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Snowflake SnowPro Advanced: Data Scientist Certification Exam DSA-C03 Prüfungsfragen mit Lösungen (Q224-Q229):

224. Frage
You're working with a large dataset containing customer purchase history. You want to identify customers whose purchase frequency deviates significantly from the average purchase frequency of all customers. The dataset is in a table named 'purchase history' with columns 'customer id' and 'purchase date'. What combination of Snowflake functionalities will allow you to achieve this task efficiently?
Choose all that apply.

A. Employ the 'QUALIFY clause along with window functions to filter customers based on a condition related to their purchase frequency compared to the average.
B. Calculate the Z-score of each customer's purchase frequency using 'AVG(Y, 'STDDEV()' , and window functions, and then filter based on a Z-score threshold.
C. Calculate the average purchase frequency across all customers using and group by 'customer_id'.
D. Create a UDF that computes the purchase frequency for a single user and apply it to all customers.
E. Use the window function to divide customers into quantiles based on their total purchase count.

Antwort: A,B

Begründung:
Options C and D are the correct approaches. Option C, using the 'QUALIFY clause with window functions, is ideal for filtering rows based on a window function result. You can calculate the purchase frequency using a window function and compare it to the average within the 'QUALIFY' clause, effectively identifying those with significant deviations. Option D offers a robust statistical approach. Calculating the Z-score, which measures how many standard deviations an element is from the mean, allows you to identify customers with purchase frequencies that are statistically significantly different from the average. Filtering based on a Z-score threshold (e.g., IZI > 2) identifies outliers. Although A and B are steps that could be useful, they are not complete solutions on their own. Option E, while technically possible, would be less efficient than using built-in functions and windowing.

225. Frage
You are building a machine learning model to predict loan defaults. You have a dataset in Snowflake with the following features: 'income' (annual income in USD), 'loan_amount' (loan amount in USD), and 'credit_score' (FICO score). You need to normalize these features before training your model. The data has outliers in both 'income' and 'loan_amount', and 'credit_score' has a roughly normal distribution but you still want to standardize it to have a mean of 0 and standard deviation of 1. You want to perform these normalizations using only SQL in Snowflake (no UDFs). Which of the following SQL transformations are most suitable?

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

Antwort: A

Begründung:
Option C is the most suitable. Robust Scaling is appropriate for 'income' and 'loan_amount' due to the presence of outliers. Robust scaling, using IQR is less sensitive to extreme values than Min-Max or Z-score. Z-score standardization is suitable for 'credit_score' as it has a roughly normal distribution, and standardization is desired. Option A is incorrect since Min-Max scaling is highly sensitive to outliers. Option B is incorrect because Z-score is not outlier resilient and it doesn't take into account the data properties given for credit score. Log transformation and arcsinh transform can handle outliers, they're not as resilient as robust scaling. The arcsinh transformation is also useful for features that may have negative values, but we don't have that information here.

226. Frage
You're building a customer segmentation model and need to aggregate data from various tables. You have the following tables in Snowflake: 'customer demographics' (customer id, age, city, income) 'customer transactionS (transaction_id, customer id, transaction_date, amount) 'product_details' (product_id, category) 'transaction_products' (transaction_id, product_id) Your goal is to create a single Snowpark DataFrame containing customer demographics along with the total amount spent by each customer on products within the 'Electronics' category in the last year. However, ensure that only customers with income greater than 50000 are considered and handle cases where customers have no transaction records, assigning a value of 0 to the 'total_electronics_spending' column for those customers. How can we achieve this using snowpark? Choose the correct options

A. Use a series of INNER JOINs to connect the tables and filter data, followed by grouping and aggregation. This approach guarantees accurate results with good performance.
B. Create a Python UDF that performs the joins and aggregations. This offers flexibility and good performance when dealing with complex data transformations.
C. Use a combination of LEFT JOINs and filtering. Start with 'customer_demographics' (filtered for income > 50000) as the base table and LEFT JOIN to subsequent tables. Use the 'coalesce' function to handle customers without transaction data.
D. Create a complex SQL query within Snowpark using 'session.sql()' to perform all the joins, filtering, and aggregation in a single step. This will be the most efficient approach.
E. Create a temporary view to store total electronics expenditure of each customer and left join with customer demographics table.

Antwort: C,D,E

Begründung:
Option B, C and D are correct. Option B is correct because using LEFT JOINs starting with 'customer_demographics' (after filtering for income) ensures all eligible customers are included. 'coalesce' is crucial for handling customers with no transactions, assigning a 0 value. Option C is also correct as using a temporary view is a valid solution to have electronics expenditure for each customer. Option D is correct as pushing down all operations to SQL within Snowpark can be highly performant, as it allows Snowflake to optimize the query execution. However, query readability and maintainability should also be considered. Option A is incorrect because it states that INNER JOINs should be used, but inner joins would exclude customers with no transaction data which is opposite to what is stated in the question. Option E is incorrect as UDFs can introduce performance overhead compared to native Snowpark DataFrame operations or direct SQL queries, especially for large datasets. Avoid UDF when the same output can be achieved without it.

227. Frage
You are deploying a fraud detection model using Snowpark Container Services. The model requires a substantial amount of GPU memory. After deploying your service, you notice that it frequently crashes due to Out-Of-Memory (OOM) errors. You have verified that the container image itself is not the source of the problem. Which of the following strategies are most appropriate to mitigate these OOM errors when using Snowpark Container Services, assuming you want to minimize costs and complexity?

A. Implement a mechanism within your model's inference code to explicitly free up unused memory after each prediction. Use Python's 'gc.collect()' and ensure proper cleanup of large data structures. Configure a smaller 'container.resources.memory' allocation.
B. Increase the 'container.resources.memory' configuration setting in the service definition to a value significantly larger than the model's memory footprint. Monitor memory utilization and adjust as needed.
C. Utilize CPU-based inference instead of GPU-based inference, as CPU inference is generally less memory-intensive. Convert the model to a format optimized for CPU inference (e.g., using ONNX). Reduce the 'container.resources.cpu' count.
D. Ignore OOM errors and rely on the container service to automatically restart the container. The model will eventually process all requests.
E. Implement model parallelism across multiple containers, splitting the model's workload and data across them. Configure each container with a smaller 'container.resources.memory' allocation.

Antwort: A,B

Begründung:
Options A and D are the best strategies. Option A directly addresses the OOM issue by increasing the memory allocation. Monitoring memory usage is crucial to optimize resource utilization. Option D focuses on efficient memory management within the model itself. Explicitly freeing memory and garbage collection can reduce memory footprint. If model need very less gpu memory then decrease container.resources.memory' configuration Option B is a valid strategy, but it introduces significantly more complexity with model parallelism and inter-container communication. Option C might be an option if GPU inference is not strictly necessary and acceptable performance can be achieved with CPU inference, but it is a significant change to the model architecture and potentially impacts performance. Option E is incorrect because ignoring OOM errors leads to unreliable service behavior and data loss.

228. Frage
A marketing team at 'RetailSphere' wants to segment their customer base using unstructured textual data (customer reviews) stored in a Snowflake VARIANT column named 'REVIEW TEXT within the table 'CUSTOMER REVIEWS'. They aim to identify distinct customer segments based on sentiment and topics discussed in their reviews. They want to use a Supervised Learning approach for this task. Which of the following strategies best describes the appropriate approach within Snowflake, considering performance and scalability? Assume you have pre-trained sentiment and topic models deployed as Snowflake external functions.

A. Create a Snowflake external function to call a pre-trained sentiment analysis and topic modeling model hosted on Azure ML. Apply these functions to the REVIEW_TEXT column to generate sentiment scores and topic probabilities. Subsequently, use these features as input to an unsupervised clustering algorithm (e.g., DBSCAN) within Snowflake, relying solely on data density to define segments.
B. Extract the ' REVIEW_TEXT column, manually categorize a small subset of reviews into predefined segments. Train a text classification model (e.g., using scikit-learn) externally, deploy it as a Snowflake external function, and then apply this function to the entire 'REVIEW TEXT column to predict segment assignments. Manually adjust cluster centroids to represent the manually labeled dataset.
C. Create a Snowflake external function to call a pre-trained sentiment analysis and topic modeling model hosted on AWS SageMaker. Apply these functions to the ' REVIEW_TEXT column to generate sentiment scores and topic probabilities. Subsequently, use these features as input to a supervised classification model (e.g., XGBoost) also deployed as a Snowflake external function, training on a manually labeled subset of reviews.
D. Extract the column, apply sentiment analysis and topic modeling using Python within a Snowflake UDF, and then perform K-Means clustering directly on the resulting features within Snowflake. Define the labels after clustering based on the majority class of the topics and sentiments in each cluster.
E. Extract the 'REVIEW TEXT column, apply sentiment analysis and topic modeling using Java within a Snowflake UDF, and then perform hierarchical clustering directly on the resulting features within Snowflake. Manually label the clusters after visual inspection.

Antwort: C

Begründung:
Option C provides the most robust and scalable approach. Using Snowflake external functions allows leveraging pre-trained models without moving the data out of Snowflake. Applying sentiment analysis and topic modeling generates features that can be used by a supervised classification model trained on a labeled subset of reviews. This combines the power of external models with Snowflake's data processing capabilities. Using labeled data allows for better segment definition using Supervised approach.

229. Frage
......

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