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[General] Hot DAA-C01 Free Learning Cram | Valid Snowflake DAA-C01: SnowPro Advanced: Data

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【General】 Hot DAA-C01 Free Learning Cram | Valid Snowflake DAA-C01: SnowPro Advanced: Data

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P.S. Free & New DAA-C01 dumps are available on Google Drive shared by ExamsLabs: https://drive.google.com/open?id=1svGqukMX6reKOXJxUstrIv847OfbbdX9
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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q132-Q137):NEW QUESTION # 132
What role does operationalizing data play in maintaining reports and dashboards for business requirements?
  • A. Operationalizing data ensures consistent and efficient usage.
  • B. It restricts data accessibility, affecting dashboard usability.
  • C. Operationalizing data complicates dashboard management.
  • D. It limits data updates, affecting dashboard accuracy.
Answer: A
Explanation:
Operationalizing data ensures consistent and efficient usage of reports and dashboards.

NEW QUESTION # 133
In Snowflake, how does leveraging Time Travel contribute to handling data anomalies and ensuring data integrity?
  • A. Restricts data access for specific user roles
  • B. Enables querying data as of specific timestamps
  • C. Validates data types for consistency
  • D. Provides real-time data updates
Answer: B
Explanation:
Time Travel in Snowflake allows querying data as of specific timestamps, aiding in addressing data anomalies and ensuring data integrity by accessing historical data versions.

NEW QUESTION # 134
You are tasked with creating a data model for a global e-commerce company in Snowflake. They have data on customers, products, orders, and website events. They need to support complex analytical queries such as 'What are the top 10 products purchased by customers in the US who have visited the website more than 5 times in the last month?' The data volumes are very large, and query performance is critical. Which of the following data modeling techniques and Snowflake features, used in combination, would be MOST effective?
  • A. A data vault model, combined with Snowflake's search optimization service on the hub tables.
  • B. A wide, denormalized table containing all customer, product, order, and event data, combined with Snowflake's zero-copy cloning for data backups.
  • C. A star schema with fact and dimension tables, combined with clustering the fact table on a composite key of customer ID and product ID.
  • D. A star schema with fact and dimension tables, combined with materialized views to pre-aggregate data and clustering on dimension keys in the fact table.
  • E. A fully normalized relational model with primary and foreign key constraints, combined with Snowflake's automatic query optimization.
Answer: C,D
Explanation:
Options B and E are the most effective. A star schema (B and E) is well-suited for analytical workloads. Clustering the fact table on customer and product IDs (B) helps improve query performance when filtering on those dimensions. Materialized views (E) provide pre-aggregated data for common queries, further boosting performance. Normalization (A) can lead to too many joins. Data Vault (C) is complex and may not be necessary. A wide, denormalized table (D) can be difficult to manage and maintain, and zero-copy cloning is for backup, not performance. Clustering on dimension keys in the fact table works best when coupled with a star schema and the keys are frequently used as a filter.

NEW QUESTION # 135
Why are Stored Procedures valuable in data analysis using SQL?
  • A. They restrict the execution of repetitive tasks, limiting efficiency.
  • B. Stored Procedures enable custom and repeated data operations, enhancing efficiency.
  • C. Stored Procedures solely facilitate data visualization.
  • D. They are exclusively used for one-time data operations.
Answer: B
Explanation:
Stored Procedures aid in data analysis by enabling custom and repeated data operations, enhancing efficiency.

NEW QUESTION # 136
You are using Snowpipe to continuously load data from an external stage (AWS S3) into a Snowflake table named 'RAW DATA. You notice that the pipe is frequently encountering errors due to invalid data formats in the incoming files. You need to implement a robust error handling mechanism that captures the problematic records for further analysis without halting the pipe's operation. Which of the following approaches is the MOST effective and Snowflake-recommended method to achieve this?
  • A. Utilize Snowpipe's 'VALIDATION_MODE' parameter set to to identify and handle invalid records. This requires modification of the COPY INTO statement to redirect errors to an error table.
  • B. Implement Snowpipe's 'ERROR _ INTEGRATION' object, configuring it to automatically log error records to a designated stage location in JSON format for later analysis. This requires updating the pipe definition.
  • C. Implement a custom error logging table and modify the Snowpipe's COPY INTO statement to insert error records into this table using a stored procedure called upon failure.
  • D. Disable the Snowpipe and manually load data using a COPY INTO statement with the 'ON_ERROR = 'SKIP_FILE" option, then manually inspect the skipped files.
  • E. Configure Snowpipe's 'ON_ERROR parameter to 'CONTINUE' and rely on the 'SYSTEM$PIPE_STATUS' function to identify files with errors. Then, manually query those files for problematic records.
Answer: B
Explanation:
Snowflake's 'ERROR INTEGRATION' feature, when configured with a pipe, automatically logs details of records that fail during ingestion to a specified stage. This provides a structured and readily accessible log of errors without interrupting the data loading process. Option A is not a native feature. Option B, while potentially usable, doesn't directly integrate with pipes as the PRIMARY mechanism. Option C involves more manual intervention and doesn't offer structured error logging. Option E defeats the purpose of automated loading via Snowpipe.

NEW QUESTION # 137
......
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