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[Hardware] DSA-C03 sure pass torrent & DSA-C03 training questions & DSA-C03 valid p

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【Hardware】 DSA-C03 sure pass torrent & DSA-C03 training questions & DSA-C03 valid p

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Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q282-Q287):NEW QUESTION # 282
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. 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.
  • C. 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.
  • D. 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.
  • E. 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.
Answer: E
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 # 283
You are training a binary classification model in Snowflake to predict customer churn using Snowpark Python. The dataset is highly imbalanced, with only 5% of customers churning. You have tried using accuracy as the optimization metric, but the model performs poorly on the minority class. Which of the following optimization metrics would be most appropriate to prioritize for this scenario, considering the imbalanced nature of the data and the need to correctly identify churned customers, along with a justification for your choice?
  • A. Area Under the Receiver Operating Characteristic Curve (AUC-ROC) - as it measures the ability of the model to distinguish between the two classes, irrespective of the class distribution.
  • B. F 1-Score - as it balances precision and recall, providing a good measure for imbalanced datasets.
  • C. Log Loss (Binary Cross-Entropy) - as it penalizes incorrect predictions proportionally to the confidence of the prediction, suitable for probabilistic outputs.
  • D. Root Mean Squared Error (RMSE) - as it is commonly used for regression problems, not classification.
  • E. Accuracy - as it measures the overall correctness of the model.
Answer: A,B
Explanation:
AUC-ROC is suitable because it evaluates the model's ability to discriminate between classes regardless of class imbalance. F1-Score balances precision and recall, which is crucial for imbalanced datasets to avoid models biased towards the majority class. Log Loss is also a good option but less robust to class imbalance than AUC-ROC. Accuracy is inappropriate due to class imbalance, and RMSE is for regression problems.

NEW QUESTION # 284
You've developed a fraud detection model using Snowflake ML and want to estimate the expected payout (loss or gain) based on the model's predictions. The cost of investigating a potentially fraudulent transaction is $50. If a fraudulent transaction goes undetected, the average loss is $1000. The model's confusion matrix on a validation dataset is: Predicted Fraud Predicted Not Fraud Actual Fraud 150 50 Actual Not Fraud 20 780 Which of the following SQL queries in Snowflake, assuming you have a table 'FRAUD PREDICTIONS' with columns 'TRANSACTION ID', 'ACTUAL FRAUD', and 'PREDICTED FRAUD' (1 for Fraud, O for Not Fraud), provides the most accurate estimate of the expected payout for every 1000 transactions?

  • A. Option A
  • B. Option D
  • C. Option B
  • D. Option E
  • E. Option C
Answer: D
Explanation:
Option E correctly calculates the expected payout by subtracting the cost of false positives (investigating non-fraudulent transactions) from the loss due to false negatives (undetected fraudulent transactions). The confusion matrix data (50 false negatives, 20 false positives) translates to an expected payout of (1000 50) - (50 20) = $49000 loss for every 1000 transactions. The other queries either incorrectly combine the costs and losses, or only calculate one aspect. The other query calculate in correct format or not relevant as per context.

NEW QUESTION # 285
You are deploying a large language model (LLM) to Snowflake using a user-defined function (UDF). The LLM's model file, '11m model.pt', is quite large (5GB). You've staged the file to Which of the following strategies should you employ to ensure successful deployment and efficient inference within Snowflake? Select all that apply.
  • A. Leverage Snowflake's Snowpark Container Services to deploy the LLM as a separate containerized application and expose it via a Snowpark API. Then call that endpoint from snowflake.
  • B. Increase the warehouse size to XLARGE or larger to provide sufficient memory for loading the large model into the UDF environment.
  • C. Use the 'PUT' command with to compress the model file before staging it. Snowflake will automatically decompress it during UDF execution.
  • D. Split the large model file into smaller chunks and stage each chunk separately. Reassemble the model within the UDF code before inference.
  • E. Use the 'IMPORTS' clause in the UDF definition to reference Ensure the UDF code loads the model lazily (i.e., only when it's first needed) to minimize startup time and memory usage.
Answer: A,B,E
Explanation:
Options B, C and D are correct. B: A large model requires sufficient memory, so using an XLARGE or larger warehouse is crucial. C: Snowpark Container Services are designed for such scenarios and is the recommended best practice. D: Specifying the model file as an import and using lazy loading helps manage memory efficiently. Option A can work, but since 'Ilm_model.pt' is already compressed. Compressing again will be not efficient. Splitting the model into chunks (Option E) is overly complicated. Option C gives flexibility of calling out functions from containerized environment, so better scalability.

NEW QUESTION # 286
You've built a machine learning model in scikit-learn and want to deploy it to Snowflake for real-time inference. You have the following options for deploying the model. Select all that apply and are considered a best practice for cost and time optimization:
  • A. Create a Snowflake external function that calls a cloud-based (AWS SageMaker, Azure Machine Learning, GCP Vertex A1) endpoint for inference, passing the input data to the endpoint and receiving the prediction back.
  • B. Migrate your entire Snowflake data warehouse to a different platform which better supports real-time ML inference.
  • C. Implement a custom microservice that reads data from Snowflake, performs inference using the scikit-learn model, and writes the predictions back to Snowflake.
  • D. Package the scikit-learn model using 'joblib' or 'pickle' , store it in a Snowflake stage, and create a Snowflake UDF (User-Defined Function) in Python to load the model from the stage and perform inference.
  • E. Use Snowflake's Snowpark Python API to directly load the model from a stage and execute inference using Snowpark DataFrames, which will implicitly handle the distributed processing of the data.
Answer: D,E
Explanation:
Options A and B are the recommended approaches. Option A leverages Snowflake UDFs for inference, which minimizes data transfer and leverages Snowflake's compute. Option B, using Snowpark, provides a more seamless integration with Snowflake's distributed processing capabilities. Option C introduces external dependencies and latency. Option D requires managing and maintaining a separate microservice and data transfer and Option E is not viable.

NEW QUESTION # 287
......
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