Snowflake DAA-C01 Real 2025 Braindumps Mock Exam Dumps
DAA-C01 Exam Questions | Real DAA-C01 Practice Dumps
NEW QUESTION # 109
A data analyst is tasked with identifying the top 3 performing sales representatives in each region based on their total sales amount. The sales data is stored in a table named 'sales data" with columns 'region', 'sales_rep', and 'sales_amount'. Which Snowflake SQL statement(s) would efficiently achieve this?
- A. Option C
- B. Option B
- C. Option D
- D. Option A
- E. Option E
Answer: A,B,D
Explanation:
Options A, B, and C correctly utilize window functions to rank sales representatives within each region. assigns a unique rank, assigns the same rank to ties and skips the subsequent ranks, and 'DENSE RANK()' assigns the same rank to ties but does not skip ranks. All three can be used with a 'WHERE clause to filter for the top 3. Option D incorrectly uses 'LAG' and doesn't achieve the desired ranking. Option E uses 'NTILE' which divides the rows into three groups; filtering 'tile = 1' might not always return the top three based on sales.
NEW QUESTION # 110
When working with semi-structured data in Snowflake, how do built-in functions for traversing, flattening, and nesting aid in data manipulation?
- A. They facilitate handling complex and nested data structures
- B. They restrict data access for user roles
- C. They only work with specific file formats
- D. They limit data transformation possibilities
Answer: A
Explanation:
Built-in functions for semi-structured data in Snowflake simplify handling complex and nested structures, making data manipulation more manageable and enhancing flexibility in data transformation.
NEW QUESTION # 111
You're tasked with creating a Snowsight dashboard to monitor the performance of different ETL pipelines. The dashboard needs to display the average run time and the number of errors for each pipeline over the last 7 days. The data is stored in a table called 'ETL LOGS' with columns 'end_time', and 'error_flag' (boolean). You need to present this information in a way that users can easily compare the performance of different pipelines. Which of the following SQL queries, used as the basis for a Snowsight tile, would be MOST appropriate for this dashboard?
- A. Option C
- B. Option D
- C. Option A
- D. Option B
- E. Option E
Answer: C
Explanation:
Option A is the most accurate. It correctly calculates the average run time in seconds using start_time, end_time)' and the error count using a conditional aggregation 'SUM(CASE WHEN error_flag THEN 1 ELSE 0 END)'. Option B is incorrect because - start_timey will not return runtime in seconds, it returns a fractional number of days. Option C uses IFF and datediff which is acceptable. Option D only counts the total logs and doesn't filter if there is an error. Option E is also technically correct by converting the boolean to a number to sum. Option A uses standard SQL which might be more preferable.
NEW QUESTION # 112
You're using Snowsight to build a dashboard for monitoring website performance. The data is in a table called 'WEB EVENTS' with columns: 'EVENT _ TIME' (TIMESTAMP_NTZ), 'EVENT _ TYPE' (VARCHAR, e.g., 'page_view', 'button_click'), 'USER_ID' (VARCHAR), and 'PAGE URL' (VARCHAR). You want to create a tile that shows the average time between consecutive 'page_view' events for each user over the last 7 days. This will help you understand how users are navigating the site. Assume that for a single user, page_view events are ordered by EVENT TIME. Which of the following SQL queries, when used as the basis for a Snowsight tile, will correctly calculate this average time difference in seconds?
- A. Option C
- B. Option D
- C. Option A
- D. Option B
- E. Option E
Answer: C
Explanation:
It uses the window function to get the previous event time for each user, then calculates the difference between consecutive event times in seconds using 'TIMESTAMP_DIFF. The outer query then averages these differences for each user. The 'WHERE PREVIOUS_EVENT_TIME IS NOT NULL' clause is important to exclude the first event for each user, which would have a null previous event time. Option B attempts to subtract timestamps directly, which is not the correct way to get the difference in seconds in Snowflake. Option C uses 'DATEDIFF which has the parameters in the wrong order compared to the logic of the question. Option D incorrectly uses FIRST _ VALUE. Option E omits the subquery necessary to correctly use the LAG function.
NEW QUESTION # 113
You are tasked with cleaning a 'COMMENTS table that contains user-generated comments in a column (VARCHAR). The comments often contain HTML tags, excessive whitespace, and potentially malicious scripts. Your goal is to remove all HTML tags, trim leading and trailing whitespace, and escape any remaining HTML entities to prevent script injection vulnerabilities. Which combination of Snowflake scalar functions provides the most robust and secure way to achieve this data cleaning?
- A. SELECT TRIM(REGEXP >', FROM COMMENTS;
- B. SELECT TRIM(HTML ENTITY DECODE(REGEXP >', FROM COMMENTS;
- C. SELECT >', FROM COMMENTS WHERE
- D. SELECT >', comment_text) FROM COMMENTS;
- E. SELECT >', FROM COMMENTS;
Answer: E
Explanation:
Option B is the most robust and secure method. Here's why: 'REGEXP REPLACE(comment_text, Y', "Y: This removes HTML tags. This attempts to parse the remaining text as XML. If there are still any unescaped or malformed HTML entities, this step will help to isolate them and get rid of the tags. If the text cannot be parsed as XML, PARSE_XML returns NULL. '$').$: This extracts the text content of the XML. Crucially, 'XMLGET' inherently performs HTML entity decoding, effectively escaping potentially dangerous characters (e.g., becomes This prevents script injection. This removes leading and trailing whitespace. Option A only removes the HTML tags and trims the text, but doesn't handle HTML entity encoding, and thus it is vulnerable to script injection. Option C is not correct as HTML ENTITY DECODE' is not an existing function in Snowflake. Option D is not correct as the text needs to be cleaned irrespective of whether it contains XML or not. Option E - if parsing the XML returns null then original value gets returned , which we don't want , we would need to make the value NULL.
NEW QUESTION # 114
When implementing pre-math calculations (e.g., randomization, ranking, grouping) in Snowflake, how do they contribute to data analysis?
- A. Randomization techniques introduce biases in data analysis
- B. Pre-math calculations affect only specific data types
- C. Ranking aids in data summarization and ordering
- D. Grouping ensures data integrity and consistency
Answer: C
Explanation:
Pre-math calculations like ranking assist in summarizing and ordering data, enabling meaningful insights and comparisons in data analysis.
NEW QUESTION # 115
You are analyzing a query profile and observe a high percentage of time spent in 'Remote Scan'. Which of the following actions would be MOST effective in reducing this bottleneck and improving query performance?
- A. Convert the table to a transient table to reduce metadata overhead.
- B. Implement clustering on the table based on the filter criteria used in the query.
- C. Increase the warehouse size to allow more concurrent operations.
- D. Create a materialized view that pre-computes the results of the query.
- E. Add a 'LIMIT' clause to the query to reduce the amount of data scanned.
Answer: B
Explanation:
High 'Remote Scan' time indicates that Snowflake is spending a significant portion of its time fetching data from remote storage. Clustering on the table based on the query's filter criteria will help Snowflake quickly identify and retrieve only the relevant data, reducing the amount of data scanned remotely.
NEW QUESTION # 116
You have two Snowflake tables: 'transactions' (containing transaction details with columns 'transaction id', 'customer id', 'amount' , 'transaction_date') and 'customer_demographicS (containing customer demographic information with columns 'customer_id', 'age' , gender' , 'location'). You need to enrich the 'transactions' table with customer demographics to analyze transaction patterns based on customer segments. What is the most efficient and scalable way to achieve this data enrichment in Snowflake, considering the 'transactions' table contains billions of rows?
- A. Using a standard INNER JOIN between the 'transactions' and 'customer_demographics' tables in a CREATE TABLE AS SELECT (CTAS) statement.
- B. Export the tables to a data lake, perform the join using a Spark cluster, and then import the enriched data back into Snowflake.
- C. Using Snowflake Streams and Tasks to incrementally enrich the 'transactions' table as new data arrives.
- D. Using Snowflake's materialized views to pre-compute the enriched data and refresh it periodically.
- E. Using a series of correlated subqueries to fetch the demographic information for each transaction record.
Answer: A,C,D
Explanation:
Options A, C, and D are efficient approaches. Option A (CTAS with INNER JOIN) is often the simplest and most performant for a one- time enrichment, leveraging Snowflake's query optimization. Option C (Materialized Views) provides pre-computed enriched data, ideal for frequent queries on the enriched dataset. Option D (Streams and Tasks) enables incremental enrichment, suitable for real-time or near real-time data updates. Option B (correlated subqueries) is generally inefficient and should be avoided with large datasets. Option E introduces unnecessary complexity by exporting data to a data lake.
NEW QUESTION # 117
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?
- A. Run the query multiple times with different warehouse sizes, recording the execution time for each size. Choose the warehouse size with the lowest execution time, regardless of cloud services usage.
- B. Start with the smallest warehouse size and incrementally increase the size, monitoring query execution time and cloud services usage. Stop increasing the size when query time plateaus or cloud services usage increases significantly.
- C. Use the query history to determine the average execution time for similar queries and choose a warehouse size that is slightly larger than the one used for those queries.
- D. Set the parameter to a low value to prevent long-running queries and force Snowflake to automatically optimize the warehouse size.
- E. Increase the warehouse size to the largest available size and monitor query execution time and cloud services usage. If cloud services usage is high, decrease the warehouse size until a balance is achieved.
Answer: B
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 # 118
How do Snowsight dashboards enable effective data presentation for business use analyses?
- A. They enable diverse data representation for effective analyses.
- B. Snowsight dashboards rely solely on text-based representations.
- C. Snowsight doesn't support visual data representation.
- D. Snowsight offers limited data representation options.
Answer: A
Explanation:
Snowsight dashboards enable diverse data representation for effective analyses in business use cases.
NEW QUESTION # 119
When utilizing materialized views, what benefit do they offer in terms of query performance and data retrieval?
- A. Regular views simplify complex data structures for better query performance.
- B. Materialized views restrict data retrieval for improved security.
- C. They offer real-time updates reflecting instantaneous database changes.
- D. Materialized views provide precomputed snapshots, improving query performance.
Answer: D
Explanation:
Materialized views provide precomputed snapshots, enhancing query performance.
NEW QUESTION # 120
You've created a Snowflake dashboard for your company's sales team using a Bl tool. The dashboard displays real-time sales data pulled directly from Snowflake. The sales team wants to be automatically notified whenever a particular product's sales exceed a certain threshold in a given day. You need to implement a system to accomplish this. Which of the following methods offer a way to trigger notifications based on data changes in Snowflake AND integrate with an external notification system (e.g., Slack, Email)? Select all that apply:
- A. BI Tool Scheduling: Configure the BI tool to run a scheduled query that checks for the sales threshold. If the threshold is exceeded, the BI tool can send an email or trigger a webhook to send a notification.
- B. Snowflake Tasks: Schedule a daily task to run a query checking for sales exceeding the threshold. If the condition is met, the task can execute a stored procedure that calls an external API to send a notification.
- C. Snowflake Streams and Pipes: Create a stream on the sales table and a pipe that loads data into a separate notification table. Configure an external service to monitor the notification table and send alerts when new records are inserted.
- D. Snowflake Replication: Replicate the sales table to a separate Snowflake account. Monitor the replicated table for threshold breaches, and use Snowflake Scripting to send email.
- E. Snowflake Alerts: Create an alert that triggers when a specific query condition (sales exceeding the threshold) is met. Configure the alert to call an external function (e.g., AWS Lambda) which then sends a notification to the desired system.
Answer: B,E
Explanation:
A and B offer native Snowflake capabilities for triggering actions based on data conditions and integrating with external systems. Alerts are designed for real-time monitoring and can call external functions to send notifications. Tasks can be scheduled to perform periodic checks and trigger notifications through stored procedures and external APIs. C: Streams and Pipes are primarily for continuous data loading and transformation, not direct notification triggering based on data conditions. It's an indirect method. D: BI tools are not the primary component for handling real time alerting based on exceeding specific values. E: Replication is for disaster recovery/high availability and doesn't address real- time notifications.
NEW QUESTION # 121
You have a Snowflake table called 'PRODUCT SALES' with columns 'PRODUCT ID (INT), 'SALE DATE' (DATE), and 'SALES AMOUNT' You want to implement a data integrity rule to prevent duplicate records based on 'PRODUCT ID and 'SALE DATE. Which of the following methods provides the most effective way to achieve this in Snowflake, and why?
- A. Create a view that filters out duplicate records using 'ROW NUMBER()' and partitioning by 'PRODUCT ID and 'SALE DATE. This guarantees data integrity during query execution.
- B. Add a composite UNIQUE constraint on 'PRODUCT ID' and 'SALE DATE. Snowflake will automatically prevent the insertion of duplicate rows during data loading or insertion.
- C. Create a stored procedure that runs periodically to identify and delete duplicate records based on 'PRODUCT and 'SALE DATE'. This approach fixes integrity issues reactively.
- D. Implement data validation within your ETL pipeline before loading data into Snowflake to prevent duplicates from entering the table. This approach keeps the table clean from the start.
- E. Create a user defined function (UDF) that checks for the existance of data before insert. If data already exist, don't insert it.
Answer: B,D
Explanation:
Options C and D provide the most effective methods. A composite UNIQUE constraint directly prevents duplicate insertions at the table level. Validating in the ETL pipeline (D) prevents duplicates before they even reach the database. A view (A) only masks the issue, and a stored procedure (B) is reactive and doesn't prevent duplicates from being inserted in the first place. A UDF could be helpful but is not the BEST option for this scenario.
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. Available storage capacity
- B. Data redundancy requirements
- C. Frequency of data analysis
- D. Speed of data retrieval
Answer: A,C
Explanation:
Identifying the volume of data involves considering available storage capacity and the frequency of data analysis.
NEW QUESTION # 123
Your organization receives daily updates to a product catalog in Avro format. The Avro schema evolves frequently, adding and removing fields. You need to ingest these Avro files into Snowflake. Which of the following strategies will BEST handle schema evolution while minimizing data loading complexity?
- A. Create a new Snowflake table for each Avro schema version. Maintain a view that unions all tables to provide a consistent interface.
- B. Use Snowflake's Schema Detection feature with the COPY command into a table with a VARIANT column. Then, use the 'LATERAL FLATTEN' function to extract the fields and load them into a relational table with the correct data types. Automate the schema creation/alteration of the relational table based on the inferred schema from the VARIANT column.
- C. Load the Avro files into a VARIANT column in Snowflake and use SQL to extract the required fields. Periodically update the table schema as the Avro schema evolves.
- D. Define a fixed Snowflake table schema with a limited set of commonly used fields. Ignore any additional fields in the Avro files during loading.
- E. Load the Avro files into a CSV format after flattening the Avro Schema.
Answer: B
Explanation:
Option D is the best approach. Loading the Avro data into a VARIANT column first allows you to capture all the data, regardless of schema evolution. Using the 'LATERAL FLATTEN' function allows extraction of fields. Utilizing Snowflake's schema detection further automates the process. Option A requires manual schema updates, which is less scalable. Option B creates a management overhead with multiple tables and views. Option C loses data. Option E requires external flattening which creates additional complexity.
NEW QUESTION # 124
What critical role do user-defined functions (UDFs) play in SQL for advanced data analysis?
- A. UDFs hinder query optimization in SQL.
- B. They enable customized operations on data, extending SQL functionalities.
- C. UDFs only handle basic data manipulation tasks.
- D. UDFs are limited to specific data types.
Answer: B
Explanation:
UDFs extend SQL functionalities by enabling customized operations on data, going beyond standard SQL capabilities.
NEW QUESTION # 125
You have identified a valuable dataset on the Snowflake Marketplace related to weather patterns. To consume this data, you perform the following actions: 1. You request and receive the data share from the provider. 2. You create a database named 'WEATHER DB' from the share. Now you want to create a secure view named 'DAILY WEATHER SUMMARY in your own database 'ANALYTICS DB.PUBLIC', which joins your internal sales data C ANALYTICS DB.PUBLIC.SALES) with the weather data from the provider's 'WEATHER DB.WEATHER SCHEMA.DAILY WEATHER' table. You only want to expose specific columns from both tables in your view to minimize data exposure. Which of the following steps are required to ensure this secure and functional integration?
- A. Grant 'SELECT privileges on 'ANALYTICS DB.PUBLIC.SALES' to the share provider.
- B. Create an outbound share and grant usage on the ANALYTICS DB database
- C. Create a warehouse with access control policies enabled.
- D. No additional steps are required; you can directly query in your view definition.
- E. Create the view using fully qualified names, selecting only the necessary columns from both the 'SALES' table and the 'DAILY WEATHER table.
Answer: E
Explanation:
Option D is the correct answer. When consuming data from a Snowflake Marketplace data share, you do not need to grant privileges to the provider on your internal data (Option A is incorrect). Option B is incorrect because creating views and acessing tables requires proper schema names. Option C is incorrect because outbound shares are not needed to consume marketplace data and access control is managed within the consumer account. Option E is incorrect because enabling access control policies on a warehouse is irrelevant to the task of creating a secure view that joins internal data with shared data.
NEW QUESTION # 126
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