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Snowflake Certified SnowPro Associate - Platform Certification Sample Questions (Q161-Q166):

NEW QUESTION # 161
A data warehouse contains a table 'SALES TRANSACTIONS that is frequently updated. You need to ensure that the data is protected against accidental deletions and updates, and you need to be able to restore the data to a specific point in time. Which of the following Snowflake features and configurations should you implement to achieve this goal effectively?

Answer: D

Explanation:
Setting the parameter enables Time Travel, which allows restoring data to a specific point in the past within the retention period. Fail-safe provides an additional layer of protection beyond Time Travel, but it's not directly accessible for user- initiated recovery. Replication (Option A) is for disaster recovery but doesn't help with user errors. Backups (Option B) are useful but add complexity compared to Time Travel. Option D relies solely on Fail-safe, which is not user- accessible. Note, it is best to only provide time travel on required databases to save money on storage costs, account level might be unnecessary. Therefore, C is still better option than setting at table level also.


NEW QUESTION # 162
How can you query nested fields in semi-structured data in Snowflake? (Choose any 2 options)

Answer: A,B

Explanation:
Snowflake provides intuitive mechanisms to navigate semi-structured data stored in VARIANT columns, especially JSON, XML, Parquet, and Avro. Two primary access methods aredot notationandbracket notation.
Dot notation, such as column.nested.key, allows straightforward access to nested elements using a hierarchical reference. Bracket notation, such as column['key name'], allows access to elements with special characters, spaces, or numeric array indices. While Snowflake also offers powerful functions such as FLATTEN(), OBJECT_CONSTRUCT(), and ARRAY_LENGTH(), these operate on semi-structured types but do not serve as direct field-access mechanisms. Cloud Functions play no role in querying semi-structured fields inside Snowflake. Dot and bracket notation work together to provide full expressiveness, enabling users to traverse complex hierarchical structures directly in SQL, making Snowflake an ideal platform for semi- structured data analytics.


NEW QUESTION # 163
A Snowflake table 'product catalog' exists with columns 'product id', 'product_name', and 'price'.
You have a pipe-delimited file in an external stage named 's3 stage' containing product data, but some rows have missing values for the 'price' column. You want to load the data, replacing missing prices with a default value of 0. Which approach is the MOST appropriate to handle missing values during the 'COPY INTO' operation?

Answer: C

Explanation:
Using a transformation within the 'COPY INTO' statement (option B) allows you to directly handle missing values during the data loading process, providing the most efficient solution. You can utilize a 'CASE' statement or similar logic within the column mapping of the 'COPY INTO' statement to replace nulls with the default value of 0. Option A is not valid. Option C requires additional steps. Option D skips over valuable data, and Option E is false because of the transformation support.


NEW QUESTION # 164
You are using Snowpipe to load data into a table. You need to monitor the data loading process and identify any files that failed to load. Which of the following actions will provide you with information about the files that were processed by Snowpipe and their loading status, including error messages?

Answer: C

Explanation:
The 'COPY HISTORY view in the 'SNOWFLAKES database provides detailed information about the COPY INTO statements executed by Snowpipe, including the files loaded, the number of rows loaded, and any error messages encountered during the loading process. This allows you to identify files that failed to load and understand the reasons for the failures. Option A is incorrect,
'VALIDATION_MODE' isn't a column. Option B provides general status, not file-specific details.
Option D 'ON_ERROR can redirect bad records, but doesn't provide a history like COPY_HISTORY. option E relies on external logging, while `COPY_HISTORY is native to Snowflake.


NEW QUESTION # 165
Which of the following are valid Snowflake Virtual Warehouse sizes? (Choose any 3 options)

Answer: A,B,C

Explanation:
Snowflake warehouse sizes followT-shirt sizing, each representing a doubling of compute resources relative to the previous size. Valid Snowflake warehouse sizes includeX-Small,Small,Medium,Large, and up to6X- Large.
X-Smallis the smallest supported warehouse size and is commonly used for light data processing, development tasks, and low-volume operations.
Smalldoubles the compute capacity of X-Small, providing better performance for moderate workloads.
Mediumcontinues scaling upward, offering increased CPU, memory, and parallelism for heavier analytics or transformation workloads.
Tinyis not a Snowflake warehouse size. Snowflake never exposes a warehouse smaller than X-Small, ensuring consistent minimum compute capabilities.
These standardized size tiers allow predictable resource scaling and cost forecasting.


NEW QUESTION # 166
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