NEW SNOWFLAKE DAA-C01 DUMPS QUESTIONS | DAA-C01 EXAM TUTORIAL

New Snowflake DAA-C01 Dumps Questions | DAA-C01 Exam Tutorial

New Snowflake DAA-C01 Dumps Questions | DAA-C01 Exam Tutorial

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Tags: New DAA-C01 Dumps Questions, DAA-C01 Exam Tutorial, DAA-C01 Exam Book, Reliable DAA-C01 Exam Camp, Test DAA-C01 Engine Version

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DAA-C01 Exam Tutorial & DAA-C01 Exam Book

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q252-Q257):

NEW QUESTION # 252
A data analyst is tasked with identifying the top 3 performing sales representatives in each region based on their total sales amount. The sales data is stored in a table named 'sales data" with columns 'region', 'sales_rep', and 'sales_amount'. Which Snowflake SQL statement(s) would efficiently achieve this?

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

Answer: B,C,E

Explanation:
Options A, B, and C correctly utilize window functions to rank sales representatives within each region. assigns a unique rank, assigns the same rank to ties and skips the subsequent ranks, and 'DENSE RANK()' assigns the same rank to ties but does not skip ranks. All three can be used with a 'WHERE clause to filter for the top 3. Option D incorrectly uses 'LAG' and doesn't achieve the desired ranking. Option E uses 'NTILE' which divides the rows into three groups; filtering 'tile = 1' might not always return the top three based on sales.


NEW QUESTION # 253
A Snowflake data analyst needs to create a secure view called "masked customer data' based on an existing table named 'customer data'. The requirement is to mask the 'email' column for all users except those with the "DATA ADMIN' role. Also, only users with the 'ANALYST' role should be able to query any data from the view. The masking policy 'email_mask' has already been created. Which of the following sequence of commands correctly implements this requirement?

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

Answer: E

Explanation:
Option C correctly implements the requirements. First, a 'SECURE VIEW' is created to ensure data security. Then, the 'ALTER VIEW' command correctly applies the masking policy to the column of the view. Finally, 'GRANT SELECT ensures that only the 'ANALYST role can query data from the view. Option B attempts to alter the underlying table directly which isn't the intention, and uses an invalid command 'MODIFY COLUMN'. Option A does not create a secure view. Option D uses incorrect syntax 'MODIFY COLUMN' for altering view. Option E try to alter the masked_customer_data, which is not a TABLE instead it is a View.


NEW QUESTION # 254
Your organization receives daily updates to a product catalog in Avro format. The Avro schema evolves frequently, adding and removing fields. You need to ingest these Avro files into Snowflake. Which of the following strategies will BEST handle schema evolution while minimizing data loading complexity?

  • A. Use Snowflake's Schema Detection feature with the COPY command into a table with a VARIANT column. Then, use the 'LATERAL FLATTEN' function to extract the fields and load them into a relational table with the correct data types. Automate the schema creation/alteration of the relational table based on the inferred schema from the VARIANT column.
  • B. Create a new Snowflake table for each Avro schema version. Maintain a view that unions all tables to provide a consistent interface.
  • C. Load the Avro files into a CSV format after flattening the Avro Schema.
  • D. Define a fixed Snowflake table schema with a limited set of commonly used fields. Ignore any additional fields in the Avro files during loading.
  • E. Load the Avro files into a VARIANT column in Snowflake and use SQL to extract the required fields. Periodically update the table schema as the Avro schema evolves.

Answer: A

Explanation:
Option D is the best approach. Loading the Avro data into a VARIANT column first allows you to capture all the data, regardless of schema evolution. Using the 'LATERAL FLATTEN' function allows extraction of fields. Utilizing Snowflake's schema detection further automates the process. Option A requires manual schema updates, which is less scalable. Option B creates a management overhead with multiple tables and views. Option C loses data. Option E requires external flattening which creates additional complexity.


NEW QUESTION # 255
A data pipeline is failing intermittently, with the error logs indicating 'Insufficient compute resources'. You are tasked with collecting data to diagnose the root cause. What combination of Snowflake features and data collection strategies would be MOST effective in identifying if warehouse auto-scaling or query performance is the primary contributor to the issue?

  • A. Create a new, larger warehouse and migrate the pipeline to it without analyzing the current warehouse's performance.
  • B. Collect only error logs generated by the data pipeline to understand underlying problem.
  • C. Increase the warehouse size significantly and observe if the pipeline failures cease.
  • D. Monitor the Snowflake warehouse resource monitor metrics (e.g., QUEUED LOAD BYTES, EXECUTION TIME) using the Snowflake web interface or view, focusing on periods coinciding with pipeline failures.
  • E. Analyze the average query execution time for all queries run by the pipeline using the QUERY_HISTORY view and compare it to the warehouse's average execution time. Additionally, check for queries spilling to local disk (using WAREHOUSE LOAD) during failure periods.

Answer: D,E

Explanation:
Options A and C provide a targeted approach. A allows tracking warehouse resource usage during the pipeline failures, identifying if the warehouse is reaching its limits. C helps identify slow-running queries contributing to resource exhaustion and detects queries using excessive local disk, indicating potential optimization opportunities. Increasing the warehouse size without analysis (B & D) is not cost-effective. Collecting only error logs (E) is not sufficient for a comprehensive diagnostic approach. It is too narrow and may not expose warehouse load issues and bottlenecks.


NEW QUESTION # 256
You are analyzing sales data in Snowflake to identify seasonal trends and patterns. You have a table 'SALES DATA with columns 'SALE DATE (DATE) and 'SALE_AMOUNT (NUMBER). Which of the following SQL queries and visualization techniques would be MOST effective in identifying and visualizing these seasonal trends? Assume the data spans several years.

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

Answer: E

Explanation:
Option C is the most effective because it combines weekly sales aggregation with a box plot analysis of monthly sales across multiple years. The weekly aggregation provides a granular view of sales trends, while the box plot effectively visualizes the distribution of sales for each month, allowing for easy identification of monthly seasonal patterns and outliers. Option A only shows monthly sales volume, not the distribution of sales within each month across years. Option B shows the yearly trend, not seasonal variations. Option D doesn't aggregate the data and hence can't show you the seasonality. Option E only shows the daily variance across weeks.


NEW QUESTION # 257
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