[2026] Pass Snowflake DAA-C01 Exam in First Attempt Easily
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NEW QUESTION # 37
You're working with product catalog data in Snowflake. The product information is stored in a table named 'PRODUCTS' , and a key attribute, 'attributes' , contains a semi-structured JSON object for each product. This 'attributes' object can have varying keys, but you are interested in extracting specific keys and pivoting them into columns. The relevant JSON structure is as follows : { "color": "red", "size": "L", "material": "cotton", "style": "casual"} '"What method is the MOST efficient to transform this data to a relational structure, assuming you want to analyze product attributes such as 'color' and 'size' as separate columns?
- A. Using LATERAL FLATTEN to unnest the 'attributes' and then using a CASE statement to pivot the data.
- B. Using dynamic SQL to generate a query that extracts the required attributes using JSON path accessors and then creates a new table.
- C. Using a stored procedure to iterate through each row, parse the JSON, and update a new table with pivoted columns.
- D. Creating a new table with a 'VARIANT column for the attributes and performing transformations in a BI tool.
- E. Creating a view with direct JSON path accessors (e.g., for each desired attribute.
Answer: E
Explanation:
Option B is the most efficient. Directly accessing the JSON elements using path accessors like allows Snowflake to optimize the query execution, which typically offers superior performance compared to flattening and pivoting with 'CASE statements. Flattening (Option A) introduces unnecessary complexity and overhead when specific attributes are known and desired. Options C and D are generally inefficient and should be avoided for this type of transformation. Creating a view is more performant and simple. Option E is overkill and introduces complexity that isn't needed since the required attributes are known.
NEW QUESTION # 38
When enriching data with Snowflake Marketplace, what role do data shares play in joining external data with existing datasets?
- A. Data shares facilitate secure data exchange between parties.
- B. They limit the types of data that can be joined.
- C. Data shares only work with Snowflake-provided datasets.
- D. They restrict access to external data.
Answer: A
Explanation:
Data shares enable secure data exchange, allowing joining external data with existing datasets.
NEW QUESTION # 39
When managing Snowsight dashboards, what role do subscriptions and updates play in meeting business requirements?
- A. They enhance dashboard usability without impacting data updates.
- B. Subscriptions and updates don't impact dashboard management.
- C. Subscriptions and updates ensure timely information delivery.
- D. Managing subscriptions and updates complicates dashboard usage.
Answer: C
Explanation:
Subscriptions and updates ensure timely information delivery, meeting business requirements.
NEW QUESTION # 40
When performing a diagnostic analysis, which actions aid in collecting related data to identify anomalies? (Select all that apply)
- A. Focusing solely on recent data for insights
- B. Collecting data from various sources
- C. Ignoring statistical trends for focused analysis
- D. Analyzing data in isolation
Answer: B
Explanation:
Collecting data from various sources aids in comprehensive analysis, assisting in identifying anomalies.
NEW QUESTION # 41
A company ingests sensor data into a Snowflake table named READINGS with columns (VARCHAR), 'reading_time' (TIMESTAMP NTZ), and 'raw_value' (VARCHAR). The 'raw_value' column contains numeric data represented as strings, but sometimes includes non-numeric characters (e.g., '123.45', 'N/A', '500'). You need to calculate the average of the numeric raw_value' readings for each within the last hour, excluding invalid readings. Which of the following Snowflake SQL statements will correctly accomplish this, handling potential conversion errors and filtering for valid data?
- A. SELECT sensor_id, FROM SENSOR_READINGS WHERE reading_time DATEADD(hour, -1 , AND TRY_TO IS NOT NULL GROUP BY sensor_id;
- B. SELECT sensor_id, raw_value, NULL))) FROM SENSOR_READINGS WHERE reading_time DATEADD(hour, -1, CURRENT TIMESTAMP()) GROUP BY sensor_id;
- C. SELECT sensor_id, FROM SENSOR_READINGS WHERE reading_time DATEADD(hour, -1, CURRENT _ TIMESTAMP()) GROUP BY sensor id;
- D. SELECT sensor_id, 'N/A'))) FROM SENSOR_READINGS WHERE reading_time DATEADD(hour, -1 , CURRENT TIMESTAMP()) GROUP BY sensor_id;
- E. SELECT sensor_id, AVG(CASE WHEN THEN ELSE NULL END) FROM SENSOR_READINGS WHERE reading_time DATEADD(hour, -1, CURRENT TIMESTAMP()) GROUP BY sensor_id;
Answer: A
Explanation:
Option B is the correct answer because 'TRY TO NUMBER attempts to convert the 'raw_value' to a number, returning NULL if the conversion fails. The 'AND TRY_TO_NUMBER(raw_value) IS NOT NULL' clause then filters out these NULL values, ensuring only valid numeric readings are included in the average calculation. Option A will throw an error if it encounters a non-numeric value. Option C, while functionally correct, utilizes which can be less reliable for specific locale formats compared to Option D is unnecessarily complex and less readable. Option E only handles 'N/A', not other potential invalid values.
NEW QUESTION # 42
Consider a scenario where you are building a dashboard to monitor the performance of a marketing campaign. The data includes daily ad spend, website conversions, and cost per acquisition (CPA). The stakeholders need to quickly assess whether the campaign is meeting its target CPA. What visualization type would be MOST appropriate to display the current CPA compared to the target CPA, providing a clear and concise view of performance?
- A. A scatter plot showing the relationship between ad spend and CPA.
- B. A Pie chart showing CPA percentage against target CPA percentage.
- C. A bar chart comparing the average CPA to the target CPA.
- D. A gauge chart displaying the current CPA and the target CPA, with color-coded zones indicating performance levels.
- E. A line chart showing the trend of CPA over time.
Answer: D
Explanation:
A gauge chart is specifically designed to display a single value (the current CPA) in relation to a target value (the target CPA). The color-coded zones provide an immediate indication of whether the campaign is performing well, needs improvement, or is failing. Line charts show trends, bar charts compare averages, and scatter plots show relationships. Pie chart showing CPA percentage against target CPA percentage do not accurately show CPA against target CPA, for better visualization gauge charts would be the preffered option
NEW QUESTION # 43
You have a large dataset in Snowflake containing customer order information stored in a table named 'ORDERS' with columns 'ORDER_ID' ONT), 'CUSTOMER_ID' ONT), 'ORDER_DATE (DATE), 'TOTAL_AMOUNT' (FLOAT), and 'DISCOUNT_APPLIED' (BOOLEAN). You need to use Snowsight dashboards to analyze customer spending behavior and identify potential outliers. Which of the following visualizations, combined with appropriate SQL queries, would be MOST effective in identifying customers with unusually high or low order values? (Select TWO)
- A. Option C
- B. Option B
- C. Option D
- D. Option A
- E. Option E
Answer: A,D
Explanation:
Options A and C are the most effective. A Box Plot (A) is ideal for identifying outliers in a distribution. By visualizing the distribution of total order amounts per customer, you can easily spot customers with unusually high or low spending. A Scatter Plot (C) directly shows the relationship between customer ID and total spending, making it easy to visually identify outliers based on their position relative to other data points. Option B is more suitable for trend analysis over time, and options D and E are useful but don't directly highlight individual customer outliers in terms of order value.
NEW QUESTION # 44
A marketing team wants to visualize website traffic data in Snowsight. They have a table named 'WEBSITE TRAFFIC' with columns 'VISIT DATE' (DATE), 'PAGE URL' (VARCHAR), and 'VISITOR COUNT' (INTEGER). The team wants to create a line chart showing the daily visitor count for a specific page, filtered by date range, allowing users to dynamically select the page URL from a dropdown. Which of the following steps are MOST effective and correct to achieve this using Snowsight dashboards?
- A. Create a view 'DAILY PAGE_VISITS using a SQL query like 'CREATE OR REPLACE VIEW DAILY PAGE_VISITS AS SELECT VISIT DATE, PAGE URL, AS TOTAL_VISITS FROM WEBSITE_TRAFFIC GROUP BY VISIT_DATE, PAGE_URL;'. Then, in Snowsight, create a dashboard and a line chart using this view with a filter on 'PAGE URL' and 'VISIT DATE', configured as dashboard variables.
- B. Create a new chart in a Snowsight dashboard. Use a SQL query like 'SELECT VISIT_DATE, FROM WEBSITE_TRAFFIC WHERE PAGE URL = 'specific_page' AND VISIT DATE BETWEEN 'start_date' AND 'end_date' GROUP BY VISIT DATE ORDER BY VISIT DATE$. Manually update the query for each page.
- C. Create a stored procedure in Snowflake that accepts 'start_date', 'end_date' , and as parameters, executes the query, and returns the result set. Call the stored procedure from the Snowsight dashboard, passing the filter values.
- D. Create a materialized view using 'CREATE MATERIALIZED VIEW AS SELECT VISIT_DATE, PAGE_URL, AS TOTAL VISITS FROM WEBSITE_TRAFFIC GROUP BY VISIT DATE, PAGE URL;'. In Snowsight, create a dashboard and a line chart using this materialized view with a filter on and configured as dashboard variables.
- E. Write a Python script using the Snowflake Connector to query the data, process it using Pandas, and then generate a chart using Matplotlib. Embed the chart as an image in the Snowsight dashboard.
Answer: A,D
Explanation:
Option B is the most effective because it creates a view that aggregates the data, and then utilizes Snowsight dashboard variables for filtering, providing a dynamic and user-friendly experience. Option D is also correct because using a materialized view will significantly improve the performance of the query, especially when dealing with large datasets. Materialized views pre-compute and store the results of the query, making it faster to retrieve the data for the dashboard. Option A is inefficient as it requires manual updates to the query. Option C introduces unnecessary complexity and doesn't leverage Snowflake's built-in capabilities. Option E is overkill and less efficient for this simple scenario.
NEW QUESTION # 45
In performing data discovery to identify necessary elements from available datasets, what role do metadata play in this process?
- A. Metadata provides insights into data structure only.
- B. Metadata impacts data transformation processes.
- C. Metadata helps in data lineage understanding.
- D. Metadata has no role in data discovery.
Answer: C
Explanation:
Metadata aids in understanding data lineage, contributing to the identification of necessary elements from datasets.
NEW QUESTION # 46
What considerations are crucial when performing table joins between parent/child tables for maintaining data integrity? (Select all that apply)
- A. Data redundancy prevention
- B. Establishing foreign key constraints
- C. Matching data types
- D. Ensuring referential integrity
Answer: B,C,D
Explanation:
Table joins require considerations such as matching data types, establishing foreign key constraints, and ensuring referential integrity for data integrity maintenance.
NEW QUESTION # 47
When creating reports and dashboards, how does evaluating data based on business requirements impact the visualization process?
- A. Evaluating data ensures relevant and useful dashboard content.
- B. It limits data selection, affecting overall dashboard quality.
- C. Business requirements have no impact on data selection for visualization.
- D. Evaluating data complicates dashboard creation.
Answer: A
Explanation:
Evaluating data based on business requirements ensures the dashboard contains relevant and useful content, improving its quality.
NEW QUESTION # 48
A Data Analyst has been asked to predict sales revenue through the end of the year. Which function will provide this information?
- A. CORR
- B. REGR_SLOPE
- C. COVAR_SAMP
- D. VARIANCE
Answer: B
Explanation:
To predict future values based on historical data, an analyst must determine the mathematical relationship between two variables-typically time (independent variable) and revenue (dependent variable). This is the foundation of linear regression.
The REGR_SLOPE function is a linear regression function that calculates the slope of the "least squares" regression line for non-null pairs in a group. In the context of sales forecasting, the "slope" represents the rate of change in revenue over time. By calculating the slope, an analyst can project that trend forward to estimate what the revenue will be at a future date (the end of the year).
Evaluating the Options:
* Option A (CORR) measures the correlation coefficient, which tells you how strongly two variables are related (between -1 and 1), but it does not provide a mathematical formula to predict a specific future value.
* Option C (COVAR_SAMP) calculates the sample covariance, which indicates the direction of a linear relationship but not the magnitude or slope required for prediction.
* Option D (VARIANCE) is a descriptive statistic that measures data spread (how far numbers are from the mean) and is not used for trend projection or prediction.
* Option B is the 100% correct answer. Along with REGR_INTERCEPT, REGR_SLOPE allows the analyst to build the linear equation $y = mx + b$ to perform predictive analytics.
NEW QUESTION # 49
You are tasked with diagnosing a performance bottleneck in a daily ETL process that loads data into a Snowflake table called 'SALES DATA'. The ETL process has been running slower than usual for the past week. You suspect a change in the source data volume or distribution. Which of the following Snowflake features and SQL queries would be MOST helpful in identifying the root cause?
- A. Query Snowflake's INFORMATION SCHEMA.QUERY HISTORY view to compare the execution times of recent ETL runs with historical averages, filtering by query ID or user.
- B. Analyze the 'SALES DATA' table's clustering keys and statistics using 'SHOW TABLES LIKE 'SALES DATA';' and 'DESCRIBE TABLE SALES DATA;' to determine if the data distribution has changed significantly, potentially leading to inefficient query performance.
- C. Use Snowflake's Time Travel feature to compare the size and structure of the 'SALES DATA table at different points in time, specifically before and after the performance degradation started.
- D. Run 'SELECT COUNT( ) FROM SALES DATA;' to check total record count and compare against historical values.
- E. Use Snowflake's Query Profile feature to analyze the execution plan of the ETL queries and identify the stages consuming the most time.
Answer: A,B,C,E
Explanation:
Options A, B, C, and D are all helpful in diagnosing the bottleneck. The query profile (A) pinpoints specific query stages, query history (B) reveals performance trends, table statistics (C) indicate data skewness, and Time Travel (D) allows for data structure comparisons. While E is useful as an initial check, it is not sufficient to determine the root cause alone.
NEW QUESTION # 50
In what ways do materialized views enhance performance in data analysis compared to regular views?
- A. Materialized views provide precomputed snapshots, reducing query computation.
- B. Regular views simplify complex data structures more effectively.
- C. They restrict data accessibility for improved security.
- D. Materialized views offer real-time updates, unlike regular views.
Answer: A
Explanation:
Materialized views provide precomputed snapshots, improving query performance by reducing computation.
NEW QUESTION # 51
A data analyst needs to enrich customer data in a Snowflake database with demographic information obtained from the Snowflake Marketplace. The purchased listing provides data as a secure view Which of the following SQL commands is the MOST efficient and secure way to create a new table in the data analyst's database that combines customer data with the demographic information from the Marketplace listing, while ensuring that only necessary columns from the Marketplace data are included?
- A.
- B.
- C.
- D.
- E.
Answer: B
Explanation:
Option D is the most efficient and secure because: it creates a new table or replaces if one exists, it explicitly selects only the necessary columns (age, income) from the Marketplace view, preventing unnecessary data exposure. It also explicitly uses JOIN condition. A new table has to created instead of view for persistence and to prevent recomputing of the data on frequent requests.
NEW QUESTION # 52
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: C
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 # 53
When considering row access policies and Dynamic Data Masking in Snowflake, how do they influence data visibility and security?
- A. Only apply to certain data types
- B. Dynamically mask sensitive data for specific users
- C. Limit data access based on user roles
- D. Grant unrestricted access to all data
Answer: B,C
Explanation:
Row access policies and Dynamic Data Masking play vital roles in limiting data access based on user roles and dynamically masking sensitive data to enhance data security and visibility for authorized users.
NEW QUESTION # 54
What role do system functions play in data manipulation within Snowflake?
- A. System functions perform administrative tasks.
- B. System functions manage query execution plans.
- C. They solely work on numeric data.
- D. They modify database structures.
Answer: A
Explanation:
System functions in Snowflake perform administrative tasks, aiding in data manipulation and management.
NEW QUESTION # 55
You have two tables in Snowflake: 'ORDERS and 'CUSTOMERS. The 'ORDERS' table contains information about customer orders, including 'ORDER ID', 'CUSTOMER ID', 'ORDER DATE, and 'ORDER AMOUNT. The 'CUSTOMERS table contains information about customers, including 'CUSTOMER ID', 'CUSTOMER NAME' , and 'CUSTOMER ADDRESS'. You need to create a view that joins these two tables based on 'CUSTOMER ID and includes only orders placed in the last 30 days. You also want to ensure that the view leverages the primary key information defined on the 'CUSTOMERS' table (even though Snowflake doesn't enforce it) for potential query optimizations. Which of the following SQL statements is the MOST efficient and best practice approach, considering Snowflake's optimizer?
- A. Option A
- B. Option B
- C. Option C
- D. Option D
- E. Option E
Answer: E
Explanation:
Using 'CREATE OR REPLACE SECURE VIEW' is the best practice. 'CREATE OR REPLACE' allows you to update the view definition if needed without dropping and recreating it, minimizing disruption. A 'SECURE VIEW' prevents users from seeing the underlying query logic or accessing the base tables directly, enhancing security. The explicit JOIN syntax is preferred over the older implicit join syntax (option B). Snowflake's optimizer will leverage defined primary key relationships to optimize the join, even though it doesn't enforce them. Explicitly referencing schema isn't needed unless dealing with ambiguous names across schemas. The WHERE clause correctly filters for orders in the last 30 days.
NEW QUESTION # 56
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