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Snowflake SnowPro Advanced: Data Analyst Certification Sample Questions:

1. A data analyst is working with JSON data representing product reviews. The JSON structure is complex, containing nested arrays and objects. The analyst needs to extract all the reviewer names (reviewer _ name) who gave a rating greater than 4, along with the product ID (product_id) and the overall average rating of that specific product. The table 'RAW REVIEWS contains a single VARIANT column named holding the JSON data'. Choose the most efficient and correct Snowflake SQL query to achieve this. Assume the JSON structure is consistent across all rows.

A) Option E
B) Option D
C) Option A
D) Option C
E) Option B

2. You are tasked with designing a Snowflake data model for a system that tracks changes to product information. You need to store both the current and historical states of the 'PRODUCTS' table. Which of the following strategies, regarding primary keys and table structures, is the MOST appropriate for efficiently querying the current product state and historical changes, considering best practices for Snowflake and minimizing storage costs? Select all that apply.

A) Create a single table, 'PRODUCTS', with columns 'PRODUCT_ID (primary key), 'PRODUCT_NAME', 'PRODUCT_DESCRIPTION', 'EFFECTIVE_DATE', and 'END_DATE. Whenever a product attribute changes, insert a new row with the updated attribute values and the new 'EFFECTIVE_DATE , setting the 'END_DATE' of the previous record to the ' EFFECTIVE_DATE of the new record.
B) Create a single table, 'PRODUCTS', with 'PRODUCT_ID as the primary key and a 'JSON' column to store the historical product data. Use a stored procedure to update the ' JSON' column whenever a product attribute changes.
C) Create a single table, 'PRODUCTS', with columns 'PRODUCT_ID (primary key), 'PRODUCT NAME, 'PRODUCT DESCRIPTION', 'EFFECTIVE DATE and 'END_DATE. Implement clustering on 'PRODUCT_ID' and 'EFFECTIVE_DATE'.
D) Create two tables: with 'PRODUCT_ID as the primary key, storing the current product information, and 'PRODUCTS_HISTORY' with 'PRODUCT ID', 'EFFECTIVE DATE, and 'END DATES columns, where the combination of 'PRODUCT and 'EFFECTIVE DATE' serves as a composite key. Use a scheduled task to copy data from 'PRODUCTS_CURRENT to 'PRODUCTS_HISTORY whenever a product is updated.
E) Create a single table, 'PRODUCTS', with columns 'PRODUCT ID, 'PRODUCT NAME', 'PRODUCT DESCRIPTION', 'EFFECTIVE DATE, and 'END_DAT Add a sequence to act as a surrogate key and define this sequence as the primary key.

3. 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 'TRY Configure the 'CUSTOMER DATA' table with a default value for 'registration_date' and use 'ON ERROR = CONTINUE'.
B) Use Snowpipe with a file format object specifying 'TYPE = JSON' and 'COMPRESSION = GZIP'. Configure error notifications for the pipe and handle errors manually.
C) 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.
D) 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.
E) 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.

4. You are building a Snowsight dashboard to monitor the performance of various SQL queries. You have a table 'QUERY HISTORY with columns 'QUERY ID', 'START TIME, 'END TIME, 'USER NAME, 'DATABASE NAME, and 'EXECUTION_TIME (in seconds). You want to create a bar chart that shows the average execution time for each user, but only for queries executed against a specific database (e.g., 'SALES DB') within the last week. Furthermore, you need to allow users to filter the data by username via a Snowsight dashboard variable. What is the most efficient SQL query and Snowsight configuration to achieve this?

A) Option E
B) Option D
C) Option A
D) Option C
E) Option B

5. You have a Snowflake table named 'sensor_data' with a column 'reading' containing JSON data'. The JSON structure varies, but you want to extract a specific nested value, 'temperature', using a UDE The path to 'temperature' might be different depending on the 'sensor_type'. Some sensors have the temperature at '$.metrics.temperature' , others at '$.reading.temp_c'. The sensor type is stored in the 'sensor_type' column. You want to create a UDF named which takes the JSON 'reading' and the 'sensor_type' as input and extracts the temperature, returning NULL if the path does not exist in the JSON. How can you implement this using a JavaScript UDF and Snowflake's JSON parsing functions for optimal performance?

A) Option E
B) Option D
C) Option A
D) Option C
E) Option B

Solutions: