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Reliable DAA-C01 Exam Answers - New DAA-C01 Test Notes

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q172-Q177):

NEW QUESTION # 172
A data analyst is tasked with creating a near-real-time dashboard using Streamlit and Snowflake to monitor website traffic. The website traffic data is continuously ingested into a Snowflake table named 'WEB TRAFFIC EVENTS' with columns 'EVENT TIME' (TIMESTAMP LTZ), 'PAGE URL' (VARCHAR), and 'USER ID' (VARCHAR). The analyst wants to ensure the Streamlit dashboard automatically reflects the latest data in Snowflake with minimal latency. Which of the following approaches would provide the MOST efficient and near-real-time data updates in the Streamlit dashboard?

A. Use Streamlit's to directly edit data in the Snowflake table from the dashboard. This provides real-time updates by allowing users to modify the data directly.
B. Leverage Snowflake's Change Data Capture (CDC) capabilities to track changes in the ' table. Streamlit can then query the CDC stream to retrieve only the changes since the last refresh, minimizing the amount of data transferred and processed. Utilize 'st.cache_data' with a short TTL.
C. Configure Streamlit to use the Snowflake Connector for Python with auto-commit enabled. This ensures that any changes made to the data in Streamlit are immediately reflected in Snowflake.
D. Use Streamlit's 'st.cache_data' decorator with a long TTL (time-to-live) on a function that queries Snowflake directly for each dashboard refresh. This will cache the query results for a longer period, reducing the load on Snowflake.
E. Implement Snowflake's Snowpipe to continuously load data into a separate summary table containing pre-aggregated metrics (e.g., page views per minute). Streamlit can then query this summary table frequently without impacting Snowflake's performance. Use 'st.cache_data' with a short TTL to refresh the data quickly.

Answer: B

Explanation:
Leveraging Snowflake's CDC capabilities is the most efficient way to achieve near-real-time data updates in Streamlit. CDC allows you to retrieve only the changes made to the table since the last refresh, significantly reducing the amount of data transferred and processed compared to querying the entire table each time. Snowpipe is a good option, but is more oriented to data ingestion and not data changes. Option A caches results, which is opposite of real time. Option B is useful, but more complex than needed. Option C is not related to the problem. Option D is incorrect because 'auto-commit' applies only to data updates from the application to Snowflake. Streamlit can then query this CDC stream to get an updated set of data. Using 'st.cache_data' with a short TTL ensures the dashboard reflects recent data changes from the CDC Stream.

NEW QUESTION # 173
In Snowsight, what is the significance of creating diverse chart types (e.g., bar charts, scatter plots, heat grids) for data visualization?

A. Snowsight doesn't support multiple chart types for visualization.
B. Different chart types offer varied data representation for better analysis.
C. It limits data presentation options for complex datasets.
D. It restricts users to specific chart types for simplicity.

Answer: B

Explanation:
Diverse chart types offer varied data representation, facilitating better analysis in Snowsight.

NEW QUESTION # 174
When performing a diagnostic analysis, what action aids in identifying demographics and relationships? (Select all that apply)

A. Analyzing statistical trends
B. Ignoring data relationships for focused analysis
C. Collecting related data
D. Focusing solely on isolated data points

Answer: A,C

Explanation:
Analyzing statistical trends and collecting related data are crucial in identifying demographics and relationships in diagnostic analysis.

NEW QUESTION # 175
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. Parquet format optimizes query performance and reduces storage requirements
C. CSV format enhances query performance and minimizes storage needs
D. JSON format accelerates query execution but increases storage requirements

Answer: B

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 # 176
You are working with a Snowflake table 'raw_data' containing a column of type TEXT that stores log messages in various formats, including JSON and CSV. You need to extract specific data points from these log entries, treating JSON entries differently from CSV entries. Specifically: For JSON log entries (identified by starting with '{l), extract the value of the 'user_id' key. For CSV log entries (identified by starting with a number), extract the second field (assuming comma-separated values). Which of the following queries is the most efficient and correct way to achieve this data extraction?

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

Answer: C

Explanation:
Option B provides the most efficient and correct solution. 1, 1) = '{' ' efficiently checks if the log entry starts with '{' without needing the full power of STARTS_WITH or PARSE_JSON. For JSON entries, it correctly uses to extract the 'user_id' directly from the VARIANT representation. For CSV entries, it uses ',', 2)' which is specifically designed to extract a single part from a delimited string, and is more efficient than using SPLIT. Option A will not treat the JSON objects in the 'log_entry' column as valid variants and needs to be parsed using PARSE_JSON explicitly to get correct answer. Option C uses GET _ PATH which cannot be used without parsing the JSON first. Option D will error out because you cannot use JSON dot notation on a TEXT column directly. Option E uses SPLIT TO TABLE which is more costly than SPLIT PART.

NEW QUESTION # 177
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