Snowflake Certified SnowPro Specialty - Snowpark (SPS-C01) Free Practice Test

Question 1

You have a Snowpark application that processes sensitive data'. To enhance security, you want to use key pair authentication and ensure that the private key is never exposed in plain text within the application or logs. Which of the following strategies offers the most robust protection against accidental key exposure, even in the event of a security breach of the application server itself?

A. Store the private key encrypted with a symmetric key. Store the symmetric key in a separate environment variable.

B. Use a dedicated Hardware Security Module (HSM) or a cloud-based key management service (e.g., AWS KMS, Azure Key Vault) to store and manage the private key. Configure the Snowpark application to retrieve the key only when needed and never store it locally.

C. Store the private key in an environment variable on the application server, ensuring that only the application user has read access.

D. Store the private key in an encrypted file with restricted access, decrypting it only during session creation and overwriting the memory location immediately afterwards.

E. Store the private key encrypted using AES encryption with a key derived from a passphrase. Prompt the user for the passphrase each time the application starts.

Correct Answer: B

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Question 2

You have a Snowpark DataFrame containing sales data with columns 'sale_date', and 'sale_amount'. You need to calculate the cumulative sales amount for each product over time, ordered by 'sale_date'. Which of the following Snowpark code snippets correctly implements this using window functions?

A.

B.

C.

D.

E.

Correct Answer: A

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Question 3

You are developing a Snowpark Python application that connects to Snowflake using key pair authentication. You have the private key stored securely in an environment variable named 'SNOWFLAKE PRIVATE KEY. Which of the following code snippets correctly establishes a Snowpark session using this method, assuming all other necessary connection parameters (account, user, database, schema, warehouse) are also set as environment variables?

A.

B.

C.

D.

E.

Correct Answer: B

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Question 4

You are tasked with optimizing a Snowpark application that performs complex data transformations on a large dataset (1 TB) stored in Snowflake. The application currently uses Snowpark DataFrames and is experiencing slow performance. You suspect the issue might be related to data transfer overhead between the Snowflake engine and the Python environment. Which of the following strategies would be MOST effective in minimizing this overhead and improving performance?

A. Reduce the data volume by applying aggressive filtering and aggregation using Snowpark DataFrame operations before any other transformations, minimizing the amount of data transferred.

B. Convert the Snowpark DataFrame to a Pandas DataFrame and perform the transformations locally within the Python environment.

C. Utilize User-Defined Functions (UDFs) written in Python to encapsulate the transformations and execute them within the Snowflake engine.

D. Implement vectorization techniques within the Snowpark DataFrame operations using built-in functions and optimized expressions where applicable.

E. Increase the virtual warehouse size to the largest available option (e.g., X-Large) to improve processing power within Snowflake, regardless of data transfer costs.

Correct Answer: A,C,D

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Question 5

You are using Snowflake Notebooks to develop a Snowpark application and want to leverage a custom Python library that is not available in the default environment. What steps are necessary to make this library available within your Snowflake Notebook?

A. Upload the Python library's ' .py' file directly to the Snowflake stage and import it using 'import sys; sys.path.append("); import

B. Install the library using pip in the Snowflake Notebook's terminal and then restart the Snowflake Notebook.

C. Create a conda environment specification file ('environment.yml') that includes the custom library, upload it to a Snowflake stage, and then create a new environment based on that file when creating or modifying the Snowflake Notebook.

D. Install the library directly within the Snowflake Notebook using '!pip install

E. Create a deployment file using setup.py, upload deployment file to stage, and create function

Correct Answer: C

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Question 6

You are tasked with automating the creation of Snowpark sessions using key pair authentication for multiple users. You have a function that retrieves connection parameters (account, user, private key, etc.) for each user from a secure configuration file. The private keys are stored in PEM format. However, some users' private keys are password-protected. Which of the following approaches ensures the secure and correct establishment of Snowpark sessions for all users, including those with password-protected private keys? Assume get_user config(username)' retrieves the user's configuration, including the private key and password (if any).

A. Store the password for each user's private key in a separate, encrypted file and retrieve it during session creation.

B.

C. Require all users to remove the password protection from their private keys to simplify the session creation process.

D.

E. Attempt to establish a session without a password. If it fails, prompt the user for the password and retry the session creation using the provided password. Store the password temporarily in memory.

Correct Answer: B

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Question 7

You have a Snowflake table named 'raw events' with a VARIANT column named 'event data'. The 'event data' column contains JSON objects with a field 'timestamp' that is sometimes represented as a string and sometimes as a number (Unix epoch). You need to create a Snowpark DataFrame that extracts the 'timestamp' as a timestamp object, handling both string and numeric representations. Which of the following code snippets correctly accomplishes this, avoiding errors when encountering incompatible types?

A.

B.

C.

D.

E.

Correct Answer: C

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Question 8

You have a Snowpark DataFrame containing customer order data with columns , and 'order_amount' . You need to identify customers who placed orders exceeding $1000 on more than 3 separate days. Which Snowpark code snippet correctly achieves this? Assume SparkSession 'spark' and DataFrame are already defined.

A.

B.

C.

D.

E.

Correct Answer: B

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Question 9

You are setting up a development environment for Snowpark using Anaconda and encounter the following error: 'ModuleNotFoundError: No module named 'snowflake.snowpark". You have already installed the package using pip. What is the MOST likely cause of this error and how do you resolve it?

A. The Snowflake account identifier is not properly configured in your environment variables. Verify that 'SNOWFLAKE ACCOUNT is correctly set.

B. The Anaconda environment is not activated. Activate the environment using 'conda activate

C. The 'snowflake-snowpark-python' package was installed in a different Anaconda environment than the one you are currently using. Ensure you are in the correct environment when running your Snowpark code.

D. The Snowflake driver is not installed. Install the Snowflake driver using 'pip install snowflake-connector-python'

E. The 'snowflake-snowpark-python' package is not compatible with the version of Python installed in your Anaconda environment. Upgrade Python to the latest version.

Correct Answer: B,C

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Question 10

You are tasked with optimizing the performance of a Snowpark Python application that performs complex data transformations on a large dataset of IoT sensor readings. The application uses a Snowpark-optimized warehouse. You notice that the application is consistently slow, with CPU utilization on the warehouse fluctuating significantly. Which of the following actions would be MOST effective in addressing this performance issue? Assume the dataset is partitioned on the 'sensor_id' column within Snowflake.

A. Ensure the Snowpark DataFrame transformations are pushed down to Snowflake as much as possible by avoiding actions like 'collect()' until absolutely necessary and leverage stored procedures.

B. Increase the warehouse size to a larger instance (e.g., from X-Small to Small). This will provide more CPU and memory resources.

C. Enable auto-scaling on the warehouse with a minimum of 2 and maximum of 5 clusters. This will allow the warehouse to dynamically adjust capacity based on workload.

D. Repartition the Snowpark DataFrame using partition_expression='sensor_id')' before applying transformations. Then, explicitly colocate similar operations.

E. Rewrite the Snowpark DataFrame transformations using only built-in Snowpark functions and avoid using User-Defined Functions (UDFs) written in Python.

Correct Answer: A,D,E

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Snowflake Certified SnowPro Specialty - Snowpark (SPS-C01) Free Practice Test