSTOMER_lD, ORDER_MONTH; Deopite the 'ORDERS' table being relatively small (10 million rows), the query performance is slow. The analyst suspects a poorly chosen warehouse size. Which of the following actions, combined with monitoring query execution, would be MOST beneficial to determine the optimal warehouse size and improve query performance?
Answer: C
Explanation:
The most beneficial approach is to start with the smallest warehouse size and incrementally increase it (B). This allows for observing the impact of warehouse size on query performance and cloud services usage. Increasing until the query time plateaus or cloud services usage increases significantly indicates the point of diminishing returns. Simply using the largest size (A) may be wasteful, and ignoring cloud services usage (C) can lead to cost overruns. Query history (D) may not be relevant if the query is significantly different. Setting a timeout (E) will not optimize the warehouse size.
NEW QUESTION # 50
You are tasked with building a dashboard that visualizes website traffic data stored in Snowflake. The data includes daily unique visitors, bounce rate, and average session duration. The business stakeholders want to understand the correlation between these metrics. They also want to identify any outliers or anomalies. Which chart type is BEST suited for identifying correlation and outliers in this dataset?
Answer: C
Explanation:
A scatter plot matrix displays the pairwise relationships between multiple variables. This allows for easy identification of correlations (positive, negative, or none) and outliers in the data. Line charts are good for showing trends over time, but not for directly visualizing correlations between different metrics. Bar charts compare average values, and pie charts show proportions. Histograms helps to show single distribution only.
NEW QUESTION # 51
How does leveraging Time Travel feature in Snowflake aid in query optimization and historical data analysis?
Answer: B
Explanation:
Time Travel feature allows querying historical data versions, facilitating historical data analysis and retrospective query optimizations based on past data states.
NEW QUESTION # 52
A Data Analyst creates and populates the following table:
create or replace table aggr(v int) as select * from values (1), (2), (3), (4); The Analyst then executes this query:
select percentile_disc(0.60) within group (order by v desc) from aggr;
What will be the result?
Answer: A
Explanation:
The PERCENTILE_DISC (discrete percentile) function is an inverse distribution function that assumes a discrete distribution model. It takes a percentile value and a sort specification and returns the value from the set that corresponds to that percentile. Unlike PERCENTILE_CONT, which interpolates between values to find a continuous result, PERCENTILE_DISC always returns an actual value from the input set.
In this scenario, we have a set of four values: $\{1, 2, 3, 4\}$. The query specifies a descending order (order by v desc), so the ordered set for the calculation is $\{4, 3, 2, 1\}$.
To find the discrete percentile, Snowflake calculates the cumulative distribution. For a set of $N$ elements, each element represents a percentile rank of $1/N$. With 4 elements, each covers 25% ($0.25$) of the distribution:
* Value 4: Cumulative Percentile $0.25$
* Value 3: Cumulative Percentile $0.50$
* Value 2: Cumulative Percentile $0.75$
* Value 1: Cumulative Percentile $1.00$
The PERCENTILE_DISC(0.60) function looks for the first value whose cumulative distribution is greater than or equal to the specified percentile ($0.60$).
* $0.25$ (Value 4) is not $\ge 0.60$.
* $0.50$ (Value 3) is not $\ge 0.60$.
* $0.75$ (Value 2) is the first value where the cumulative distribution is $\ge 0.60$.
Therefore, the result is 2. If the order had been ascending (ASC), the cumulative distribution would have been
$\{1: 0.25, 2: 0.50, 3: 0.75, 4: 1.00\}$, and the result for $0.60$ would have been 3. Understanding the impact of the ORDER BY clause within the WITHIN GROUP syntax is a critical skill for the Data Analysis domain of the SnowPro Advanced: Data Analyst exam.
NEW QUESTION # 53
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