Nov-2025 Snowflake DAA-C01 Certification Real 2025 Mock Exam
DAA-C01 Exam Questions and Valid PMP Dumps PDF
NEW QUESTION # 17
When utilizing materialized views, what benefit do they offer in terms of query performance and data retrieval?
- A. Materialized views provide precomputed snapshots, improving query performance.
- B. They offer real-time updates reflecting instantaneous database changes.
- C. Materialized views restrict data retrieval for improved security.
- D. Regular views simplify complex data structures for better query performance.
Answer: A
Explanation:
Materialized views provide precomputed snapshots, enhancing query performance.
NEW QUESTION # 18
You have a table named 'sales_data' with a column 'product_details' of type VARIANT containing JSON data for various products. The JSON structure is inconsistent; some products have a 'size' attribute as a string, while others have it as an integer, and some don't have the attribute at all. You need to extract the 'size' as a consistent numeric value (NULL if it's missing) for analysis. Which SQL statement using table functions and data type conversion techniques correctly and efficiently handles this data inconsistency?
- A.
- B.
- C.
- D.
- E.
Answer: B
Explanation:
TRY_TO_NUMBER() is the most robust and concise way to handle potential data type inconsistencies and missing values when extracting data from VARIANT columns. It attempts to convert the value to a NUMBER and returns NULL if the conversion fails. This elegantly handles both string and integer representations of 'size', as well as cases where 'size' is missing from the JSON. Using Case statements or IS_NUMBER requires more verbose logic and can be less efficient than using the built-in function. NVL replaces NULL with 0, which isn't suitable as the question asks for NULL if missing.
NEW QUESTION # 19
You are working with a Snowflake table 'ORDERS that contains order data in a VARIANT column named 'ORDER DETAILS'. The 'ORDER DETAILS column contains JSON objects with nested arrays of product information, including 'product_id', 'quantity', and 'price'. You need to calculate the total revenue for each order. Which of the following SQL snippets correctly calculates the total revenue for each order using LATERAL FLATTEN and aggregation?
- A. Option D
- B. Option C
- C. Option E
- D. Option A
- E. Option B
Answer: A,B
Explanation:
Snowflake requires explicit casting to numeric datatypes when performing arithmetic operations on VARIANT data. Options A and B do not cast the 'quantity' and 'price' fields to numbers, which would result in incorrect calculations. Option E uses a deprecated ' TO_NUMBER function.
NEW QUESTION # 20
When manipulating data in Snowflake, what distinguishes aggregate functions from analytic functions?
- A. Aggregate functions work on entire datasets
- B. Analytic functions operate on individual rows within a partition
- C. Analytic functions return single calculated values
- D. Aggregate functions handle distinct value sets only
Answer: B
Explanation:
Analytic functions perform calculations on individual rows within a partition, while aggregate functions operate on entire datasets, making them distinct in their functionality.
NEW QUESTION # 21
You are building a dashboard to monitor the performance of online advertisements. The data is stored in a Snowflake table 'AD PERFORMANCE' with columns like 'AD ID', 'IMPRESSIONS', 'CLICKS', 'CONVERSIONS', 'COST' , and 'DATE'. You want to display a trendline of the daily Click-Through Rate (CTR = CLICKS / IMPRESSIONS) and Cost Per Conversion (CPC = COST / CONVERSIONS). Some days might have zero impressions or conversions. To handle these cases and avoid errors or misleading visualizations, which of the following SQL code snippets BEST calculates CTR and CPC safely within a view or CTE used by your dashboard? (Assume appropriate data types for columns).
- A.
- B.
- C.
- D.
- E.
Answer: A
Explanation:
The function in Snowflake (D) is specifically designed to handle division by zero by returning NULL. It is the cleanest and most Snowflake-idiomatic way to address this issue. (B) and 'CASE' statement(E) are valid, but slightly more verbose. Will not work in the scenario when one the columns being divided is null. Option A will result in division by zero errors when IMPRESSIONS or CONVERSIONS are 0.
NEW QUESTION # 22
How do Snowsight dashboards enable effective data presentation for business use analyses?
- A. Snowsight doesn't support visual data representation.
- B. Snowsight offers limited data representation options.
- C. Snowsight dashboards rely solely on text-based representations.
- D. They enable diverse data representation for effective analyses.
Answer: D
Explanation:
Snowsight dashboards enable diverse data representation for effective analyses in business use cases.
NEW QUESTION # 23
You have a CSV file loaded into a Snowflake table named 'raw data'. The file contains customer order data, but some rows have missing values in the 'order date' column. You need to create a new table, 'cleaned data' , that contains only valid records and handles missing 'order date' values by substituting them with the date '1900-01-01'. Which of the following approaches is the MOST efficient and correct way to achieve this using Snowflake features?
- A.
- B.
- C.
- D.
- E.
Answer: C
Explanation:
Option E is the most efficient and correct. 'COALESCE' efficiently handles NULL replacement, and ensures the replacement value is the correct data type (DATE). It also explicitly selects all other columns. Option A only filters out rows with null order_date. Options B, C and D creates a new column, . It does not also implicitly take all columns, which would make this more appropriate.
NEW QUESTION # 24
A marketing team requires a daily report showcasing website traffic, conversion rates, and cost per acquisition (CPA). They want to receive this report as a CSV file attached to an email. The data is stored in a Snowflake table 'WEB ACTIVITY with columns 'DATE , 'VISITS, , and SPEND. Which of the following steps, combined and executed in the correct order, would be the MOST efficient and secure way to automate this report delivery?
- A. Create a Snowflake Alert that triggers when the 'WEB ACTIVITY table is updated. The alert executes a stored procedure that queries the data, formats it as CSV using Snowflake Scripting, writes the CSV to an internal stage, and then uses a Python UDF to send the email with the CSV attachment. Grant the alert necessary privileges.
- B. Create a scheduled task in an external orchestrator (e.g., Airflow). This task uses the Snowflake Python connector to query the data, format it as CSV, writes the CSV to an external stage (e.g., AWS S3), and then uses an email service (e.g., AWS SES) to send the email with the CSV attachment. Configure appropriate IAM roles for Snowflake to access S3.
- C. Use a third-party reporting tool (e.g., Tableau, Power Bl) to connect to Snowflake, create the report, and schedule it for daily email delivery with a CSV attachment. Configure the tool with the appropriate Snowflake connection details and credentials.
- D. Create a Snowflake Stream on the 'WEB ACTIVITY table. When data changes are detected, trigger an external function (e.g., AWS Lambda) via a pipe. The Lambda function queries the data, formats it as CSV, uploads the file to S3, and sends an email with a download link.
- E. Create a Snowflake Task that executes a stored procedure. The stored procedure uses a Snowflake Scripting block to query the data, format it as CSV using Javascript UDF, writes the CSV to an internal stage, and then uses a Java UDF (Util.EmailSender) to send the email with the CSV attachment. Grant necessary permissions to the task's service account to access the stage and execute the UDFs.
Answer: B
Explanation:
Option B is the most efficient and scalable. Using an external orchestrator provides better scheduling control and separation of concerns. Writing to S3 allows for easier integration with other external services. Python connector is preferred for data transformation and CSV generation compared to Snowflake scripting. Utilizing AWS SES for email delivery scales well and aligns with a cloud-native approach. Option A is viable but can be resource-intensive within Snowflake. Options C, D and E are less efficient due to relying on event-based triggers or third party tools for scheduling and email delivery. Option D exposes the data to a third-party tool. Option E requires complex setup and is not the best choice for a simple daily report.
NEW QUESTION # 25
Which actions are typically part of responding to data import errors in Snowflake? (Select all that apply)
- A. Identifying error sources
- B. Analyzing error logs for resolution
- C. Resuming data import immediately
- D. Resolving data inconsistencies
Answer: A,B,D
Explanation:
Responding to data import errors involves identifying error sources, resolving inconsistencies, and analyzing logs for resolution.
NEW QUESTION # 26
How can incorporating visualizations in reports and dashboards facilitate better data comprehension and analysis for business use scenarios?
- A. They enhance data comprehension, aiding effective analysis.
- B. Visualizations limit data exploration and analysis capabilities.
- C. Presenting data visually increases complexity in analysis.
- D. Visualizations don't impact data comprehension or analysis significantly.
Answer: A
Explanation:
Visualizations enhance data comprehension, aiding effective analysis in business use scenarios.
NEW QUESTION # 27
When working with Snowsight dashboards to summarize large data sets, what key advantage do they offer in exploratory analyses?
- A. Snowsight dashboards can't handle large data sets efficiently.
- B. They only support basic data summarization.
- C. They are limited to presenting static data sets.
- D. Snowsight dashboards facilitate quick, visual comprehension of complex data.
Answer: D
Explanation:
Snowsight dashboards aid in exploratory analysis by providing visually accessible insights into complex data, aiding quick comprehension.
NEW QUESTION # 28
You have a table 'PRODUCTS' with a 'PRICE' column stored as VARCHAR. Some values in this column are valid numerical strings (e.g., '12.99'), while others contain invalid characters (e.g., '12.99USD', 'N/A'). You need to calculate the average price of all valid products. Which of the following approaches ensures that you only consider valid numeric values and handles potential errors effectively? Select all that apply.
- A.
- B.
- C.
- D.
- E.
Answer: B,D
Explanation:
Option B is correct because it uses a regular expression to explicitly check if the PRICE column contains only valid numeric characters (digits and an optional decimal point) before attempting to cast it to a DECIMAL. 'REGEXP LIKE' ensures that non-numeric values are filtered out, preventing errors during casting, and then 'CAST is used since it is validated by the regular expression check. Option D is also correct because it uses 'CASE statement with 'IS DECIMAL' function that is a user defined function (UDF) for checking if a string can be converted to decimal, and if True, it casts the PRICE to DECIMAL; otherwise, it assigns NULL. The AVG function automatically ignores NULL values, ensuring that only valid numeric values are considered. Option A won't work as DECIMAL' function is not standard, so you will need to create user defined function. 'TO_NUMBER and 'TO_DECIMAL' throws error if it cannot parse which is not ideal. Option E, throws error if column PRICE cannot be converted, where as 'TRY TO DECIMAL' will not throw the error and return NULL.
NEW QUESTION # 29
You are analyzing the query execution plan of a complex data transformation pipeline in Snowflake. The plan shows a 'Remote Join' operation with high execution time. The two tables involved, 'CUSTOMER and 'ORDERS' , reside in different Snowflake accounts, and the join is performed on the 'CUSTOMER ID' column. Which of the following actions would MOST effectively optimize this query and reduce the 'Remote Join' execution time?
- A. Increase the warehouse size of the account containing the 'ORDERS' table to improve its processing speed.
- B. Implement data filtering on the 'CUSTOMER table before the 'Remote Join' to reduce the amount of data transferred across accounts. Using temporary table can be used for this task.
- C. Create a materialized view in the ORDERS account that pre-aggregates the data needed for the join to reduce the data size sent over the network for remote join.
- D. Ensure both 'CUSTOMER and 'ORDERS tables have the same clustering key, prioritizing 'CUSTOMER IDS.
- E. Replicate the smaller table (either 'CUSTOMER or 'ORDERS, based on size) to the same Snowflake account as the larger table to eliminate the remote join.
Answer: B,E
Explanation:
Options B and C are the most effective. B eliminates the need for a remote join altogether, and C reduces the amount of data transferred during the remote join. Clustering keys (A) don't directly affect remote joins in the same way they affect local joins. Increasing warehouse size (D) can improve performance but doesn't address the fundamental issue of the remote join data transfer. Option E can help if the aggregated data fulfills the query's requirement and reduces significant data transfer, so it might be partially correct, but replicating data or filtering before joining is optimal in most cases.
NEW QUESTION # 30
What actions are involved in performing general DML (Data Manipulation Language) operations in Snowflake? (Select all that apply)
- A. Updating existing data
- B. Deleting data entirely
- C. Inserting new data
- D. Merging data from multiple tables
Answer: A,B,C
Explanation:
General DML operations in Snowflake include inserting, updating, and deleting data.
NEW QUESTION # 31
How does automating and implementing data processing contribute to the overall efficiency of data ingestion?
- A. Implementing data processing increases data redundancy.
- B. Automating processing hampers data accuracy.
- C. Automation eliminates human intervention, ensuring consistency.
- D. It slows down the data ingestion process significantly.
Answer: C
Explanation:
Automation ensures consistency and eliminates manual interventions, enhancing the efficiency of data ingestion.
NEW QUESTION # 32
Why are Stored Procedures valuable in data analysis using SQL?
- A. They restrict the execution of repetitive tasks, limiting efficiency.
- B. Stored Procedures enable custom and repeated data operations, enhancing efficiency.
- C. Stored Procedures solely facilitate data visualization.
- D. They are exclusively used for one-time data operations.
Answer: B
Explanation:
Stored Procedures aid in data analysis by enabling custom and repeated data operations, enhancing efficiency.
NEW QUESTION # 33
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