2025 Updated Verified Pass DAA-C01 Exam - Real Questions & Answers [Q104-Q124]

2025 Updated Verified Pass DAA-C01 Exam - Real Questions and Answers

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NEW QUESTION # 104
You have created a forecasting model in Snowflake to predict customer churn based on historical data'. The model is named 'CHURN FORECAST MODEL'. After running the forecast, you need to evaluate the model's performance and understand its accuracy.
Which of the following methods or SQL commands can you use to accomplish this effectively?

• A. Use the 'DESCRIBE MODEL CHURN FORECAST MODEL' command to retrieve model statistics such as the features used and their importance in the model.
• B. Use the 'SELECT FROM command to get a summary of the model's performance, including error metrics. Additionally, manually compare the forecasted values to the actual values to calculate error metrics.
• C. Use the 'SHOW MODELS command to view the model's metadata, including accuracy metrics like Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE).
• D. There is no native way to evaluate forecasting model performance in Snowflake. You must manually calculate error metrics by comparing the forecast results with actual historical data using SQL queries.
• E. Use the function to download the model's internal representation and analyze it using external machine learning libraries.

Answer: B

Explanation:
Option E is the most comprehensive approach. The command 'SELECT FROM MODEL', EXPLAIN is the appropriate command to view model performance metrics. While snowflake can get summary stats. It's best to manually calculate error metrics by comparing the forecasted values with the actual historical data, that ensures a comprehensive evaluation. 'SHOW MODELS' command does not provide detailed accuracy metrics. 'SYSTEM$GET_MODEL' is not intended for model evaluation. 'DESCRIBE MODEL' provides details about model features but not the statistical performance. Snowflake supports built-in forecasting, so model calculation error is unnecessary.

NEW QUESTION # 105
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: E

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 # 106
What distinguishes Materialized views from Secure views in the context of data analysis?

• A. Materialized views enhance data security, while Secure views offer improved query performance.
• B. Materialized views restrict data access for security purposes, unlike Secure views.
• C. Secure views provide enhanced data security without precomputing data.
• D. Secure views provide a precomputed snapshot of data, unlike Materialized views.

Answer: C

Explanation:
Secure views offer enhanced data security without precomputing data, distinguishing them from Materialized views.

NEW QUESTION # 107
Consider a 'customer_orders' table with 'customer_id' , 'order_date', and 'order_amount'. You need to identify customers who have placed orders consistently over the last 3 months, specifically, you need to find customers who have placed an order in each of the last 3 months (including the current month). Assume the current date is '2024-01-15'. Which of the following query snippets, when incorporated into a complete query, would be most efficient and accurate for identifying these customers?

• A.
• B.
• C.
• D.
• E.

Answer: E

Explanation:
Option E is the most precise and efficient. It explicitly checks if a customer has an order in each of the three specific months (November, December, January). It does this by truncating the 'order_date' to the beginning of the month using 'DATE TRUNC('MONTH', order_datey and then comparing against the truncated values for the last three months calculated using 'DATEADD. The 'SUM' will only be equal to 3 if the customer has at least one order in each of those months. Option A calculates the number of distinct months for each customer but doesn't guarantee they are the last 3 months. Option B checks if the customer has placed at least 3 orders in the last 3 months, but it might be that all 3 orders are in a single month. Option C doesn't count distinct months. Option D only returns 1 if the customer has placed an order in the last 3 months. It does not guarantee the customer placed an order in all the past 3 months.

NEW QUESTION # 108
A data analyst needs to process a large JSON payload stored in a VARIANT column named 'payload' in a table called 'raw events' The payload contains an array of user sessions, each with potentially different attributes. Each session object in the array has a 'sessionld' , 'userld' , and an array of 'eventS. The events array contains objects with 'eventType' and 'timestamp'. The analyst wants to use a table function to flatten this nested structure into a relational format for easier analysis. Which approach is most efficient and correct for extracting and transforming this data?

• A. Employ a combination of LATERAL FLATTEN and Snowpark DataFrames, using LATERAL FLATTEN to partially flatten the JSON and then Snowpark to handle the remaining complex transformations and data type handling.
• B. Use LATERAL FLATTEN with multiple levels of nesting, specifying 'path' for each level and directly selecting the desired attributes.
• C. Utilize a Snowpark DataFrame transformation with multiple 'explode' operations and schema inference to flatten the nested structure and load data into a new table.
• D. Load the JSON data into a temporary table, then write a series of complex SQL queries with JOINs and UNNEST operations to flatten the data.
• E. Create a recursive UDF (User-Defined Function) in Python to traverse the nested JSON and return a structured result, then call this UDF in a SELECT statement.

Answer: B

Explanation:
Option A is the most efficient and Snowflake-native approach. LATERAL FLATTEN is optimized for handling nested data structures within Snowflake. While other options might work, they introduce overhead (UDF execution), are less efficient (temporary tables and complex SQL), or rely on external frameworks (Snowpark), making them less suitable for this scenario. Specifying the path ensures specific fields are targeted, avoiding unnecessary processing of irrelevant data. LATERAL flatten allows you to join the output of a table function with each row of the input table. This is essential to maintain the context (e.g., userId) from the outer table.

NEW QUESTION # 109
Your organization is migrating its data warehouse to Snowflake. You need to monitor the resource consumption of different users. You want to identify which users are running the most expensive queries (in terms of credits consumed) over the last 7 days. You need to create a query using system functions to achieve this. Which of the following queries will accurately provide this information?

• A. SELECT user_name, SUM(credits_used_compute) AS total_credits_used FROM snowflake.account_usage.query_history WHERE start_time DATEADD(day, -7, CURRENT TIMESTAMP()) GROUP BY user_name ORDER BY DESC;
• B. SELECT user_name, SUM(credits_used) AS total_credits_used FROM snowflake.account_usage.query_history WHERE start_time DATEADD(day, -7, GROUP BY user_name ORDER BY DESC;
• C. SELECT user_name, SUM(credits_used_cloud_services) AS total_credits_used FROM snowflake.account_usage.query_history WHERE start_time >=DATEADD(day, -7, CURRENT TIMESTAMP()) GROUP BY user_name ORDER BY DESC;
• D. SELECT user_name, SUM(credits_used) AS total_credits_used FROM snowflake.account_usage.warehouse_metering_history WHERE start_time >= DATEADD(day, -7, CURRENT TIMESTAMP()) GROUP BY user_name ORDER BY DESC;
• E. SELECT user_name, SUM(credits_used) AS total_credits_used FROM snowflake.account_usage.execution_history WHERE start_time >= DATEADD(day, -7, GROUP BY user_name ORDER BY DESC;

Answer: A

Explanation:
The correct query should use the 'snowflake.account_usage.query_history' view and specifically sum the column as this reflects the credits used for the compute resources by query. Option C accurately reflects this. Option A is incorrect because the 'credits_used' column in 'query_history' does not give a direct credit consumption cost based on computation. Option B uses 'execution_history' which does not aggregate the same detailed credit usage information. Option D sums credits used for cloud services not the compute. Option E references warehouse metering history, not specific users' query execution history.

NEW QUESTION # 110
A logistics company needs to determine which warehouses are within a 50km radius of a new distribution center. The warehouse locations are stored in a table 'WAREHOUSES' with columns 'WAREHOUSE ID' ONT), (GEOGRAPHY) and the distribution center's location is stored in a variable of type GEOGRAPHY. Which query will efficiently identify all warehouses within the specified radius, returning the 'WAREHOUSE ID and distance in kilometers?

• A. Option C
• B. Option E
• C. Option D
• D. Option B
• E. Option A

Answer: B

Explanation:
The correct answer uses 'ST DWITHIN' with the correct parameters and unit. 'ST DWITHIN(LOCATION, @distribution_center, 50000)' correctly filters the warehouses based on the 50km (50000 meters) radius. ST_DISTANCE calculates the distance in meters, which is then converted to kilometers by dividing by 1000. The warehouse location should come first followed by the distribution centre in the DWITHIN' Function.

NEW QUESTION # 111
How do materialized views differ from regular views in terms of data storage and computation?

• A. Regular views provide precomputed snapshots, unlike materialized views.
• B. Regular views provide precomputed snapshots for improved query performance.
• C. Materialized views simplify complex data structures for better computation.
• D. Materialized views restrict data storage for better computation.

Answer: A

Explanation:
Materialized views provide precomputed snapshots, differentiating them from regular views.

NEW QUESTION # 112
In performing data discovery to identify necessary elements from available datasets, what role do metadata play in this process?

• A. Metadata has no role in data discovery.
• B. Metadata helps in data lineage understanding.
• C. Metadata provides insights into data structure only.
• D. Metadata impacts data transformation processes.

Answer: B

Explanation:
Metadata aids in understanding data lineage, contributing to the identification of necessary elements from datasets.

NEW QUESTION # 113
A retail company uses Snowflake to store sales data'. They want to build a dashboard in Tableau to analyze regional sales performance. The sales data is stored in a table called 'SALES DATA' with columns 'REGION', 'PRODUCT CATEGORY, 'SALE AMOUNT, and 'SALE DATE. They want to optimize the Tableau dashboard's performance when querying Snowflake. Which of the following Snowflake features, when correctly implemented, will MOST effectively improve the query speed of the dashboard?

• A. Creating a standard Snowflake view directly querying 'SALES DATA' and connecting Tableau to that view.
• B. Using a stored procedure in Snowflake to calculate and store aggregated sales data in a separate table, and connecting Tableau to this aggregated table.
• C. Using Tableau's data extract feature to import all 'SALES DATA into a Tableau Hyper file and connecting the dashboard to the extract.
• D. Implementing Snowflake's Data Marketplace to source external sales data, which Tableau can then directly connect to without needing to access the company's 'SALES DATA'.
• E. Creating a materialized view on top of 'SALES_DATA' , pre-aggregating sales data by REGION' and 'PRODUCT_CATEGORY , and connecting Tableau to the materialized view.

Answer: E

Explanation:
Materialized views in Snowflake are designed to pre-compute and store the results of a query, significantly reducing the query execution time when the same query is run again. By pre-aggregating the sales data, the Tableau dashboard can retrieve the required aggregated data much faster than querying the entire table each time. Option A will still query the base table. Option C bypasses Snowflake entirely, which may not be desired. Option D is irrelevant to the problem stated. Option E involves a more complex setup and maintenance compared to materialized views, making it less optimal.

NEW QUESTION # 114
You are analyzing sales data in Snowflake to identify seasonal trends and patterns. You have a table 'SALES DATA with columns 'SALE DATE (DATE) and 'SALE_AMOUNT (NUMBER). Which of the following SQL queries and visualization techniques would be MOST effective in identifying and visualizing these seasonal trends? Assume the data spans several years.

• A. Option D
• B. Option B
• C. Option E
• D. Option C
• E. Option A

Answer: D

Explanation:
Option C is the most effective because it combines weekly sales aggregation with a box plot analysis of monthly sales across multiple years. The weekly aggregation provides a granular view of sales trends, while the box plot effectively visualizes the distribution of sales for each month, allowing for easy identification of monthly seasonal patterns and outliers. Option A only shows monthly sales volume, not the distribution of sales within each month across years. Option B shows the yearly trend, not seasonal variations. Option D doesn't aggregate the data and hence can't show you the seasonality. Option E only shows the daily variance across weeks.

NEW QUESTION # 115
You have a table named 'event_data' that tracks user activities. The table contains 'event_id' (INT), 'user _ id' (INT), (TIMESTAMP NTZ), 'event_type' (VARCHAR), and 'event_details' (VARIANT). The table is partitioned by Performance on queries filtering by both 'event_type' and a specific date range on is slow You suspect inefficient partition pruning and JSON parsing as potential bottlenecks. Which combination of actions will most effectively address these performance issues?

• A. Add a masking policy on the 'event_details' column and recluster the table by 'user_id'.
• B. Create a materialized view partitioned by and clustered by 'event_type' , pre-extracting relevant fields from 'event_detailS into separate columns.
• C. Create a temporary table containing the results and then performing a Merge operation.
• D. Create a view that extracts specific fields from the 'event_details' column into separate columns and add a secondary index on 'event_type' .
• E. Change the partition key to 'event_type' and create a table function to query sevent_detailss.

Answer: B

Explanation:
Option B provides the most effective solution. Creating a materialized view addresses both problems: Partitioning by ensures efficient partition pruning when querying by date ranges. Clustering by 'event_type' improves performance when filtering on this column. Pre-extracting fields from sevent_detailS into separate columns avoids expensive JSON parsing at query time. Option A, adding index will not perform better than partition pruning. Option C changing partition key will require full reload of data and clustering table is expensive. Option D, masking policy will secure sensitive data but won't resolve performance issues. Option E, creating a temporary table and performing a Merge operation will increase cost and time.

NEW QUESTION # 116
A company stores web analytics data in a Snowflake table named 'WEB EVENTS. This table includes a 'USER ID column, a 'TIMESTAMP' column indicating when the event occurred, and a 'EVENT TYPE column that captures the type of event (e.g., 'page_view', 'add_to_cart', 'purchase'). The data analysts want to enrich this data to identify the first and last event times for each user. Which Snowflake features or functions would be MOST appropriate and efficient for achieving this enrichment?

• A. Creating a stored procedure that iterates through each user ID and finds the minimum and maximum timestamp using separate queries.
• B. Using a lateral view combined with a table function to find the first and last event times.
• C. Using a simple GROUP BY clause on 'USER ID to find the minimum and maximum timestamp.
• D. Using a correlated subquery to find the minimum and maximum timestamp for each user in the 'WEB EVENTS' table.
• E. Using window functions such as FIRST _ VALUE and 'LAST_VALUE partitioned by 'USER_ID and ordered by 'TIMESTAMP' to find the first and last event times.

Answer: E

Explanation:
Window functions are the most efficient approach for calculating aggregate values (like minimum and maximum) within partitions (in this case, per user) without requiring self-joins or subqueries. Correlated subqueries can be inefficient for large datasets. Stored procedures with iteration are generally slower than set-based operations. Lateral views are more suitable for exploding array structures, not for finding min/max values. A simple GROUP BY would provide the overall minimum and maximum, not per user.

NEW QUESTION # 117
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?

• A. A histogram showing the distribution of individual Metrics.
• B. A pie chart showing the percentage contribution of each metric to the total.
• C. A line chart showing each metric over time.
• D. A scatter plot matrix showing the pairwise relationships between all metrics.
• E. A bar chart comparing the average values of each metric.

Answer: D

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 # 118
Consider a Snowflake table 'USER EVENTS' with a 'VARIANT' column named 'event_data' containing JSON objects representing user activity. The JSON structure varies significantly across rows. You need to extract all the distinct event types from this data'. Which of the following Snowflake queries is the most efficient way to achieve this, handling potential null or missing 'event_type' fields gracefully and avoiding errors? Assume the volume of data is very large.

• A.
• B.
• C.
• D.
• E.

Answer: B

Explanation:
Option C, using , is the most efficient and robust solution. attempts to convert the JSON value to a string and returns NULL if the conversion fails (e.g., if is an object or array, not a string or a value that can be cast to a string). This avoids errors and simplifies the query. Using 'DISTINCT on the result then gives the distinct event types. Options A, B, D and E have the overhead of IS NULL or NVL functions, that make processing slower and inefficeint compared to C. While these options handle nulls, they are more verbose and potentially less performant due to the explicit null checks. Option A will also exclude rows where event_data:event_type is actually NULL, which might be undesirable.

NEW QUESTION # 119
What actions are involved in performing general DML (Data Manipulation Language) operations in Snowflake? (Select all that apply)

• A. Inserting new data
• B. Updating existing data
• C. Merging data from multiple tables
• D. Deleting data entirely

Answer: A,B,D

Explanation:
General DML operations in Snowflake include inserting, updating, and deleting data.

NEW QUESTION # 120
How does incorporating visualizations in reports and dashboards aid in presenting data for business use analyses?

• A. It limits data presentation to textual formats only.
• B. Presenting data visually doesn't impact business use analyses.
• C. Visualizations enhance data comprehension for effective analysis.
• D. Visualizations complicate data representation, hindering analysis.

Answer: C

Explanation:
Visualizations enhance data comprehension, aiding effective analysis in business use scenarios.

NEW QUESTION # 121
How do row access policies and Dynamic Data Masking affect the creation and maintenance of reports and dashboards?

• A. They enhance data visibility without any restrictions.
• B. Row access policies limit data visibility based on user privileges.
• C. Dynamic Data Masking doesn't impact dashboard creation or maintenance.
• D. Both policies restrict data visibility for better security.

Answer: B

Explanation:
Row access policies restrict data visibility based on user privileges, ensuring better security in creation and maintenance of reports and dashboards.

NEW QUESTION # 122
What considerations are essential when identifying the volume of data to be collected in a collection system? (Select all that apply)

• A. Frequency of data analysis
• B. Available storage capacity
• C. Speed of data retrieval
• D. Data redundancy requirements

Answer: A,B

Explanation:
Identifying the volume of data involves considering available storage capacity and the frequency of data analysis.

NEW QUESTION # 123
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?

• A. Option C
• B. Option B
• C. Option E
• D. Option A
• E. Option D

Answer: E

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 # 124
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