Oct-2025 Free DAA-C01 Test Questions Real Practice Test Questions
DAA-C01 Dumps Updated Oct 17, 2025 WIith 198 Questions
NEW QUESTION # 72
What role does leveraging native data types play in Snowflake while working with different datasets?
- A. Reduces query flexibility and optimization possibilities
- B. Impedes data compatibility with other platforms
- C. Improves query performance and storage efficiency
- D. Limits data type conversion capabilities
Answer: C
Explanation:
Using native data types in Snowflake enhances query performance and storage efficiency by optimizing data representation and storage structures.
NEW QUESTION # 73
You have a table 'CUSTOMER DATA' with a column 'phone_number" (VARCHAR) that contains phone numbers in various formats (e.g., '123-456-7890', '1234567890', '+11234567890'). You need to standardize the phone numbers to a format of '1234567890' (no hyphens or country code). Which Snowflake SQL statement, using scalar string functions, will achieve this standardization while gracefully handling potentially invalid phone numbers (e.g., too short or containing letters) by returning NULL for invalid entries?
- A. Option D
- B. Option E
- C. Option B
- D. Option A
- E. Option C
Answer: E
Explanation:
Option C is the correct answer. It first uses 'REGEXP REPLACE(phone_number, '[AO-91+', to remove all non-numeric characters from the phone number. Then, it uses a 'CASE statement to check if the resulting string has a length of 10 (a valid phone number length after standardization). If it does, the standardized phone number is returned; otherwise, NULL is returned. Option A only removes non-numeric characters but doesn't handle invalid lengths. Option B and D is doing same ,using 'IFF and adds unnecessary complexity by using 'IS NUMERIC' which is redundant since 'REGEXP REPLACE ensures only numbers exist. Option E filters on 'WHERE' Clause that reduces the record which are having length of 10, But Question needs to return NULL for invalid entries .
NEW QUESTION # 74
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 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.
- 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. 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.
- D. 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.
- E. 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.
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 # 75
In Snowflake, how does partition pruning contribute to optimizing query performance?
- A. Impacts query planning but not execution
- B. Filters unnecessary partitions during query execution
- C. Increases query complexity and optimization
- D. Limits data accessibility across warehouses
Answer: B
Explanation:
Partition pruning in Snowflake filters out unnecessary partitions during query execution, significantly enhancing query performance by minimizing the data scanned during the query.
NEW QUESTION # 76
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 # 77
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 line chart showing the trend of CPA over time.
- B. A Pie chart showing CPA percentage against target CPA percentage.
- C. A scatter plot showing the relationship between ad spend and CPA.
- D. A bar chart comparing the average CPA to the target CPA.
- E. A gauge chart displaying the current CPA and the target CPA, with color-coded zones indicating performance levels.
Answer: E
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 # 78
Your company is using Snowflake to store customer transaction data'. You want to enrich this data with demographic information from a Snowflake Marketplace data provider. The provider offers a secure data share with a view called 'CUSTOMER DEMOGRAPHICS. You need to join the customer transaction data in your 'TRANSACTIONS' table with the demographic data from the 'CUSTOMER DEMOGRAPHICS' view. Which of the following SQL queries is the MOST efficient and secure way to achieve this, assuming you have already created a database from the share?
- A. Option D
- B. Option C
- C. Option E
- D. Option B
- E. Option A
Answer: D
Explanation:
Option B is the most efficient and secure because it explicitly uses an INNER JOIN, ensuring that only matching records between the TRANSACTION table and the Customer Demographics view are included. Using INNER JOIN improves performance compared to implicit joins (Option A). Options C and E uses LEFT and FULL OUTER joins which might result in unnecessary nulls and impacting the perfromance. Option D will not work because the schema name is missing.
NEW QUESTION # 79
You are developing a data pipeline that involves loading data from multiple CSV files stored in an Amazon S3 bucket into a Snowflake table. The files have different schemas (different column names and data types), but all files contain a common column named 'record_type' that identifies the schema of the data in that file. You need to create a single Snowflake table that can store data from all the files, while ensuring data integrity and proper data typing based on the 'record_type'. Which of the following approaches is the MOST efficient and scalable method to achieve this in Snowflake?
- A. Create a single external table pointing to the S3 bucket. Use a Snowflake stream on the external table to track changes. Implement a series of tasks, one for each 'record_type', that transform data from the stream and load into a single target table with appropriate datatypes.
- B. Create multiple Snowflake tables, one for each 'record_type', with the corresponding schema. Use a Snowflake task that runs periodically to identify new files in the S3 bucket, determine their 'record_type', and load them into the appropriate table using a dynamic SQL query.
- C. Use Snowflake's VARIANT data type for a single column in the Snowflake table. Load the entire CSV file content as a JSON string into this column. Create views to extract and cast the data based on the 'record_type'.
- D. Create a single Snowflake table with a VARIANT column to store the raw data. Use a Snowpipe to load the data continuously. Implement a stored procedure that is triggered by the Snowpipe to parse the VARIANT data based on 'record_type' and insert it into correctly typed columns in the same table.
- E. Create a single, wide Snowflake table with all possible columns from all CSV files, defining all columns as VARCHAR. Load all data into this table, then create views for each 'record_type' that cast the data to the correct data types.
Answer: B
Explanation:
The most efficient and scalable method involves creating multiple Snowflake tables, one for each 'record_type'. Loading data into correctly typed tables, using a task to dynamically identify files and load them into the appropriate table, leverages Snowflake's strengths. While VARIANT can store different schemas, querying and processing data in VARIANT columns are less performant than querying and processing data with correct data types. A single wide table with all VARCHAR columns requires extensive casting in views. External tables with streams can work, but creating multiple tasks for each record type becomes complex. Choosing the correct data type at load time and having well-defined schemas will minimize transformation later.
NEW QUESTION # 80
You need to load data from a set of CSV files located in an AWS S3 bucket into a Snowflake table called 'SALES DATA. The CSV files have a header row, and you want to automatically detect the data types of the columns. However, some files might have inconsistent data types (e.g., a column that is usually an integer might occasionally contain a string). Furthermore the file are compressed using GZIP. Which of the following COPY INTO command options can you use to achieve this goal while handling potential data type mismatches?
- A. Option D
- B. Option C
- C. Option B
- D. Option A
- E. Option E
Answer: E
Explanation:
The 'ON_ERROR = 'SKIP_RECORD" option allows the COPY INTO command to skip individual records that have data type mismatches or other errors during the load process. This ensures that the load process continues even if some records fail validation. 'SKIP_FILE' would skip entire file, 'CONTINUE' will only continue the process with the given data, and 'SKIP_FILE' skips the file. The VALIDATE UTF8 isn't directly relevant to data type issues
NEW QUESTION # 81
A data analyst needs to ingest data from various sources into Snowflake, cleanse it, and load it into target tables. Which of the following actions are MOST crucial for ensuring data quality and consistency during the ingestion and preparation phases?
- A. Using Snowflake's data masking policies to protect sensitive data during ingestion.
- B. Enforcing data type constraints on Snowflake table columns.
- C. Implementing data validation checks using Snowflake user-defined functions (UDFs) or stored procedures to detect and handle invalid data.
- D. Implementing data profiling techniques to identify data quality issues and inconsistencies before loading data into target tables and creating detailed data dictionaries.
- E. Storing all data in a single large table to avoid data silos.
Answer: B,C,D
Explanation:
Options A, B, and E are most crucial for data quality and consistency. Enforcing data type constraints (A) ensures that only data of the correct type is loaded into the tables. Implementing data validation checks (B) using UDFs or stored procedures allows for detecting and handling invalid data, preventing it from corrupting the data warehouse. Data profiling (E) helps to understand data quality issues before the load and guides data cleansing efforts. Storing all data in a single table (C) is generally not recommended as it can lead to performance issues and make it harder to manage data. While data masking (D) is important for data security, it is not directly related to data quality and consistency during ingestion and preparation. Data dictionaries promote data quality and usability.
NEW QUESTION # 82
You have a Snowflake table containing order data'. You need to calculate the shipping cost for each order based on the order amount and the destination country. You decide to use a Java UDF for this calculation, as the logic is complex and involves external APIs (simulated here). The UDF should take the order amount (FLOAT) and destination country (VARCHAR) as input and return the calculated shipping cost (FLOAT). The Java code requires external JAR files to be imported. Which of the following options correctly defines and calls the Java UDF in Snowflake, assuming the necessary JAR file has been uploaded to a stage named 'my_stage'?
- A. Option D
- B. Option C
- C. Option B
- D. Option A
- E. Option E
Answer: E
Explanation:
Option E is the most correct because the function definition does not require the definition of the class 'com.example.ShippingCalculator' within the function body. Since the jar file is defined within the imports section, snowflake does not need the explicit definition. Option A, C, and D requires the function and class definition which is already defined in the jar, and defining it again will lead to conflicts. Option B doesn't correctly define the class. All the rest of the options either try to define the Java code inline (which is incorrect when using IMPORTS) or have syntax errors in the UDF definition.
NEW QUESTION # 83
How do Materialized views differ from Regular views in the context of data analysis?
- A. Regular views provide a persisted snapshot of data, unlike Materialized views.
- B. Materialized views simplify complex data structures for ease of analysis, unlike Regular views.
- C. Materialized views restrict data accessibility compared to Regular views.
- D. Regular views offer precomputed snapshots, differentiating them from Materialized views.
Answer: D
Explanation:
Materialized views offer precomputed snapshots, differentiating them from Regular views which don't precompute data.
NEW QUESTION # 84
You have identified a potentially valuable dataset on the Snowflake Marketplace that provides demographic information by zip code. Your company has customer data in a table 'CUSTOMERS' with columns 'CUSTOMER ID' ONT), 'ZIP CODE' (VARCHAR), and 'PURCHASE AMOUNT (NUMBER). Before subscribing to the Marketplace data, you want to preview the data to ensure it contains the necessary information and assess its quality. What is the recommended and MOST efficient way to preview the data from the Snowflake Marketplace listing?
- A. Utilize the 'Preview Data' tab available directly within the Snowflake Marketplace listing. This tab typically provides a sample of the data, allowing you to inspect the data structure, content, and quality before subscribing.
- B. Subscribe to the free trial of the Snowflake Marketplace listing, create a database from the shared data, and then sample the demographic data using 'SELECT FROM .. LIMIT 100'.
- C. Contact the data provider directly and request a sample dataset to be delivered to you as a CSV file. Then upload this CSV file to an internal stage and query it to preview the data.
- D. Create a stored procedure that dynamically calls the Marketplace data listing API to fetch a JSON sample and parse the JSON data into a temporary table for previewing.
- E. Use the 'Get Data' button on the Marketplace listing and specify that you only want a small subset of the data, such as data for a specific geographic region. This will create a smaller, more manageable dataset for previewing.
Answer: A
Explanation:
Option D is the most recommended and efficient way. The 'Preview Data' tab within the Snowflake Marketplace listing is designed precisely for this purpose. It allows you to inspect a sample of the data directly within the Marketplace interface, without requiring you to subscribe or incur costs. Option A requires you to subscribe to the listing, even if it's a free trial, which is an unnecessary step for simply previewing the data. Option B is inefficient and requires manual effort to obtain and load the data. Option C is not a standard feature of the Snowflake Marketplace. Option E is overly complex and inefficient for a simple preview task.
NEW QUESTION # 85
When working with CSV, JSON, and Parquet data types in Snowflake, how does selecting the appropriate data format impact query performance and storage efficiency?
- A. All formats have similar impacts on query performance and storage
- B. JSON format accelerates query execution but increases storage requirements
- C. Parquet format optimizes query performance and reduces storage requirements
- D. CSV format enhances query performance and minimizes storage needs
Answer: C
Explanation:
Parquet format in Snowflake optimizes query performance and storage efficiency by enhancing data compression, reducing storage needs, and accelerating query execution compared to other formats like CSV and JSON.
NEW QUESTION # 86
You are preparing data for a machine learning model that predicts customer churn. You have a table 'CUSTOMER TRANSACTIONS with columns 'CUSTOMER ID', 'TRANSACTION DATE, and 'TRANSACTION AMOUNT'. Your model requires a feature representing the recency of the customer's last transaction (days since last transaction) calculated as of today. Which of the following SQL snippets in Snowflake best achieves this, considering potential performance implications on a large dataset?
- A.
- B.
- C.
- D.
- E.
Answer: C
Explanation:
The best approach is E because it explicitly casts 'CURRENT _ to a DATE data type using 'CAST(CURRENT This ensures consistency and avoids potential issues if the TRANSACTION DATE' column is also of DATE data type. provides a date only, while provides date and time. Using 'CURRENT directly in 'DATEDIFF with a DATE field could lead to unexpected results due to the time component. Also other options are incorrect because 'GETDATE()' does not exist in snowflake, and because is redundant since CURRENT TIMESTAMP returns a timestamp already
NEW QUESTION # 87
When considering row access policies and Dynamic Data Masking in Snowflake, how do they influence data visibility and security?
- A. Dynamically mask sensitive data for specific users
- B. Only apply to certain data types
- C. Limit data access based on user roles
- D. Grant unrestricted access to all data
Answer: A,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 # 88
......
View All DAA-C01 Actual Free Exam Questions Updated: https://www.prepawaytest.com/Snowflake/DAA-C01-practice-exam-dumps.html
Pass Authentic Snowflake DAA-C01 with Free Practice Tests and Exam Dumps: https://drive.google.com/open?id=1MjbrY8hKnbzc85tkG7ip2GNG-ojjYgzo