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100% Pass Quiz Snowflake - Fantastic DSA-C03 - SnowPro Advanced: Data Scientist Certification Exam Exam Review

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Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q151-Q156):

NEW QUESTION # 151
You are developing a regression model in Snowflake using Snowpark to predict house prices based on features like square footage, number of bedrooms, and location. After training the model, you need to evaluate its performance. Which of the following Snowflake SQL queries, used in conjunction with the model's predictions stored in a table named 'PREDICTED PRICES, would be the most efficient way to calculate the Root Mean Squared Error (RMSE) using Snowflake's built-in functions, given that the actual prices are stored in the 'ACTUAL PRICES' table?

A. Option C
B. Option E
C. Option B
D. Option D
E. Option A

Answer: D

Explanation:
Option D is the most efficient and correct way to calculate RMSE. RMSE is the square root of the average of the squared differences between predicted and actual values. - p.predicted_price), 2)' calculates the squared difference. calculates the average of these squared differences. calculates the square root of the average, resulting in the RMSE. Option A is less efficient because it requires creating a temporary table. Option B and E are incorrect since they uses 'MEAN' which is unavailable in Snowflake and Exp/ln will return geometic mean instead of RMSE. Option C calculates the standard deviation of the differences, not the RMSE.

NEW QUESTION # 152
You are training a binary classification model in Snowflake using Snowpark to predict customer churn. The dataset contains a mix of numerical and categorical features, and you've identified that the 'COUNTRY' feature has high cardinality. You observe that your model performs poorly for less frequent countries. To address this, you decide to up-sample the minority classes within the 'COUNTRY' feature before training. Which combination of techniques would be MOST appropriate and computationally efficient for up-sampling in this scenario within Snowflake, considering you are working with a large dataset and want to minimize data shuffling across the network?

A. Use a stored procedure written in Python to iterate through each unique country, identify minority countries, and then use Snowpark to up-sample those countries using 'DataFrame.sample()' with replacement. This offers the most flexibility but introduces significant overhead due to context switching.
B. Use the 'SAMPLE clause in Snowflake SQL with 'REPLACE' for each minority country, creating separate temporary tables and then combining them with UNION ALL'. This is efficient for small datasets but scales poorly with high cardinality.
C. Leverage Snowpark's 'DataFrame.collect()' to bring the entire dataset to the client machine, then use Python's scikit-learn library for up-sampling. This is suitable only for small datasets as it incurs significant network overhead.
D. Utilize Snowflake UDFs (User-Defined Functions) written in Java to perform stratified sampling on the 'COUNTRY' feature, ensuring each minority class is adequately represented in the up-sampled dataset. UDFs allow for complex logic but can be challenging to debug within Snowflake.
E. Use Snowpark's 'DataFrame.groupBy()" and 'DataFrame.count()' to identify minority countries. Then, for each minority country, use DataFrame.unionByName()' to combine the original data with multiple copies of the minority country's data, created using 'DataFrame.sample()' with replacement. This minimizes data movement within Snowflake.

Answer: E

Explanation:
Option B is the most suitable. Using Snowpark's 'DataFrame.groupBy()' and 'DataFrame.count()' allows efficient identification of minority classes directly within Snowflake. Then, employing 'DataFrame.unionByName(V and ' DataFrame.sample(Y with replacement minimizes data movement within Snowflake and performs the up-sampling efficiently. Options A and E are inefficient for large datasets. Option C introduces overhead with stored procedures, and Option D presents debugging challenges with UDFs. Crucially, option B keeps the transformations within the Snowflake engine, reducing network traffic.

NEW QUESTION # 153
You're tasked with building an image classification model on Snowflake to identify defective components on a manufacturing assembly line using images captured by high-resolution cameras. The images are stored in a Snowflake table named 'ASSEMBLY LINE IMAGES', with columns including 'image_id' (INT), 'image_data' (VARIANT containing binary image data), and 'timestamp' (TIMESTAMP NTZ). You have a pre-trained image classification model (TensorFlow/PyTorch) saved in Snowflake's internal stage. To improve inference speed and reduce data transfer overhead, which approach provides the MOST efficient way to classify these images using Snowpark Python and UDFs?

A. Create a Python UDF that loads the entire table into memory, preprocesses the images, loads the pre-trained model, and performs classification for all images in a single execution.
B. Create a vectorized Python UDF that takes a batch of 'image_id' values as input, retrieves the corresponding 'image_data' from the 'ASSEMBLY LINE IMAGES table using a JOIN, preprocesses the images in a vectorized manner, loads the pre-trained model once at the beginning, performs classification on the batch, and returns the results.
C. Create a Java UDF that loads the pre-trained model and preprocesses the images. Call this Java UDF from a Python UDF to perform the image classification. Since Java is faster than Python, this will optimize performance.
D. Use Snowflake's external function feature to offload the image classification task to a serverless function hosted on AWS Lambda, passing the and 'image_icf to the function for processing.
E. Create a Python UDF that takes a single 'image_id' as input, retrieves the corresponding 'image_data' from the table, preprocesses the image, loads the pre-trained model, performs classification, and returns the result. This UDF will be called for each image individually.

Answer: B

Explanation:
Option C offers the most efficient solution. Vectorized UDFs allow processing batches of data at once, significantly reducing overhead compared to processing each image individually (Option B). Loading the model once per batch avoids redundant model loading. Option A is highly inefficient as it attempts to load the entire table into memory. While Java can be faster in certain scenarios, the complexity of calling a Java UDF from a Python UDF (Option D) will likely introduce more overhead than benefits. External functions (Option E) introduce network latency and are generally less efficient than in-database processing, unless there's a specific need for external resources or specialized hardware that Snowflake doesn't offer.

NEW QUESTION # 154
You are tasked with building a machine learning model in Python using data stored in Snowflake. You need to efficiently load a large table (100GB+) into a Pandas DataFrame for model training, minimizing memory footprint and network transfer time. You are using the Snowflake Connector for Python. Which of the following approaches would be MOST efficient for loading the data, considering potential memory limitations on your client machine and the need for data transformations during the load process?

A. Use the 'COPY INTO' command to unload the table to an Amazon S3 bucket and then use bot03 in your python script to fetch data from s3 and load into pandas dataframe.
B. Load the entire table into a Pandas DataFrame using with a simple 'SELECT FROM my_table' query and then perform data transformations in Pandas.
C. Utilize the 'execute_stream' method of the Snowflake cursor to fetch data in chunks, apply transformations in each chunk, and append to a larger DataFrame or process iteratively without creating a large in-memory DataFrame.
D. Create a Snowflake view with the necessary transformations, and then load the view into a Pandas DataFrame using 'pd.read_sql()'.
E. Use 'snowsql' to unload the table to a local CSV file, then load the CSV file into a Pandas DataFrame.

Answer: C

Explanation:
Option C is the most efficient. 'execute_stream' allows you to fetch data in chunks, preventing out-of-memory errors with large tables. You can perform transformations on each chunk, reducing the memory footprint. Loading the entire table at once (A) is inefficient for large datasets. Using ssnowsqr (B) or 'COPY INTO' (E) adds an extra step of unloading and reloading, increasing the time taken. Creating a Snowflake view (D) is a good approach for pre-processing but might not fully address memory issues during the final load into Pandas, especially if the view still contains a large amount of data.

NEW QUESTION # 155
You are managing a machine learning model lifecycle in Snowflake using the Model Registry. Which of the following statements are true regarding model lineage and governance when utilizing the Model Registry for model versioning and deployment?

A. Integration with Snowflake's RBAC (Role-Based Access Control) allows for granular control over who can register, update, and deploy model versions.
B. Model Registry automatically retrains models based on scheduled data updates, ensuring models are always up-to-date without manual intervention.
C. The Model Registry provides a central repository to register, version, and manage models, enabling better collaboration and governance across data science teams.
D. Custom tags and metadata can be associated with each model version, enabling detailed documentation and traceability of model development and deployment.
E. The Model Registry automatically tracks the exact SQL queries used to train the model, allowing for full reproducibility of the training process.

Answer: A,C,D

Explanation:
Options B, C, and D are correct. The Model Registry offers a centralized repository for model management (B), supports custom tags for documentation and traceability (C), and integrates with Snowflake's RBAC for access control (D). Option A is incorrect because the Model Registry does not automatically track SQL queries used for training. While lineage is a part of model governance, Model Registry's lineage capabilities are not focused on capturing training queries but rather on tracking model versions, metrics, and associated metadata. Option E is incorrect; automated retraining is not a feature of the Model Registry itself but can be orchestrated using Snowflake Tasks or other scheduling tools in conjunction with the Model Registry.

NEW QUESTION # 156
......

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