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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q136-Q141):

NEW QUESTION # 136
You are designing a data ingestion pipeline for a financial institution. The pipeline loads transaction data from various sources into a Snowflake table named 'TRANSACTIONS. The 'TRANSACTIONS table includes columns such as TRANSACTION , 'ACCOUNT ID', 'TRANSACTION DATE, 'TRANSACTION AMOUNT, and 'TRANSACTION TYPE. The data is loaded in micro- batches using Snowpipe. Due to potential source system errors and network issues, duplicate records with the same 'TRANSACTION ID' are occasionally ingested. You need to ensure data integrity by preventing duplicate 'TRANSACTION_ID' values in the 'TRANSACTIONS' table while minimizing the impact on ingestion performance. Which of the following approaches is the MOST efficient and reliable way to handle this deduplication requirement in Snowflake, considering data integrity and performance?

A. Define as the primary key on the 'TRANSACTIONS' table. Snowflake will automatically reject any duplicate inserts during Snowpipe ingestion.
B. Use a materialized view built on top of the TRANSACTIONS table that selects distinct transaction ids. This ensures that querying through the materialized view returns no duplicates.
C. Create a scheduled task that runs every hour to identify and delete duplicate records based on 'TRANSACTION ID. The task will use a SQL query to find duplicate ' TRANSACTION ID values and remove the older entries.
D. Create a stream on the 'TRANSACTIONS' table and use it to identify newly inserted rows. Then, use a merge statement to insert new, distinct transactions into a separate staging table. Finally, periodically truncate the original 'TRANSACTIONS table and load the deduped data from the staging table.
E. Create a staging table with the same schema as 'TRANSACTIONS'. Use a 'MERGE' statement within the Snowpipe load process to insert new records from the incoming data into the 'TRANSACTIONS' table, only if the 'TRANSACTION ID does not already exist. Define 'TRANSACTION ID' as the primary key in the staging table. Use clustering on 'TRANSACTION_ID on the target 'TRANSACTIONS' table.

Answer: E

Explanation:
Option E provides the most performant and robust solution. Although Snowflake doesn't enforce primary key constraints, defining them on the staging table and leveraging a 'MERGE' statement during the Snowpipe load process allows for efficient deduplication. Clustering on TRANSACTION_I[Y on the target table also helps with performance. A regular task would be less efficient and introduce latency. Snowflake does not automatically reject duplicate inserts based on defined primary keys (option A). Materialized views don't prevent duplicate data from entering the base table. Option C is possible but more complex to implement than a MERGE statement.

NEW QUESTION # 137
A data analyst needs to ingest data from various sources into Snowflake, cleanse it, and load it into target tables. Which of the following actions are MOST crucial for ensuring data quality and consistency during the ingestion and preparation phases?

A. Using Snowflake's data masking policies to protect sensitive data during ingestion.
B. Implementing data validation checks using Snowflake user-defined functions (UDFs) or stored procedures to detect and handle invalid data.
C. Enforcing data type constraints on Snowflake table columns.
D. Implementing data profiling techniques to identify data quality issues and inconsistencies before loading data into target tables and creating detailed data dictionaries.
E. Storing all data in a single large table to avoid data silos.

Answer: B,C,D

Explanation:
Options A, B, and E are most crucial for data quality and consistency. Enforcing data type constraints (A) ensures that only data of the correct type is loaded into the tables. Implementing data validation checks (B) using UDFs or stored procedures allows for detecting and handling invalid data, preventing it from corrupting the data warehouse. Data profiling (E) helps to understand data quality issues before the load and guides data cleansing efforts. Storing all data in a single table (C) is generally not recommended as it can lead to performance issues and make it harder to manage data. While data masking (D) is important for data security, it is not directly related to data quality and consistency during ingestion and preparation. Data dictionaries promote data quality and usability.

NEW QUESTION # 138
When performing forecasting using statistics and built-in functions, what role do these functions play?

A. They enable the creation of custom forecasting models as needed.
B. They solely aid in data visualization without impacting the forecasting process.
C. They restrict forecasting to predefined models without flexibility.
D. These functions only apply to limited data types for forecasting.

Answer: A

Explanation:
Built-in functions in statistics facilitate the creation of custom forecasting models, allowing flexibility in the forecasting process.

NEW QUESTION # 139
How do stored procedures contribute to data analysis efficiency in SQL compared to UDFs?

A. They enable the execution of repetitive tasks, enhancing efficiency.
B. UDFs enhance query performance more effectively than stored procedures.
C. Stored procedures allow limited data accessibility for improved security.
D. Stored procedures hinder customization in data operations.

Answer: A

Explanation:
Stored procedures enhance efficiency by enabling the execution of repetitive tasks.

NEW QUESTION # 140
When maintaining reports and dashboards, why is it essential to build automated and repeatable tasks?

A. Automated tasks reduce manual efforts, ensuring consistency.
B. Automated tasks increase the complexity of dashboard management.
C. Repeatable tasks hinder data updates in dashboards.
D. They ensure inconsistency in reports and dashboards.

Answer: A

Explanation:
Automated tasks reduce manual efforts, ensuring consistency in reports and dashboards.

NEW QUESTION # 141
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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q136-Q141):

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