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Snowflake DSA-C03 Exam Overview:

Certification Vendor:	Snowflake
Exam Name:	SnowPro Advanced: Data Scientist Certification Exam (DSA-C03)
Exam Number:	DSA-C03
Available Languages:	English
Exam Format:	Multiple choice, Multiple select
Related Certifications:	SnowPro Core Certification
Recommended Training:	Snowflake Training & Learning Resources
Exam Registration:	Snowflake Certification Registration
Sample Questions:
Exam Way:	Online proctored or testing center
Pre Condition:	Recommended: SnowPro Core Certification or equivalent Snowflake experience
Official Syllabus URL:	https://www.snowflake.com/certifications/

Snowflake DSA-C03 Exam Syllabus Topics:

Answers Every DSA-C03 Candidate Should Read First

Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

Question 1

You are tasked with identifying fraudulent transactions in a large financial dataset stored in Snowflake using unsupervised learning. The dataset contains features like transaction amount, merchant ID, location, time, and user ID. You decide to use a combination of clustering and anomaly detection techniques. Which of the following steps and techniques would be MOST effective in achieving this goal while leveraging Snowflake's capabilities and minimizing false positives?

A. Perform K-means clustering on the entire dataset using all available features, then flag any transaction that falls outside of any cluster as fraudulent. Ignore any feature selection or engineering to simplify the process.
B. Use only the 'transaction amount' feature and perform histogram-based anomaly detection in Snowflake SQL by identifying values outside of the common ranges, disregarding other potentially relevant information.
C. Implement an Isolation Forest algorithm directly in SQL using complex JOINs and window functions to identify anomalies based on transaction volume and velocity.
D. Use a Snowflake Python UDF to perform feature selection, apply a combination of K-means clustering and anomaly detection techniques like Isolation Forest or Local Outlier Factor (LOF), and then score each transaction based on its likelihood of being fraudulent. Tune parameters and use a hold-out validation set to minimize false positives, using a Snowpark DataFrame to retrieve the data.
E. Apply Principal Component Analysis (PCA) for dimensionality reduction, then use DBSCAN clustering to identify dense regions of normal transactions and flag any transaction that is not within a dense region as potentially fraudulent. After, review the anomalous data points.

Question 2

You are tasked with forecasting the daily sales of a specific product for the next 30 days using Snowflake. You have historical sales data for the past 3 years, stored in a Snowflake table named 'SALES DATA', with columns 'SALE DATE (DATE type) and 'SALES AMOUNT' (NUMBER type). You want to use the Prophet library within a Snowflake User-Defined Function (UDF) for forecasting. The Prophet model requires the input data to have columns named 'ds' (for dates) and 'y' (for values). Which of the following code snippets demonstrates the CORRECT way to prepare and pass your data to the Prophet UDF in Snowflake, assuming you've already created the Python UDF 'prophet_forecast'?

A.

B.

C.

D.

E.

Question 3

You've trained a sales forecasting model using Snowpark ML and want to deploy it within Snowflake for real-time predictions. You've decided to store the predictions directly in a Snowflake table. The model predicts sales for different product categories based on historical data and promotional activities. Which of the following approaches is the MOST efficient and scalable way to store these predictions, considering a high volume of prediction requests and the need for quick retrieval for downstream dashboards?

A. Storing predictions in a separate table with a composite key of product category and timestamp, with clustering on the timestamp column and partitioning by product category.
B. Storing predictions in a VARIANT column in a single table. All prediction results for a given product category are stored as a JSON document within the VARIANT column.
C. Storing predictions in a single, wide table with all features and predictions as columns. No partitioning or clustering is implemented.
D. Storing predictions in a key-value store like Redis and referencing the keys from a Snowflake table. Requires external network access from Snowflake.
E. Storing predictions in an external stage (e.g., AWS S3) and querying them using an external table. The external table definition includes the sales prediction as a column.

Question 4

You're building a model to predict whether a user will click on an ad (binary classification: click or no-click) using Snowflake. The data is structured and includes features like user demographics, ad characteristics, and past user interactions. You've trained a logistic regression model using SNOWFLAKE.ML and are now evaluating its performance. You notice that while the overall accuracy is high (around 95%), the model performs poorly at predicting clicks (low recall for the 'click' class). Which of the following steps could you take to diagnose the issue and improve the model's ability to predict clicks, and how would you implement them using Snowflake SQL? SELECT ALL THAT APPLY.

A. Reduce the amount of training data to avoid overfitting. Overfitting is known to produce low recall for the 'click' class.
B. Calculate precision, recall, F I-score, and AUC for the 'click' class using SQL queries to get a more detailed understanding of the model's performance on the minority class. Example:

C. Implement feature engineering by creating interaction terms or polynomial features from existing features using SQL, to capture potentially non-linear relationships between features and the target variable. Example:

D. Increase the complexity of the model by switching to a non-linear algorithm like Random Forest or Gradient Boosting without performing hyperparameter tuning, as more complex models always perform better.
E. Generate a confusion matrix using SQL to visualize the model's performance across both classes. Example SQL:

Question 5

A team is using Snowflake to build a supervised machine learning model for image classification. The images are stored in a Snowflake table, and the labels are in a separate table. The goal is to train a model using Snowpark Python. Which of the following code snippets represents the MOST efficient way to join the image data with its corresponding labels, pre-process the images (resize and normalize), and prepare the data for model training using Snowpark DataFrame transformations? Assume contains image data as binary, 'label df contains the image labels, and 'resize normalize udf' is a UDF that handles resizing and normalization.

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B.

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D.

E.

Solutions: