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最新的 SnowPro Advanced DSA-C03 免費考試真題:

1. You are training a regression model to predict house prices using a Snowflake dataset. The dataset contains various features, including 'number of_bedrooms', , and You want to use time-based partitioning for your training, validation, and holdout sets. However, you also need to ensure that the dataset is properly shuffled within each time partition to mitigate potential bias introduced by the order of data entry. Which of the following strategies is MOST EFFECTIVE and EFFICIENT for partitioning your data into train, validation, and holdout sets in Snowflake, while also ensuring random shuffling within each partition, and addressing potential data leakage issues?

A) Use Snowflake's SAMPLE clause with a 'REPEATABLE seed for each split (train, validation, holdout), filtering by 'sale_date'. Add an 'ORDER BY RANDOM()' clause within each 'SAMPLE query to shuffle the data within each split. This approach does not guarantee non-overlapping sets and can introduce sampling bias.
B) Create a new column 'split_group' using a CASE statement based on 'sale_date' to assign each row to 'train', 'validation', or 'holdout'. Calculate a random number within each 'split_group' by using OVER (PARTITION BY split_group ORDER BY RANDOM())'. Then create temporary tables for each split using 'CREATE TABLE AS SELECT FROM WHERE split_group = QUALIFY ROW NUMBER() OVER (ORDER BY RANDOM()) (SELECT COUNT( ) FROM transactions WHERE split_group -- ...) (respective split percentage);'
C) Create separate views for train, validation, and holdout sets, filtering by 'sale_date' . Shuffle the entire dataset using 'ORDER BY RANDOM()' before creating the views to ensure randomness across all sets. This does not address shuffling within parition.
D) Create a new column 'split_group' using a CASE statement based on 'sale_date' to assign each row to 'train', 'validation', or 'holdout'. Then, create temporary tables for each split using 'CREATE TABLE AS SELECT FROM WHERE split_group = ORDER BY RANDOM()'. This can be very slow because of global RANDOM sort and leakage issues with using full dataset for randomness.
E) Create a user-defined function (UDF) in Python that takes a 'sale_date' as input and returns either 'train', 'validation', or 'holdout' based on pre-defined date ranges. Apply this UDF to each row, creating a 'split_group' column. Then, create temporary tables for each split using 'CREATE TABLE AS SELECT ... FROM . WHERE split_group = ... ORDER BY RANDOM()'. UDF overhead and global RANDOM sort make it very slow.

2. A marketing team is using Snowflake to store customer data including demographics, purchase history, and website activity. They want to perform customer segmentation using hierarchical clustering. Considering performance and scalability with very large datasets, which of the following strategies is the MOST suitable approach?

A) Randomly sample a small subset of the customer data and perform hierarchical clustering on this subset using an external tool like R or Python with scikit-learn. Assume that results generalize well to the entire dataset. Avoid using Snowflake for this purpose.
B) Perform mini-batch K-means clustering using Snowflake's compute resources through a Snowpark DataFrame. Take a large sample of each mini-batch and perform hierarchical clustering on each mini-batch and then create clusters of clusters.
C) Employ BIRCH clustering with Snowflake Python UDF. Configure Snowflake resources accordingly. Optimize the clustering process. And tune parameters.
D) Utilize a SQL-based affinity propagation method directly within Snowflake. This removes the need for feature scaling and specialized hardware.
E) Directly apply an agglomerative hierarchical clustering algorithm with complete linkage to the entire dataset within Snowflake, using SQL. This is computationally feasible due to SQL's efficiency.

3. You are developing a real-time fraud detection system using Snowflake and an external function. The system involves scoring incoming transactions against a pre-trained TensorFlow model hosted on Google Cloud A1 Platform Prediction. The transaction data resides in a Snowflake stream. The goal is to minimize latency and cost. Which of the following strategies are most effective to optimize the interaction between Snowflake and the Google Cloud A1 Platform Prediction service via an external function, considering both performance and cost?

A) Implement asynchronous invocation of the external function from Snowflake using Snowflake's task functionality. This allows Snowflake to continue processing transactions without waiting for the response from the Google Cloud A1 Platform Prediction service, but requires careful monitoring and handling of asynchronous results.
B) Implement a caching mechanism within the external function (e.g., using Redis on Google Cloud) to store frequently accessed model predictions, thereby reducing the number of calls to the Google Cloud A1 Platform Prediction service. This requires managing cache invalidation.
C) Use a Snowflake pipe to automatically ingest the data from the stream, and then trigger a scheduled task that periodically invokes a stored procedure to train the model externally.
D) Invoke the external function for each individual transaction in the Snowflake stream, sending the transaction data as a single request to the Google Cloud A1 Platform Prediction service.
E) Batch multiple transactions from the Snowflake stream into a single request to the external function. The external function then sends the batched transactions to the Google Cloud A1 Platform Prediction service in a single request. This increases throughput but might introduce latency.

4. You are building a customer support chatbot using Snowflake Cortex and a large language model (LLM). You want to use prompt engineering to improve the chatbot's ability to answer complex questions about product features. You have a table PRODUCT DETAILS with columns 'feature_name', Which of the following prompts, when used with the COMPLETE function in Snowflake Cortex, is MOST likely to yield the best results for answering user questions about specific product features, assuming you are aiming for concise and accurate responses focused solely on providing the requested feature description and avoiding extraneous chatbot-like conversation?

A) Option E
B) Option B
C) Option C
D) Option A
E) Option D

5. You have deployed a vectorized Python UDF in Snowflake to perform sentiment analysis on customer reviews. The UDF uses a pre-trained transformer model loaded from a Stage. The model consumes a significant amount of memory (e.g., 5GB). Users are reporting intermittent 'Out of Memory' errors when calling the UDF, especially during peak usage. Which of the following strategies, used IN COMBINATION, would MOST effectively mitigate these errors and optimize resource utilization?

A) Increase the value of 'MAX BATCH_ROWS' for the UDF to process larger batches of data at once.
B) Implement lazy loading of the model within the UDF, ensuring it's only loaded once per warehouse node and reused across multiple invocations within that node.
C) Increase the warehouse size to provide more memory per node.
D) Partition the input data into smaller chunks using SQL queries and call the UDF on each partition separately.
E) Reduce the value of 'MAX for the UDF to process smaller batches of data.

問題與答案：