Latest [Oct 19, 2025] DAA-C01 Exam Dumps - Valid and Updated Dumps
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NEW QUESTION # 19
A healthcare provider is investigating patient readmission rates within 30 days of discharge. They suspect a correlation between patient demographics (age, gender, location) and readmission. You have the following tables: 'PATIENTS': 'patient_id', 'age', 'gender' , 'zip_code' 'ADMISSIONS': 'admission_id' , 'patient_id' , 'admission_date', 'discharge_date' Which of the following approaches would be MOST effective to identify patient demographics significantly correlated with higher readmission rates within 30 days? (Select TWO)
- A. Implement a cohort analysis to track patient readmission rates over time for different demographic segments and visualize the trends using Snowflake's data visualization capabilities (if integrated) or export the data to a BI tool.
- B. Develop a complex SQL query to directly identify and list all patients who have been readmitted more than twice in the last year, irrespective of their demographics.
- C. Calculate the overall readmission rate and compare it to the readmission rates for different demographic groups using Chi-Square tests or similar statistical methods to assess statistical significance.
- D. Create a Snowflake user-defined function (UDF) in Python to perform a complex machine learning model directly on the data to predict readmissions based on demographics, without any initial exploratory data analysis.
- E. Use correlation coefficients (e.g., Pearson, Spearman) to directly measure the linear association between demographics and the binary readmission outcome (1 -readmitted, 0=not readmitted).
Answer: A,C
Explanation:
Options A and D are the most effective. A suggests using statistical tests (Chi-square) which can identify statistically significant differences in readmission rates across demographic groups. D proposes cohort analysis, enabling the tracking and visualization of readmission trends for different demographics over time, allowing for the identification of segments with consistently high readmission rates. Option B is not optimal as it jumps directly into complex modeling without initial EDA. Option C, while valid, doesn't handle categorical variables (gender, zip_code) well and might miss non-linear relationships. Option E, identifying high-frequency readmitters, is helpful but doesn't directly address the relationship with demographics.
NEW QUESTION # 20
Which approach is most suitable for making data-driven predictions?
- A. Using built-in SQL functions for statistical analysis
- B. Ignoring historical data trends
- C. Relying on external data sources only
- D. Basic SQL queries
Answer: A
NEW QUESTION # 21
You are tasked with identifying potential data sources for a new marketing analytics dashboard. The dashboard needs to provide insights into customer behavior across various touchpoints. Which of the following would be the MOST appropriate data sources to consider?
- A. Social media activity data ingested via a third-party API and stored in a relational database.
- B. Website clickstream data stored in AWS S3 buckets in Parquet format.
- C. Database containing HR employee data.
- D. Salesforce data containing customer interactions and sales opportunities.
- E. IoT sensor data containing temperature readings.
Answer: A,B,D
Explanation:
Options A, B, and C are the most relevant data sources for a marketing analytics dashboard focused on customer behavior. Website clickstream data (A) provides insights into user interactions on the website. Social media activity data (B) offers insights into customer sentiment and engagement. Salesforce data (C) provides information about customer interactions and sales opportunities. HR employee data (D) and IoT sensor data (E) are less relevant to customer behavior and marketing analytics.
NEW QUESTION # 22
A retail company suspects a sudden drop in sales in the 'Electronics' category. You, as a data analyst, need to perform a diagnostic analysis. Which of the following data collection strategies would be MOST effective to pinpoint the root cause, considering the limitations of Snowflake's cost optimization?
- A. Collect aggregated monthly sales data for the 'Electronics' category, comparing this year's figures to the previous year's.
- B. Execute a sampling query to extract 10% of the sales data of Electronic Category and compare it with previous period data.
- C. Extract a full data dump of all sales transactions across all categories and historical periods to identify any anomalies globally.
- D. Gather customer support tickets related to 'Electronics' purchases from the last month, focusing on complaints about product quality or delivery issues.
- E. Collect detailed sales data (product ID, price, discounts, region, customer segment, timestamps) for the 'Electronics' category for the last 3 months, focusing on daily and hourly trends.
Answer: D,E
Explanation:
Option A provides granular data for trend analysis, and option D brings qualitative data (support tickets) that can reveal underlying issues like product defects or shipping problems. Collecting ALL sales data (option B) is resource-intensive and inefficient for initial diagnostic analysis. Option C lacks the necessary granularity. Option E can be used to establish a baseline for comparison, but is not effecient due to sampling of data.
NEW QUESTION # 23
A retail company wants to visualize sales performance across different product categories and regions. The business stakeholders need to identify both overall sales trends and granular insights into the performance of specific products in specific regions. They require a dashboard that allows for easy comparison of sales across categories and regions, highlighting best and worst performers. Which combination of chart types would be MOST effective for this dashboard, considering scalability and the need to avoid over-plotting?
- A. A scatter plot comparing sales volume and profit margin for each product, a bar chart for sales by region, and a gauge chart indicating overall sales target achievement.
- B. A stacked bar chart for sales by category, a line chart for overall sales trend over time, and a pie chart for regional sales distribution.
- C. A heat grid showing sales by category and region, a time series chart for overall sales trends, and a treemap representing the contribution of each category to total sales.
- D. A geographical map visualizing sales by region with color-coded regions, a time series chart for overall sales trends, and a detail table for viewing sales by product categories.
- E. A combination of bullet charts to show sales performance against targets for each region and category, a time series chart for overall sales trend, and a scatter plot showing discount vs quantity.
Answer: C
Explanation:
A heat grid effectively visualizes the relationship between two categorical variables (category and region) using color intensity, making it easy to identify high and low sales areas. A time series chart is appropriate for displaying trends over time. A treemap shows the proportional size of each category contributing to total sales. Stacked bar charts can become difficult to read with many categories and pie charts are not ideal for precise comparisons. Scatter plots are useful for correlation analysis (Sales vs Profit). A map would be good for high level visualization but not for specific numbers or precise details. Bullet charts are more suitable for target vs actual comparisons than a regional overview.
NEW QUESTION # 24
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. Run 'SELECT COUNT( ) FROM SALES DATA;' to check total record count and compare against historical values.
- C. Use Snowflake's Query Profile feature to analyze the execution plan of the ETL queries and identify the stages consuming the most time.
- D. 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.
- E. 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.
Answer: A,C,D,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 # 25
You are using Snowpipe to continuously load JSON data from an external stage. Occasionally, some JSON records are malformed and cause the pipe to fail. You want to configure the pipe to skip these invalid records and continue loading valid data, while also capturing the error details for later analysis. Which approach provides the most efficient and appropriate solution for this scenario?
- A. Use the 'ON ERROR = 'CONTINUE'' option in the 'COPY INTO' statement used by the Snowpipe definition in conjunction with the 'VALIDATE function to capture error details.
- B. Implement custom error handling in your application code to pre-validate JSON records before uploading them to the stage.
- C. Configure the Snowpipe definition to use the 'VALIDATE(O)' function within the 'COPY INTO' statement.
- D. Use the = 'SKIP_FILE" option in the 'COPY INTO' statement used by the Snowpipe definition.
- E. Use the 'VALIDATION MODE = RETURN ALL ERRORS parameter in the 'COPY INTO' statement and then filter the data based on the errors returned.
Answer: A
Explanation:
Option E is the most efficient and complete solution. SON ERROR = 'CONTINUE" allows Snowpipe to skip bad records and continue processing. Using it in conjunction with the 'VALIDATE function within the 'COPY INTO' statement enables capturing error information for analysis. This combines error skipping with error logging. Options A, B, C, and D are either less efficient (requiring pre-processing or post- processing of data), or do not provide a comprehensive solution for both skipping and capturing error information. Using (option B) is too coarse-grained as it skips entire files, even with only a few errors. Using 'VALIDATION MODE without ERROR=CONTINUE will still stop the pipe on errors.
NEW QUESTION # 26
How do constraints contribute to ensuring data integrity in Snowflake?
- A. Constraints ensure data consistency and accuracy.
- B. Constraints enforce data uniqueness only.
- C. Constraints solely control data deletion.
- D. They limit data access for users.
Answer: A
Explanation:
Constraints in Snowflake ensure data consistency and accuracy, aiding in maintaining data integrity.
NEW QUESTION # 27
What is the primary benefit of using secure views in data analysis?
- A. Secure views simplify complex data structures more effectively than materialized views.
- B. They prevent the creation of materialized views.
- C. They don't impact data security but significantly enhance query performance.
- D. Secure views offer enhanced data security while allowing selective data access.
Answer: D
Explanation:
Secure views enhance data security while allowing selective data access.
NEW QUESTION # 28
What is the primary benefit of connecting BI tools to Snowflake for dashboard creation?
- A. BI tools restrict dashboard customization
- B. Seamless integration and data visualization
- C. Simplified data access for all users
- D. Improved data security in dashboards
Answer: B
Explanation:
Connecting BI tools to Snowflake enables seamless integration and data visualization in dashboard creation.
NEW QUESTION # 29
When implementing data processing solutions, how does aggregating and enriching data contribute to the overall data analysis process?
- A. Increases data redundancy
- B. Limits data correlation possibilities
- C. Slows down data retrieval speed
- D. Enhances data granularity
Answer: D
Explanation:
Aggregating and enriching data enhances data granularity by providing more detailed and comprehensive insights into datasets, enabling better analysis and correlation of information.
NEW QUESTION # 30
What role do secure views play in data analysis practices?
- A. They prevent the creation of materialized views.
- B. Secure views limit access to data, hindering analysis.
- C. They don't impact data security but significantly enhance query performance.
- D. Secure views offer enhanced data security while allowing selective data access.
Answer: D
Explanation:
Secure views enhance data security by allowing selective data access while benefiting analysis.
NEW QUESTION # 31
You are tasked with analyzing website traffic patterns using Snowflake. The data is stored in a table 'WEB TRAFFIC' with columns 'VISIT DATE (DATE) and 'PAGE_VIEWS (NUMBER). You need to identify anomalies (unusually high or low traffic days) using the 'STDDEV POP function to calculate the standard deviation and flag days that fall outside a certain number of standard deviations from the mean. Which of the following SQL queries BEST implements this anomaly detection logic, flagging days with page views more than 2 standard deviations above the mean?
- A.
- B.
- C.
- D.
- E.
Answer: B
Explanation:
Option D correctly uses window functions to calculate the average and standard deviation across the entire dataset without grouping, then filters the rows where 'PAGE_VIEWS' are more than 2 standard deviations above the mean. Window functions (OVER ()) allow aggregate calculations without collapsing the rows, making it suitable for anomaly detection on a row-by-row basis. Option A and C incorrectly tries to use aggregate function without group by condition, and option B without CTE its creating ambiguity. Option E has logical issue.
NEW QUESTION # 32
When dealing with JSON data in Snowflake and utilizing built-in functions for traversing, flattening, and nesting, what complexities might arise? (Select all that apply)
- A. Complexity in handling nested JSON structures
- B. Challenges in handling specific data types only
- C. Limitations in processing specific JSON data sizes
- D. Difficulties in parsing and querying nested JSON objects
Answer: A,D
Explanation:
Handling nested JSON structures and complexities in parsing and querying nested JSON objects might pose challenges when manipulating JSON data in Snowflake using built-in functions.
NEW QUESTION # 33
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. 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.
- B. 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.
- C. 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.
- D. 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.
- E. 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$. Manually update the query for each page.
Answer: B,C
Explanation:
Option B is the most effective because it creates a view that aggregates the data, and then utilizes Snowsight dashboard variables for filtering, providing a dynamic and user-friendly experience. Option D is also correct because using a materialized view will significantly improve the performance of the query, especially when dealing with large datasets. Materialized views pre-compute and store the results of the query, making it faster to retrieve the data for the dashboard. Option A is inefficient as it requires manual updates to the query. Option C introduces unnecessary complexity and doesn't leverage Snowflake's built-in capabilities. Option E is overkill and less efficient for this simple scenario.
NEW QUESTION # 34
You are tasked with creating a data pipeline that ingests data from various sources, including a Snowflake Marketplace data share, and prepares it for analysis. The pipeline involves several transformations and enrichments. Which of the following methods offer the BEST approach to manage data lineage and auditability within this pipeline, considering the shared data from the Marketplace?
- A. Implement a custom logging system that records each transformation step and data source, including the data share details.
- B. Replicate the data share's tables into your own database and track changes on the replicated tables.
- C. Create a series of temporary tables at each stage of the pipeline to store intermediate results and track data lineage.
- D. Use Snowflake's 'SYSTEM$GET_PREDECESSORS' and functions combined with a metadata repository to capture and visualize data lineage.
- E. Rely solely on Snowflake's query history and table metadata to track data lineage.
Answer: D
Explanation:
Option C is the best approach. Snowflake's built-in functions like 'SYSTEM$GET_PREDECESSORS' and allow you to programmatically trace the dependencies and data flow within your Snowflake environment, including data accessed from shares. Combining this information with a metadata repository provides a robust and auditable data lineage solution. Option A is insufficient as it doesn't provide a structured and easily navigable lineage. Option B is viable but requires significant manual effort to maintain and scale. Option D creates unnecessary storage overhead and doesn't inherently improve data lineage tracking. Option E is not recommended as replicating shared data goes against the purpose of data sharing and can lead to synchronization issues.
NEW QUESTION # 35
You are tasked with loading multiple CSV files from an external stage into a Snowflake table. The files have the following naming convention: HHMlSS.csv' (e.g., 'data_20240101 120000.csv'). You need to load all the files that were created on or after '2024-01-01' into the 'target_table'. Which of the following approaches can be used to load those data?
- A. Using a single COPY INTO statement with a wildcard pattern in the file path and filtering in a subsequent INSERT INTO statement using a WHERE clause on the filename derived from METADATA$FILENAME.
- B. Using a loop with dynamic SQL to construct COPY INTO statements for each matching file based on the date substring.
- C. Using a Snowpipe with a file notification integration, configuring the notification to trigger only for files created on or after '2024-01-01'.
- D. Using a Snowflake task that runs daily, retrieves all the file names from the stage, filters the file names based on created date and loops through each filtered file name to load them
- E. Using a single COPY INTO statement with a pattern match against METADATA$FILENAM The matching logic is done inline with the COPY INTO statement using regex.
Answer: A,E
Explanation:
Option B is a viable solution. COPY INTO can be combined with METADATA$FILENAME filtering to load files matching a criteria. Option E provides a better, more efficient solution that leverages the 'METADATA$FILENAME' pseudo-column to filter files directly within the 'COPY INTO' statement using regular expressions or string functions to extract and compare the date from the filename. Option A will work but is very inefficient. Option C will work, but complex to maintain. Option D is more complex to setup than option E. Snowpipe generally does not accept a single COPY command but rather an ongoing copy operation
NEW QUESTION # 36
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