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【General】 DAA-C01 Exam Questions Fee - 100% Valid Questions Pool

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q43-Q48):NEW QUESTION # 43
You are designing a data pipeline to ingest JSON data from an external stage (AWS S3) into a Snowflake table called 'ORDERS' Some of the JSON files contain nested arrays that need to be flattened and transformed during the loading process. You have already defined a VARIANT column in the 'ORDERS table to store the raw JSON data'. However, occasionally, some files fail to load completely, and the 'SYSTEM$PIPE STATUS' shows a 'LOAD FAILED' status without providing granular details about the specific records causing the failure. Which of the following strategies, used IN COMBINATION, would be MOST effective in troubleshooting and resolving these failures while minimizing the impact on the overall data ingestion process?
  • A. Enabling the ERROR=CONTINUE parameter on the COPY INTO statement used in the pipe, and regularly querying the function to identify issues in the loaded data.
  • B. Enable Snowpipe's 'ERROR_INTEGRATION' , examine the error logs for malformed JSON records, and adjust the COPY INTO statement with appropriate FILE_FORMAT options to handle the nested arrays.
  • C. Use a 'VALIDATE' statement with the same COPY INTO statement to identify records that will fail. Then, modify the COPY INTO statement to handle the errors or exclude the problematic records using a 'WHERE clause in the transformation logic of the COPY INTO statement or inline SQL Transformations
  • D. Implement a pre-processing step using a Snowflake task to validate the JSON data before loading it into the 'ORDERS' table using a COPY INTO statement. The Task would filter out bad records.
  • E. Increase the compute resources allocated to the virtual warehouse used by the Snowpipe. Also, disable Snowpipe and load the data manually using the COPY INTO command to identify any errors during load.
Answer: B,C
Explanation:
ERROR INTEGRATION' allows you to inspect individual error records and identify patterns in those failing files. The 'VALIDATE' function allows you to perform a COPY INTO using similar parameters as your copy into statement to validate the record, and helps you tune your data pipeline for errors. Option B is viable, but has increased maintenance overhead compared to VALIDATE, because you would need to write code for the preprocessing. Option D focuses on resource allocation, which doesn't directly address data quality issues. Option E by itself only attempts to continue, and doesn't do any validation. 'ON is a good idea when paired with validating the data after the load.

NEW QUESTION # 44
You are responsible for maintaining a dashboard displaying real-time website traffic data'. The data is ingested into a Snowflake table named 'WEB EVENTS using Snowpipe from cloud storage. The 'WEB EVENTS' table includes 'EVENT TIMESTAMP' , 'PAGE URL' , and 'USER ID columns. The dashboard requires near real-time updates, but you are noticing significant latency. Which of the following actions, performed in isolation, is LEAST likely to improve the dashboard update frequency?
  • A. Increase the size of the Snowflake virtual warehouse used for Snowpipe data loading.
  • B. Create a materialized view that pre-aggregates the data needed for the dashboard and configure it for automatic refresh.
  • C. Refactor dashboard queries to directly query the 'WEB EVENTS' table without any aggregations or transformations.
  • D. Reduce the frequency of micro-batch data loading by increasing the Snowpipe 'AUTO_INGEST schedule to reduce the number of pipe executions per minute.
  • E. Optimize the Snowpipe configuration by adjusting the 'COPY INTO' statement to use file format options appropriate for the source data (e.g., compression, field delimiters).
Answer: D
Explanation:
Reducing the frequency of micro-batch data loading (option E) is LEAST likely to improve dashboard update frequency; in fact, it will decrease it. The dashboard needs near real-time updates, so reducing how often data is loaded will directly conflict with that requirement. All the other options are designed to help with optimization. Optimizing Snowpipe configuration, creating materialized views for pre-aggregation, and increasing warehouse size for Snowpipe can help, too. Reducing the amount of aggregations that dashboard needs will help since less processing is necessary.

NEW QUESTION # 45
A financial institution uses Snowflake to store customer transaction data'. They need to create a dashboard that visualizes daily transaction volume and average transaction amount for fraud detection purposes. This dashboard needs to be automatically updated every hour. The current dashboard query performance is slow, especially during peak hours. Given that the 'TRANSACTIONS table contains billions of rows, which of the following strategies would BEST optimize both the query performance and the automated update process?
  • A. Create a regular view that calculates daily transaction volume and average transaction amount. Use a Snowflake stored procedure to execute the query and update a separate reporting table hourly.
  • B. Create a temporary table that stores daily transaction summaries. Truncate and reload the temporary table hourly using a scheduled Snowflake task.
  • C. Implement caching within the dashboard application to store the query results and only refresh the data once a day to avoid performance issues
  • D. Create a materialized view that pre-aggregates the daily transaction volume and average transaction amount. Schedule a Snowflake task to refresh the materialized view hourly.
  • E. Increase the warehouse size to X-Large and rely on Snowflake's query optimization engine without any changes to the data model or update process.
Answer: D
Explanation:
Materialized views are designed for pre-computation of aggregations, providing significant performance improvements. Scheduling a task to refresh the materialized view ensures automatic updates. Regular views are calculated at query time and would not improve performance. Increasing warehouse size (C) might help, but it's not the most efficient solution. Temporary tables are not persistent and truncation/reload is inefficient. Dashboard caching (E) does not solve underlying Snowflake performance issues.

NEW QUESTION # 46
You are tasked with enriching a 'SALES DATA' table in Snowflake with geographic information based on IP addresses. You have access to an external function 'GEO LOOKUP(ip_address)' that returns a JSON object containing geographical details (city, region, country) for a given IP address. The 'SALES DATA' table contains 'SALE D', 'CUSTOMER D', ADDRESS', and 'SALE AMOUNT columns. You need to enrich the table with city and country information derived from the IP address. Which of the following statements will correctly add 'CITY' and 'COUNTRY columns to a new table 'ENRICHED SALES DATA based on the external function 'GEO LOOKUP , correctly handling potential NULL values and ensuring data type consistency?

  • A. Option A
  • B. Option C
  • C. Option B
  • D. Option D
  • E. Option E
Answer: D
Explanation:
Option D is the most robust solution. It extracts the city and country values from the JSON object returned by the function using the operator. The cast ensures the data is stored as strings. Critically, it uses to handle cases where the 'GEO_LOOKUP' function might return NULL (e.g., for invalid IP addresses), preventing errors and providing a default value ('Unknown'). Option A does not handle NULL, Option B's 'GET_PATH' is not a standard Snowflake function for JSON parsing, Options C parses the GEO LOOKUP output to json format if its not which can result in the 'CITY' and 'COUNTRY becoming 'NULL'. The option E 'PARSE_JSON' would throw errors on invalid json strings in the ip address.

NEW QUESTION # 47
A Data Analyst runs a query in a Snowflake worksheet, and selects a numeric column from the result grid.
What automatically-generated contextual statistic can be visualized?
  • A. A histogram, displayed for all numeric, date, and time columns
  • B. A frequency distribution, displayed for all numeric columns
  • C. A key distribution
  • D. MIN/MAX values for the column
Answer: A
Explanation:
One of the standout features of the Snowsight interface is its ability to perform automatic Data Profiling.
When a Data Analyst executes a query, Snowflake doesn't just return a raw grid of data; it analyzes the result set to provide immediate visual insights.
When you click on a column header in the results pane, a summary statistics panel appears. For numeric, date, and time columns, Snowflake automatically generates a histogram (Option A). This histogram provides a visual representation of the data distribution, allowing the analyst to quickly identify patterns, concentrations of values, or significant outliers without writing additional SQL code.
Evaluating the Options:
* Option B: While a histogram is a type of frequency distribution, Option A is more accurate because Snowsight also provides these visualizations for date and time types, not just integers/floats.
* Option C: While MIN and MAX values are displayed in the summary panel, they are text-based statistics, not the "visualized" contextual statistic (the histogram) emphasized in the question.
* Option D: "Key distribution" is not a standard visualization term used in the Snowsight profiling tool.
* Option A: Is the 100% correct answer. It highlights the breadth of the profiling tool (covering numbers, dates, and times) and the specific visual element (the histogram) that makes exploratory data analysis significantly faster for a Data Analyst.

NEW QUESTION # 48
......
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