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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q56-Q61):

NEW QUESTION # 56
You are designing a dimensional model for a subscription-based service. You have a 'FACT SUBSCRIPTIONS' table with columns like 'subscription_id', 'customer id', 'start date', 'end date', and 'subscription_amount'. The business wants to analyze monthly recurring revenue (MRR) and churn rate. You need to model the temporal aspect of subscriptions to accurately calculate these metrics. Select the TWO best approaches to model the time dimension to facilitate these calculations:

A. Create a 'DIM_SUBSCRIPTIOW table with 'subscription_id' as the primary key and store all subscription details there, avoiding the need for a fact table.
B. Create a 'DIM_DATE table and link 'FACT_SUBSCRIPTIONS' to it using 'start_date' and 'end_date' columns.
C. Store 'start_date' and 'end_date' as VARCHAR columns in the 'FACT SUBSCRIPTIONS table to avoid data type conversions.
D. Create a 'DIM MONTH' table with columns like 'month id', 'month start date', and 'month end date' and link the 'FACT SUBSCRIPTIONS table to it based on the 'start_date' falling within the month.
E. Create a snapshot fact table FACT SUBSCRIPTION SNAPSHOTS that captures the state of each subscription at the end of each month. This table would include 'subscription_id' , 'customer_id', 'snapshot_date', 'is_active' , and 'subscription_amount' .

Answer: D,E

Explanation:
Creating a snapshot fact table (option B) allows for direct calculation of MRR and churn at a specific point in time. Analyzing subscription state at monthly intervals is helpful for these metrics. Creating a 'DIM_MONTH' table (option E) simplifies grouping and aggregation of subscriptions by month. Option A (linking to 'DIM DATE' using both start and end dates) might be useful for other types of analysis, but not directly for MRR/churn calculations. Storing dates as VARCHAR (option C) is bad practice and will hinder performance. A dimension table for subscriptions (option D) won't capture the temporal changes necessary for these metrics.

NEW QUESTION # 57
What actions are involved in data discovery to identify necessary elements from available datasets in Snowflake? (Select all that apply)

A. Evaluating necessary transformations
B. Performing data mining techniques
C. Running SQL queries on tables
D. Identifying missing data fields

Answer: A,C

Explanation:
Data discovery in Snowflake involves querying tables and evaluating required transformations for dataset refinement.

NEW QUESTION # 58
Which statement accurately describes the usage of materialized views in data analysis?

A. Materialized views are only accessible through stored procedures.
B. Materialized views update in real-time, reflecting instantaneous changes in the database.
C. They offer a precomputed, persisted snapshot of data, improving query performance.
D. Materialized views are restricted to storing small subsets of data.

Answer: C

Explanation:
Materialized views provide precomputed snapshots of data, enhancing query performance by reducing computation overhead.

NEW QUESTION # 59
You are responsible for loading data into a Snowflake table named 'CUSTOMER DATA' from a series of compressed JSON files located in a Google Cloud Storage (GCS) bucket. The data volume is significant, and the loading process needs to be as efficient as possible. The JSON files are compressed using GZIP, and they contain a field called 'registration date' that should be loaded as a DATE type in Snowflake. However, some files contain records where the 'registration_date' is missing or has an invalid format. Your goal is to load all valid data while skipping any files that contain invalid dates, and log any files that contain invalid records. You want to choose the most efficient approach. Which of the following options represents the best strategy to achieve this?

A. Create a file format object specifying 'TYPE = JSON' and 'COMPRESSION = GZIP. Use a COPY INTO command with a transformation function 'TO DATE(registration_datey and SON ERROR = CONTINUE. Use a validation table to store rejected records.
B. Create a file format object specifying 'TYPE = JSON' and 'COMPRESSION = GZIP. Use a COPY INTO command with a transformation function 'TRY TO DATE(registration_date)' and 'ON ERROR = SKIP FILE. Implement a separate process to validate the loaded data for NULL 'registration_date' values.
C. create a file format object specifying 'TYPE = JSON' and 'COMPRESSION = GZIP'. Use a COPY INTO command with a transformation function 'TRY Configure the 'CUSTOMER DATA' table with a default value for 'registration_date' and use 'ON ERROR = CONTINUE'.
D. create a file format object specifying 'TYPE = JSON' and 'COMPRESSION = GZIP. Use a COPY INTO command with 'ON_ERROR = SKIP_FILE. Implement a scheduled task to query the COPY HISTORY view to identify any skipped files and manually investigate the errors.
E. Use Snowpipe with a file format object specifying 'TYPE = JSON' and 'COMPRESSION = GZIP'. Configure error notifications for the pipe and handle errors manually.

Answer: C

Explanation:
The correct answer is E. Using gracefully handles invalid dates by returning NULL, which can be managed using a default value on the target table. 'ON ERROR = CONTINUE' ensures the loading process doesn't halt. Combining this with a default value provides for a fast, efficient load. Option A skips the entire file, which is not desired if only some records are invalid. Option B will halt the load process if the target field cannot accept a value. Option C is valid but requires a separate process. Option D makes the manual handling more complex since Snowpipe is designed for near real time instead of batch. E is the best option as all invalid fields will be populated with the default value and load will be unaffected.

NEW QUESTION # 60
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 D
B. Option C
C. Option E
D. Option B
E. Option A

Answer: B

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 # 61
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