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Snowflake DSA-C03 Exam Syllabus Topics:

Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. You are using Snowflake Cortex to analyze customer reviews. You have created a vector embedding for each review using a UDF that calls a remote LLM inference endpoint. Now you need to perform a similarity search to identify reviews that are similar to a given query review. Which of the following SQL queries leveraging vector functions in Snowflake is the MOST efficient and appropriate way to achieve this, assuming the 'REVIEW EMBEDDINGS' table has columns 'review_id' and 'embedding' (a VECTOR column) and query_embedding' is a pre-computed vector embedding?

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

2. You are deploying a fraud detection model hosted on a third-party ML platform and accessing it via an external function in Snowflake. The model API has a strict rate limit of 10 requests per second. To prevent exceeding this limit and ensure smooth operation, what strategies could you implement within Snowflake, considering performance and cost implications? Select all that apply.

A) Scale up the Snowflake virtual warehouse to the largest size possible. This will allow for more concurrent requests without exceeding the rate limit.
B) Implement a custom queueing system within Snowflake using temporary tables and stored procedures to batch requests and send them to the external function at a controlled rate.
C) Implement a retry mechanism within the external function definition to handle API rate limit errors (e.g., HTTP 429 errors) using exponential backoff.
D) Utilize Snowflake's built-in caching mechanism for the external function results. This reduces the number of calls to the external API for repeated input data.
E) Implement a UDF (User-Defined Function) that sleeps for 0.1 seconds before each call to the external function. This guarantees a maximum rate of 10 requests per second.

3. A data scientist is tasked with identifying customer segments for a new marketing campaign using transaction data stored in Snowflake. The transaction data includes features like transaction amount, frequency, recency, and product category. Which unsupervised learning algorithm would be MOST appropriate for this task, considering scalability and Snowflake's data processing capabilities, and what preprocessing steps are crucial before applying the algorithm?

A) K-Means clustering, after standardizing numerical features (transaction amount, frequency, recency) and using one-hot encoding for product category. This is highly scalable within Snowflake using UDFs and SQL.
B) DBSCAN, using raw data without any scaling or encoding. The algorithm's density-based nature will automatically handle the varying scales of the features.
C) Principal Component Analysis (PCA) followed by K-Means. This reduces dimensionality and then clusters, improving the visualization of the cluster.
D) K-Means clustering, after applying min-max scaling to numerical features and converting categorical features to numerical representation. The optimal 'k' (number of clusters) should be determined using the elbow method or silhouette analysis.
E) Hierarchical clustering, using the complete linkage method and Euclidean distance. No preprocessing is necessary, as hierarchical clustering can handle raw data.

4. You are building a predictive model on customer churn using Snowflake data'. You observe that the distribution of 'TIME SINCE LAST PURCHASE' is heavily left-skewed. Which of the following strategies would be MOST appropriate to handle this skewness before feeding the data into a linear regression model to improve its performance? (Select TWO)

A) Apply a logarithmic transformation to the 'TIME SINCE LAST PURCHASE' column.
B) Use a winsorization technique to cap extreme values in the 'TIME SINCE LAST PURCHASE' column at a predefined percentile (e.g., 99th percentile).
C) Remove all records with 'TIME SINCE LAST PURCHASE' values below the mean.
D) Apply a square root transformation to the 'TIME_SINCE_LAST_PURCHASE' column.
E) Standardize the 'TIME_SINCE_LAST_PURCHASE' column using Z-score normalization.

5. You are developing a Spark application that needs to read data from a Snowflake table and write the processed data back to a different Snowflake table. Which of the following configurations and code snippets, used in conjunction with the Spark Snowflake Connector, would ensure secure and efficient data transfer, taking into account potential network latency and authentication best practices? Select all that apply.

A) Configure network timeout parameters in the Spark Snowflake Connector options to handle potential network latency, specifically 'networkTimeoutlnMilliSeconds'. Use 'PREACTIONS' and 'POSTACTIONS' to prepare and finalize data loading. Implement robust error handling to retry failed operations.
B) Use Snowflake's Key Pair Authentication. Store the private key securely, and configure 'sflJRL', 'sflJser', 'private_key', 'sfDatabase', and 'sfSchema' properties in the Spark configuration. Ensure the user has appropriate Snowflake privileges. Configure 'numPartitions' parameter based on the scale of data to parallelize read and write operations.
C) Employ Snowflake's OAuth authentication. Obtain an OAuth token and pass it as a parameter to the Spark Snowflake Connector. You need to also provide 'sfDatabase' and 'sfSchemas properties. Don't set the number of partitions, leaving it to Spark's default behavior.
D) Set 'sfURL', 'sfUser', 'sfPassword', 'sfDatabase', and 'sfSchema' properties in the Spark configuration. Use to read data and to write data. Rely on Snowflake's default JDBC driver settings for network optimization.
E)

Solutions: