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Utilizing DAA-C01 Practice Questions - Get Rid Of SnowPro Advanced: Data Analyst Certification ExamWe will provide you with comprehensive study experience by give you DAA-C01 free study material & Snowflake exam prep torrent. The questions & answers from the Snowflake practice torrent are all valid and accurate, made by the efforts of a professional IT team. The authority and validity of Snowflake DAA-C01 training practice are the guarantee for all the IT candidates. We arrange our experts to check the update every day. Once there is any new technology about DAA-C01 Exam Dumps, we will add the latest questions into the DAA-C01 study pdf, and remove the useless study material out, thus to ensure the DAA-C01 exam torrent you get is the best valid and latest. So 100% pass is our guarantee. Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q48-Q53):NEW QUESTION # 48
You are designing a data ingestion pipeline to collect clickstream data from a high-traffic e-commerce website. You anticipate a daily data volume that fluctuates significantly, ranging from 5 TB to 20 TB. To optimize Snowflake costs and ensure efficient data loading, which combination of Snowflake features and ingestion methods would be MOST effective for identifying the accurate data volume before the data is fully loaded into a production table?
A. Rely solely on Snowsight's query history and data loading metrics after the data has been loaded into Snowflake, adjusting future ingestion parameters based on historical data.
B. Use Snowflake's Resource Monitors to track credit usage during data loading. Configure a data pipeline using Snowpipe with auto-ingest enabled. Rely solely on Snowflake's cost estimation tools to predict volume.
C. Utilize a cloud-based message queue (e.g., AWS SQS, Azure Queue Storage) to buffer incoming data. Implement a separate Snowflake task to periodically query the message queue's approximate message count and size. Combine this with Snowsight monitoring of storage costs for the raw data stage.
D. Implement Snowpipe with pre-validation using a data transformation tool like dbt Core. Create an external function to execute a sampling query against a percentage of files in cloud storage and estimate the total volume. Load a smaller sample data to a staging table and then use 'TABLE SIZE' to get the actual size and scale it up.
E. Use Snowflake Tasks to schedule a Python script that reads file metadata (size, number of files) from the cloud storage location using the cloud provider's SDK (e.g., bot03 for AWS S3, azure-storage-blob for Azure Blob Storage). Store the collected metadata in a Snowflake table. Then, use SQL queries to calculate the total data volume.
Answer: E
Explanation:
Option D is the most effective because it directly measures the volume of data before it is fully loaded into Snowflake, allowing for accurate volume identification and cost optimization. Using cloud provider SDK to check the data volume will help to estimate cost and volume.
NEW QUESTION # 49
A large retail company is migrating its transaction data to Snowflake and wants to build a consumption layer for BI reporting. They have historical data with frequent updates and require both point-in-time analysis and trend analysis. Which modeling technique(s) would be MOST suitable for this scenario, considering performance, storage efficiency, and the need for both historical tracking and current state views?
A. Star Schema with Slowly Changing Dimension Type 0 (SCDO) for critical dimensions.
B. A wide, denormalized table created using CREATE TABLE AS SELECT (CTAS) statement, refreshed nightly.
C. Data Vault modeling, with point-in-time tables built on top of the Data Vault for Bl reporting.
D. Snowflake Schema with Slowly Changing Dimension Type 2 (SCD2) for relevant dimensions.
E. Star Schema with Slowly Changing Dimension Type 1 (SCDI) for all dimensions.
Answer: C,D
Explanation:
Data Vault provides the historical tracking and auditability needed, while point-in-time tables on top optimize for BI queries. Snowflake schema with SCD2 also allows historical tracking within a dimensional model. SCDI alone will not preserve history, and SCDO locks in attributes indefinitely. A wide denormalized table is not suitable for historical analysis or frequent updates and can lead to redundancy. Therefore options B and C are suitable for BI reporting.
NEW QUESTION # 50
Consider the following SQL query:
Analyzing the Query Profile, you observe that the 'WHERE' clause is not effectively filtering the data'. Which of the following actions could improve performance, assuming the 'order date' column is NOT currently clustered or indexed?
A. Create a materialized view with the 'WHERE clause condition and relevant columns.
B. Increase the virtual warehouse size.
C. Partition the 'orders' table by 'order_date' .
D. Create a secondary index on the 'order_date' column.
E. Cluster the 'orders' table on the 'order date' column.
Answer: A,E
Explanation:
Clustering on 'order_date' will physically organize the data on disk according to the date, allowing Snowflake to efficiently retrieve the relevant data based on the 'WHERE clause. Creating a materialized view pre-computes the result set based on the filter, so the query would only need to retrieve the top 10 from that smaller dataset. Secondary indexes are not supported in Snowflake. Partitioning is not a feature in Snowflake. Increasing the warehouse size might help with processing power, but it won't directly improve data access efficiency.
NEW QUESTION # 51
You are preparing to load data from an internal stage that contains multiple CSV files into a Snowflake table. You need to ensure that only files matching a specific pattern are loaded (e.g., files starting with 'sales_'). You also need to transform the date column (located in the third position of each CSV file) from 'YYYYMMDD' to 'YYYY-MM-DD' format during the load. The target table 'SALES has columns 'product_id', and 'region'. Which combination of options correctly filters the files and transforms the date format during the load process?
A. Option C
B. Option B
C. Option E
D. Option A
E. Option D
Answer: E
Explanation:
Option D is correct. It uses the 'PATTERN' parameter to filter files starting with 'sales_' and ending with '.csv'. It then uses the TRANSFORM AS SELECT clause to select and transform the data. Inside the select clause, it correctly uses 'substr' to reformat the date and 'TO_DATE to convert the re-formatted string into a DATE datatype. The 'ON_ERROR = 'SKIP_FILE" is a good practice for handling potential errors. Option A has an invalid parameter declaration within the COPY INTO command and does not use PATTERN, making the filter inaccurate. Option B attempts to define date format at file format level, but date transformatinon is required and FILES filter declaration is invalid. Option C uses 'TRANSFORMATION' instead of 'TRANSFORM AS SELECT , which is incorrect and will cause an error. Option E attempts to define DATE FORMAT with the wrong TYPE and is missing concatination of DATE string.
NEW QUESTION # 52
When maintaining reports and dashboards, why is it essential to build automated and repeatable tasks?
A. Automated tasks reduce manual efforts, ensuring consistency.
B. They ensure inconsistency in reports and dashboards.
C. Repeatable tasks hinder data updates in dashboards.
D. Automated tasks increase dashboard management complexity.
Answer: A
Explanation:
Automated tasks reduce manual efforts, ensuring consistency in reports and dashboards.
NEW QUESTION # 53
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