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Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q60-Q65):

NEW QUESTION # 60
You are tasked with developing a multi-class image classification model to categorize product images stored in Snowflake external stage. The categories are 'Electronics', 'Clothing', 'Furniture', 'Books', and 'Food'. You plan to use a pre-trained Convolutional Neural Network (CNN) model and fine-tune it using your dataset. However, you're facing challenges in efficiently loading and preprocessing the image data within the Snowflake environment before feeding it to your model. Which of the following approaches would be MOST efficient for image data loading and preprocessing in Snowflake, minimizing data movement and leveraging Snowflake's scalability, for a large dataset exceeding 1 TB of images?

A. Write a Python User-Defined Function (UDF) that loads each image from the external stage directly into memory, performs preprocessing (resizing, normalization), and returns the processed image data. The UDF is then called in a SQL query to process the image data.
B. Download all the images from the external stage to a local machine, preprocess them using a standard Python library like OpenCV, and then upload the processed data back into Snowflake as a table for model training.
C. Create a Snowflake Stream to continuously ingest new images into a Snowflake table. Use a task to periodically trigger a Python UDF that preprocesses the newly ingested images and stores them in another table for model training.
D. Utilize Snowflake's external function integration with AWS Lambda to preprocess images as they are uploaded to S3, storing the preprocessed data back in S3 and creating an external table pointing to the preprocessed data.
E. Use Snowflake's Snowpark to read images from the external stage into a Snowpark DataFrame. Then, implement image preprocessing using Snowpark DataFrame operations, such as resizing and normalization, within the DataFrame transformations before sending the data to the model.

Answer: D,E

Explanation:
Option B (Snowpark) leverages Snowflake's data processing capabilities within the Snowflake environment, avoiding unnecessary data movement. Snowpark allows you to perform DataFrame operations directly on the data stored in Snowflake, making it an efficient way to preprocess large datasets. Option E (External functions with Lambda) is also very efficient as it preprocesses data outside of Snowflake upon ingestion, minimizing resource utilization inside Snowflake. Options A, C, and D are less efficient. Option A might lead to memory limitations with large images. Option C requires significant data transfer and manual processing. Option D introduces unnecessary complexity with streams and tasks for an initial large dataset.

NEW QUESTION # 61
You are working with a large dataset of transaction data in Snowflake to identify fraudulent transactions. The dataset contains millions of rows and includes features like transaction amount, location, time, and user ID. You want to use Snowpark and SQL to identify potential outliers in the 'transaction amount' feature. Given the potential for skewed data and varying transaction volumes across different locations, which of the following data profiling and feature engineering techniques would be the MOST effective at identifying outlier transaction amounts while considering the data distribution and location-specific variations?

A. Use Snowflake's APPROX_PERCENTILE function with Snowpark to calculate percentiles of the 'transaction amount' feature. Transactions with amounts in the top and bottom 1% are flagged as outliers.
B. Apply a clustering algorithm (e.g., DBSCAN) using Snowpark ML to the transaction data, using transaction amount, location and time as features. Treat data points in small, sparse clusters as outliers. This approach does not need to be performed for each location, just the entire dataset.
C. Use Snowpark to calculate the interquartile range (IQR) of the 'transaction amount' feature for the entire dataset. Identify outliers as transactions with amounts that fall below QI - 1.5 IQR or above Q3 + 1.5 IQR.
D. Calculate the mean and standard deviation of the 'transaction amount' feature for the entire dataset using SQL. Identify outliers as transactions with amounts that fall outside of 3 standard deviations from the mean.
E. Partition the data by location using Snowpark. For each location, calculate the median and median absolute deviation (MAD) of the 'transaction amount' feature. Identify outliers as transactions with amounts that fall outside of the median +/- 3 MAD for that location.

Answer: B,E

Explanation:
Options C and E are the most effective for identifying outliers, considering the skewed nature of transaction data and location-specific variations. The IQR is better than mean and Standard Deviation. The MAD is more robust to outliers compared to standard deviation, which may be inflated by extreme values. Partitioning by location allows for a more nuanced identification of outliers specific to each location. DBSCAN is a great option to include with the partitioning because it considers transaction amount, location, and time as a factor in determine whether the data is an outlier. A and B are less effective because the median and standard deviation are sensitive to extreme values, and the IQR will not consider other dimensions such as location and time. D is only okay because it does not consider the impact of location on determining outliers.

NEW QUESTION # 62
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 automatically tracks the exact SQL queries used to train the model, allowing for full reproducibility of the training process.
D. The Model Registry provides a central repository to register, version, and manage models, enabling better collaboration and governance across data science teams.
E. Custom tags and metadata can be associated with each model version, enabling detailed documentation and traceability of model development and deployment.

Answer: A,D,E

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 # 63
You are building a fraud detection model in Snowflake using Snowpark Python. You want to evaluate the model's performance, particularly focusing on identifying instances of fraud (minority class). Which combination of metrics provides the most comprehensive assessment for this imbalanced classification problem within the Snowflake environment, considering the need to minimize both false positives (legitimate transactions flagged as fraudulent) and false negatives (fraudulent transactions missed)?

A. Accuracy and ROC AUC.
B. Precision and Fl-score.
C. Precision, Recall, and Fl-score.
D. ROC AUC and Recall.
E. Accuracy and Recall.

Answer: C

Explanation:
Option D (Precision, Recall, and Fl-score) is the most comprehensive. In an imbalanced classification problem like fraud detection, accuracy alone is misleading because a model can achieve high accuracy by simply predicting the majority class (non-fraud) for all instances. Precision measures the proportion of correctly identified fraudulent transactions out of all transactions flagged as fraudulent. Recall measures the proportion of correctly identified fraudulent transactions out of all actual fraudulent transactions. F 1-score is the harmonic mean of precision and recall, providing a balanced measure of the model's performance. All three metrics are needed to comprehensively evaluate a fraud detection model on an imbalanced dataset.

NEW QUESTION # 64
You're developing a fraud detection system in Snowflake. You're using Snowflake Cortex to generate embeddings from transaction descriptions, aiming to cluster similar fraudulent transactions. Which of the following approaches are MOST effective for optimizing the performance and cost of generating embeddings for a large dataset of millions of transaction descriptions using Snowflake Cortex, especially considering the potential cost implications of generating embeddings at scale? Select two options.

A. Create a materialized view containing pre-computed embeddings for all transaction descriptions.
B. Generate embeddings using snowflake-cortex-embed-text function, using the OPENAI embedding model
C. Use a Snowflake Task to incrementally generate embeddings only for new transactions that have been added since the last embedding generation run.
D. Implement caching mechanism based on a hash of transaction description if transaction description does not change then no need to recompute the emebeddings again.
E. Generate embeddings on the entire dataset every day to capture all potential fraudulent transactions and ensure the model is always up-to-date.

Answer: C,D

Explanation:
Option B is a better approach compared to option A to generate embeddings because its incrementally generate embeddings for new transactions. Option E is also an important approach where if transaction description remains same for the embeddings will not be re-computed. Materialized view is not suited for API integrations like those using Snowflake Cortex. Option D is technically correct, but doesn't address the optimization and cost concerns. Option A Regenerating embeddings for the entire dataset daily is computationally expensive and can quickly lead to high costs, especially with Snowflake Cortex. The best approach is to use caching and compute only for a new transaction description. So correct answer is B and E.

NEW QUESTION # 65
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Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q60-Q65):

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