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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q31-Q36):

NEW QUESTION # 31
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. Implementing data validation checks using Snowflake user-defined functions (UDFs) or stored procedures to detect and handle invalid data.
B. Storing all data in a single large table to avoid data silos.
C. Implementing data profiling techniques to identify data quality issues and inconsistencies before loading data into target tables and creating detailed data dictionaries.
D. Enforcing data type constraints on Snowflake table columns.
E. Using Snowflake's data masking policies to protect sensitive data during ingestion.

Answer: A,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 # 32
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. 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.
B. 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.
C. 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.
D. 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.
E. Define as the primary key on the 'TRANSACTIONS' table. Snowflake will automatically reject any duplicate inserts during Snowpipe ingestion.

Answer: D

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 # 33
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. 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.
B. 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.
C. 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.
D. 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
E. 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.

Answer: D,E

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 # 34
You are analyzing customer churn for a subscription-based service. You have a table 'SUBSCRIPTIONS' with columns: 'CUSTOMER_ID, 'START_DATE', 'END_DATE', 'SUBSCRIPTION TYPE, and 'REVENUE'. You want to classify customers who are likely to churn based on their past subscription behavior. Which Snowflake SQL code snippet is MOST efficient for calculating the number of months each customer was subscribed and identifying those who subscribed for less than 3 months as potential churn candidates?

Answer: B

Explanation:
Option D is the most efficient. It calculates the 'MONTHS SUBSCRIBED directly in the SELECT statement and filters the results in the 'WHERE clause. Option A will produce an error because you cannot refer to an alias (MONTHS_SUBSCRIBED) in the same 'WHERE clause where it's defined. Option B creates a temporary table, which is unnecessary overhead for this simple calculation. Option C uses HAVING& which is used for filtering aggregated results, not individual rows before aggregation. Snowflake does not natively support MONTHS_BETWEEN, thus option E would result in an error.

NEW QUESTION # 35
You have a Snowsight dashboard that visualizes daily sales trends. Business users complain that the dashboard takes too long to load, especially when filtering by specific product categories. The underlying data resides in a large table partitioned by 'sale date'. Which of the following actions would BEST improve the dashboard's performance, assuming the filters are appropriately configured in the dashboard and the virtual warehouse size is already appropriately sized?

A. Create a materialized view that pre-aggregates the data used by the dashboard, including the dimensions used in the filters.
B. Increase the virtual warehouse size used by Snowsight.
C. Convert the dashboard into a Streamlit application for improved rendering performance.
D. Implement result caching by setting = TRUE at the session level.
E. Use query acceleration on the base table to improve the speed of underlying queries when the filter are being applied by users.

Answer: A

Explanation:
Creating a materialized view pre-aggregates the data, significantly reducing query execution time. The materialized view stores the result of a query, and Snowflake automatically refreshes it when the underlying data changes. Since the product categories are used as filters, pre- aggregating along these dimensions directly addresses the slow loading times. Increasing warehouse size (B) only helps if the compute resources are a bottleneck, which might not be the primary issue. Converting to Streamlit (C) changes the presentation layer but doesn't inherently improve data retrieval. Query Acceleration (D) can help, but only if it properly sized and configured. Session level caching (E) might only benefit the same user, but if multiple users are accessing the same dashboard, the best way would be through pre-aggregated results in a materialized view.

NEW QUESTION # 36
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