Reliable DAA-C01 Cram Materials, Lab DAA-C01 Questions

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Snowflake DAA-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Perform Descriptive and Diagnostic Analysis	10–15%	- Anomaly detection and root cause analysis - Statistical summarization and trend analysis - Exploratory and ad-hoc analysis
Prepare and Load Data	15–20%	- External tables and data validation - File formats: CSV, JSON, Parquet, Avro - Data ingestion methods: COPY INTO, stages, Snowpipe
Perform Simple Data Transformations for Analysis	15–20%	- Data cleansing, standardization, type conversion - Handling NULLs and structuring datasets - Views, materialized views, CTEs
Build and Troubleshoot Advanced SQL Queries	20–25%	- Semi-structured data processing - Query optimization and troubleshooting - Complex joins, subqueries, window functions
Prepare and Present Data	10–15%	- Data visualization and reporting - Snowsight dashboards and sharing results - Align outputs with business requirements
Use Built-in Functions and Create UDFs	10–15%	- Scalar, aggregate, table, system functions - User-Defined Functions (UDFs)
Perform Predictive Analysis	5–10%	- Using Snowflake ML and built-in analytics - Forecasting and predictive modeling

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Lab DAA-C01 Questions & Latest DAA-C01 Exam Duration

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q66-Q71):

NEW QUESTION # 66
You are tasked with creating a Snowsight dashboard to monitor the daily sales performance of an e-commerce platform. The sales data is stored in a table named 'SALES_DATR with columns 'SALE_DATE' (DATE), 'PRODUCT_ID' ONT), 'SALES AMOUNT' (FLOAT), and 'CUSTOMER REGION' (VARCHAR). You need to display a trendline chart showing the total sales amount for each day over the last 30 days, and also a table showing the top 5 regions by total sales amount. Which of the following steps are most efficient to achieve this in Snowsight? (Select TWO)

A. Create a single tile using a combination chart in Snowsight. Use 'SALE DATE as the x-axis and 'SUM(SALES AMOUNT)' as the y-axis, specifying a trendline. Then, add a second series to display region-based sales in a separate panel below.
B. Create two separate tiles in the Snowsight dashboard. One tile should use a time series chart visualizing 'SELECT SALE_DATE, SUM(SALES_AMOUNT) FROM SALES_DATA WHERE SALE_DATE DATEADD(day, -30, GROUP BY SALE_DATE ORDER BY SALE_DATE;' and the other tile should use a table visualizing 'SELECT CUSTOMER REGION, SUM(SALES AMOUNT) FROM SALES DATA GROUP BY CUSTOMER REGION ORDER BY DESC LIMIT 5;'
C. Create a single tile with two tabs. The first tab displays the trendline chart using a time series and the second tab displays the region-based sales table. Both tabs use the same underlying SQL query but filter the results differently.
D. Use two separate worksheets in Snowsight. In the first worksheet, create the time series chart. In the second worksheet, create the region-based sales table. Then, embed one worksheet inside the other to create a combined dashboard.
E. Use the 'Notebook' feature in Snowsight to first perform the aggregations required to obtain the data for the visualizations, and then create visualization tiles by referencing the calculated dataframes. No direct SQL is used in the tiles.

Answer: B,E

Explanation:
Option A is correct because it directly and efficiently creates the desired visualizations in Snowsight using separate tiles. Option C is also correct. Using Snowsight's Notebook feature allows for pre-aggregation, which can be useful in complex scenarios where directly visualizing from the raw data would impact dashboard performance. Options B, D, and E are either not directly supported or less efficient methods within the current Snowsight dashboarding capabilities.

NEW QUESTION # 67
You are tasked with ingesting data from a REST API that returns data in JSON format. The API requires authentication via an API key passed in the header and returns a large dataset, potentially exceeding Snowflake's recommended file size for optimal loading. Furthermore, the API has rate limits, and exceeding them results in temporary blocking. What is the MOST efficient and reliable way to retrieve this data into Snowflake?

A. Utilize Snowpipe with a REST API integration configured directly with the API endpoint. Snowflake will automatically handle the data retrieval and loading.
B. Create a Python script using Snowpark that connects to the API, handles authentication, rate limiting, and partitions the data into smaller files. Stage these files in cloud storage and then use COPY INTO to load them into Snowflake.
C. Employ a third-party ETL tool that supports REST API connections, data partitioning, rate limiting, and Snowflake integration. Configure the tool to extract, transform, and load the data into Snowflake.
D. Use a Snowflake external function to directly call the API within a COPY INTO statement. Handle authentication and rate limiting within the external function's code.
E. Write a Snowpark stored procedure that invokes a Java UDF to connect to the REST API. Handle rate limiting and authentication within the Java UDF and use it to insert data directly into a Snowflake table.

Answer: B,C

Explanation:
Options B and D offer the most robust solutions. Option B, using a Python script with Snowpark, allows for fine-grained control over authentication, rate limiting, and data partitioning before loading into Snowflake. Option D, utilizing a dedicated ETL tool, provides a pre-built and often more scalable solution for complex data ingestion scenarios, including handling authentication, rate limits, and data transformation. Option A is less scalable and maintainable. Option C is incorrect because Snowpipe is designed for continuous loading from cloud storage, not directly from REST APIs. Option E is less efficient because it uses Java UDF inside stored procedure.

NEW QUESTION # 68
A Data Analyst runs this query:

The Analyst men runs this query:

What will be the output?

Answer: D

Explanation:
Understanding how Snowflake aggregate functions like MIN() and MAX() handle numerical data and NULL values is fundamental for accurate Data Analysis. In this scenario, we have a table with five records distributed across two departments.
The MIN() function returns the smallest non-null value in the specified column across all rows in the group (or the entire table, if no GROUP BY is present). Looking at the salary column in the employees table, the values are: 10000, 9000, 8000, 15000, and NULL. The NULL value is ignored by the calculation. Among the remaining numerical values, 8000 is the smallest. Therefore, MIN_VAL will be 8000.
The MAX() function operates similarly, returning the largest non-null value from the set. Comparing the same list of numerical values-10000, 9000, 8000, and 15000-the largest value is clearly 15000. Consequently, MAX_VAL will be 15000.
Evaluating the Options based on the exhibit (image_8ca0df.png):
* Option A incorrectly identifies the minimum and maximum based on the employee_id column (where
2000 is max and 900 is min), rather than the requested salary column.
* Option B is incorrect as it seems to mix values from different columns or specific rows.
* Option C is incorrect because it mistakenly suggests that MIN() returns NULL if a NULL value is present in the column. In Snowflake, standard aggregate functions (except for specialized ones like ARRAY_AGG or certain window functions with specific clauses) skip NULL values entirely.
* Option D is the 100% correct output. It displays MIN_VAL as 8000 and MAX_VAL as 15000, which accurately reflects the mathematical minimum and maximum of the non-null entries in the salary column.
This behavior is consistent across all relational databases adhering to ANSI SQL standards, which Snowflake follows for these core aggregate operations.

NEW QUESTION # 69
You have identified corrupted data in a production table 'CUSTOMER DATA. Before attempting to clean the data directly in the production table, you want to create a safe environment to test your data cleaning scripts. You are also concerned about the impact of your data cleaning efforts on downstream reporting. Which of the following approaches using Snowflake clones is the MOST appropriate for this scenario?

A. Create a full copy of 'CUSTOMER DATA' named 'CUSTOMER DATA DEV' for testing. Clean the data in 'CUSTOMER DATA DE-VS. Use a 'MERGE statement to update with the cleaned data from
B. Create a zero-copy clone of named for testing. Create another zero-copy clone of 'CUSTOMER DATA DEV' named 'CUSTOMER DATA REPORTING'. Clean the data in 'CUSTOMER DATA DENT. Point downstream reporting to 'CUSTOMER DATA REPORTING'.
C. Create a zero-copy clone of 'CUSTOMER DATA' named 'CUSTOMER DATA DEV' for testing. Clean the data in 'CUSTOMER DATA DEV'. Create a zero- copy clone of 'CUSTOMER_DATX named Update 'CUSTOMER_DATX with the cleaning logic. Point the downstream reporting to 'CUSTOMER DATA REPORTING'.
D. Create a zero-copy clone of 'CUSTOMER_DATA' named for testing. Clean the data in 'CUSTOMER_DATA_DEV'. Once satisfied, update the 'CUSTOMER_DATR table directly with the cleaning logic.
E. Create a zero-copy clone of named for testing. Clean the data in Create a separate table named 'CLEANED CUSTOMER DATA'. Insert the cleaned data from 'CUSTOMER DATA DEV' into the new 'CLEANED CUSTOMER DATA' table. Update with the cleaning logic.

Answer: C

Explanation:
Option D is the most appropriate and safely covers all aspects. Cloning to lets you experiment with cleaning. The most important part of the question is to handle the downstream reporting. So cloning 'CUSTOMER DATA' to lets you test how your new updates will affect the reports that depend on the data. Updating the 'CUSTOMER_DATR with the cleaning logic lets you apply the tested data cleaning. The other options do not protect the production reporting from potentially breaking changes during the data cleaning process. They may also directly update the production data, increasing risk. In option B, even though you are pointing to the new cloned reporting table, since that is created from DEV table it will already have changed data, and we want to report on the original, not the one with the dev changes. Option E does not discuss downstream impact on the reports, so this is not fully addressing all the impacts.

NEW QUESTION # 70
While reviewing the query profile for a complex data transformation pipeline, you notice a significant amount of time spent in the 'Join' operation between two large tables, 'transactions' and 'customers'. The join is performed on the 'customer _ id' column. Which of the following are potential strategies to optimize the join performance?

A. Ensure that the 'customer_id' column has identical data types in both tables.
B. Cluster both tables on the 'customer id' column.
C. Broadcasting the smaller table to all compute nodes.
D. Increase the size of the virtual warehouse.
E. Rewrite the query to use a 'LATERAL FLATTEN' function instead of a 'JOIN'.

Answer: A,B

Explanation:
Mismatched data types can cause implicit type conversions, which can significantly degrade join performance. Clustering both tables on the join key ('customer_id') will ensure that rows with the same customer ID are located closer together on disk, reducing 1/0 and improving join efficiency. Broadcasting the smaller table is not directly controllable by the user in Snowflake and is handled automatically by the optimizer. 'LATERAL FLATTEN' is not a direct replacement for a join. Increasing the virtual warehouse size might improve overall processing time but doesn't address the specific inefficiency of the join operation.

NEW QUESTION # 71
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