2025 DAA-C01 Valid Test Voucher | High Pass-Rate DAA-C01 Reliable Dumps Ebook: SnowPro Advanced: Data Analyst Certification Exam

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q55-Q60):

NEW QUESTION # 55
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 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.
C. 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.
D. 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'.
E. 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.

Answer: E

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 # 56
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 pie chart showing the percentage contribution of each metric to the total.
B. A bar chart comparing the average values of each metric.
C. A histogram showing the distribution of individual Metrics.
D. A scatter plot matrix showing the pairwise relationships between all metrics.
E. A line chart showing each metric over time.

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 # 57
You have a stored procedure written in Python within Snowflake that needs to process a large dataset. The procedure's performance is critical, and you want to optimize it for speed. Which of the following strategies would be MOST effective in improving the performance of your Python stored procedure when dealing with large datasets in Snowflake? (Select TWO)

A. Increasing the warehouse size for the Snowflake account where the stored procedure is running. A larger warehouse provides more compute resources, which can speed up data processing tasks, including Python stored procedures.
B. Leveraging Pandas DataFrames within the Python stored procedure to perform data manipulation tasks in a vectorized manner. Pandas provides efficient data structures and functions for data processing, which can significantly improve performance compared to using standard Python loops.
C. Converting the Python stored procedure to a SQL stored procedure, as SQL is generally faster for data manipulation tasks within Snowflake due to its optimized query engine.
D. Using the Snowflake Python Connector's 'execute_stream' method to stream data in chunks instead of loading the entire dataset into memory at once. This reduces memory consumption and improves performance, especially for very large datasets.
E. Replacing all Python loops with recursive SQL queries to improve performance

Answer: B,D

Explanation:
Options A and C are the most effective strategies. Option A, 'execute_stream' , is ideal for large datasets as it avoids loading everything into memory. Option C, leveraging Pandas DataFrames for vectorized operations, significantly speeds up data manipulation within Python. Option B is partially correct. SQL is often faster, but rewriting a complex Python procedure in SQL can be time-consuming and may not always be feasible or optimal. Option D, increasing the warehouse size, can help, but it's more of a general performance improvement rather than a specific optimization for Python stored procedures. Also it will cost more. E is incorrect as recursive SQL queries, while sometimes useful, aren't a general-purpose replacement for Python loops and can be less efficient for complex logic.

NEW QUESTION # 58
You're analyzing a sales dataset in Snowflake. The dataset contains 'customer id', 'product id', 'sale date', and 'sale amount'. You've noticed that some 'sale_amount' values are negative, which is illogical. You also suspect duplicate records, indicated by identical 'customer_id', 'product_id', and 'sale_date'. Additionally, a business rule dictates that sale amounts must be less than $10,000. Which combination of Snowflake SQL statements is the MOST efficient and accurate way to address these data quality issues?

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

Answer: D

Explanation:
Option E correctly handles all specified data quality issues. It replaces negative sale amounts with 0, enforces the business rule (sale_amount < 10000), and removes duplicate records based on 'customer_id', 'product_id', and 'sale_date' using the QUALIFY clause and ROW_NUMBER() window function, selecting only the first occurrence of each duplicate set. Options A and D do not handle negative sales amount correctly, Option B uses IFF(sale_amount < O, O, sale_amount) but do not handle boundary values of sale_amount < 10000, Option C considers sale amount between 0 and 10000.

NEW QUESTION # 59
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. 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.
B. Use the function to download the model's internal representation and analyze it using external machine learning libraries.
C. 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.
D. Use the 'DESCRIBE MODEL CHURN FORECAST MODEL' command to retrieve model statistics such as the features used and their importance in the model.
E. 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).

Answer: C

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 # 60
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2025 DAA-C01 Valid Test Voucher | High Pass-Rate DAA-C01 Reliable Dumps Ebook: SnowPro Advanced: Data Analyst Certification Exam

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q55-Q60):

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