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[General] Online DSA-C03 Training - Exam DSA-C03 Braindumps

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【General】 Online DSA-C03 Training - Exam DSA-C03 Braindumps

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Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q134-Q139):NEW QUESTION # 134
You are analyzing sales data in Snowflake using Snowpark to identify seasonality. You have a table named 'SALES DATA with columns 'SALE DATE (TIMESTAMP NTZ) and 'AMOUNT (NUMBER). You want to calculate the rolling average sales for each week over a period of 12 weeks using a Snowpark DataFrame. Which of the following Snowpark code snippets correctly implements this calculation?
  • A.
  • B.
  • C.
  • D.
  • E.
Answer: B,E
Explanation:
Options B and E are correct. They both calculate the 12 week rolling average grouped by week correctly and will display the average. Option B is the more correct of the two, because it does not require the user to sort the result to get the appropriate rolling average. Option A is incorrect because rangeBetween with seconds is not appropriate for weekly aggregation and calculation. Option C is incorrect because to_date would truncate the time component, grouping everything with the same date. Option D calculates a cumulative average since the beginning of the dataset

NEW QUESTION # 135
You are using Snowpark Feature Store to manage features for your machine learning models. You've created several Feature Groups and now want to consume these features for training a model. To optimize retrieval, you want to use point-in-time correctness. Which of the following actions/configurations are essential to ensure point-in-time correctness when retrieving features using Snowpark Feature Store?
  • A. Create an associated Stream on the source tables used for Feature Groups
  • B. Ensure that all source tables used by the Feature Groups have Change Data Capture (CDC) enabled.
  • C. Explicitly specify a in the call.
  • D. When creating Feature Groups, specify a 'timestamp_key' that represents the event timestamp of the data in the source tables.
  • E. Use the method on the Feature Store client, providing a dataframe containing the 'primary_keyS and the desired for each record.
Answer: D,E
Explanation:
Options B and C are correct. B: Specifying a 'timestamp_key' during Feature Group creation is crucial for enabling point-in-time correctness. This tells the Feature Store which column represents the event timestamp. C: The method is specifically designed for point-in-time lookups. It requires a dataframe containing primary keys and the desired timestamp for each lookup. This enables the Feature Store to retrieve the feature values as they were at that specific point in time. Option A is incorrect, while enabling CDC is valuable for incremental updates, it does not guarantee point-in-time correctness without specifying the timestamp key and retrieving historical features using that key. Option D is not necessary, streams enable incremental loads but are separate from point in time. Option E, is not needed, its implicit via using .

NEW QUESTION # 136
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 VARIANT column in a single table. All prediction results for a given product category are stored as a JSON document within the VARIANT column.
  • B. 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.
  • C. 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.
  • 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 a single, wide table with all features and predictions as columns. No partitioning or clustering is implemented.
Answer: C
Explanation:
Option B is the most efficient and scalable approach. Partitioning by product category allows for faster querying of specific categories. Clustering on the timestamp column ensures that recent predictions are quickly accessible. A composite key of product category and timestamp provides uniqueness. Option A lacks any optimization for querying. Option C can lead to performance issues with large JSON documents and querying specific values within the VARIANT. Option D introduces latency due to external stage access, and external tables are generally slower for frequent queries compared to native Snowflake tables. Option E introduces external dependency and network latency, which is generally not preferred if a native Snowflake solution is possible.

NEW QUESTION # 137
You are using Snowpark to build a collaborative filtering model for product recommendations. You have a table 'USER_ITEM INTERACTIONS with columns 'USER ID', 'ITEM ID', and 'INTERACTION TYPE'. You want to create a sparse matrix representation of this data using Snowpark, suitable for input into a matrix factorization algorithm. Which of the following code snippets best achieves this while efficiently handling large datasets within Snowflake?
  • A.
  • B.
  • C.
  • D.
  • E.
Answer: B
Explanation:
Option B is the most efficient and scalable approach. It utilizes Snowpark's 'pivot' function for efficient aggregation and sparse matrix creation directly within Snowflake's engine. Option A pulls the entire dataset into pandas, which is inefficient for large datasets. Options C and D are not complete as option C does not correctly populate values in the sparse matrix while creating empty matrix without any values , and D attempts a cross join which can lead to extreme performance issues. Option E, although similar to B, is incorrect because the pivot() function does not accept the values parameter. It's already present in agg function.

NEW QUESTION # 138
A data science team at a retail company is using Snowflake to store customer transaction data'. They want to segment customers based on their purchasing behavior using K-means clustering. Which of the following approaches is MOST efficient for performing K-means clustering on a very large customer dataset in Snowflake, minimizing data movement and leveraging Snowflake's compute capabilities, and adhering to best practices for data security and governance?
  • A. Using Snowflake's Snowpark DataFrame API with a Python UDF to preprocess the data and execute the K-means algorithm within the Snowflake environment. This approach allows for scalable processing within Snowflake's compute resources with data kept securely within the governance boundaries.
  • B. Employing only Snowflake's SQL capabilities to perform approximate nearest neighbor searches without implementing the full K-means algorithm. This compromises the accuracy and effectiveness of the clustering results.
  • C. Implementing K-means clustering using SQL queries with iterative JOINs and aggregations to calculate centroids and assign data points to clusters. This approach is computationally expensive and not recommended for large datasets. Moreover, security considerations are minimal.
  • D. Using a Snowflake User-Defined Function (UDF) written in Python that leverages the scikit-learn library within the UDF to perform K-means clustering directly on the data within Snowflake. Ensure the UDF is called with appropriate resource allocation (WAREHOUSE SIZE) and security context.
  • E. Exporting the entire customer transaction dataset from Snowflake to an external Python environment, performing K-means clustering using scikit-learn, and then importing the cluster assignments back into Snowflake as a new table. This approach involves significant data egress and potential security risks.
Answer: A
Explanation:
Snowpark and Python UDFs provide a way to execute code within the Snowflake environment, leveraging its compute resources and keeping data within Snowflake's security and governance boundaries. This avoids data egress and is more efficient than exporting data or attempting to implement K-means directly in SQL. While B is potentially viable, D leveraging DataFrames provides further optimization. The other options are either inefficient or insecure.

NEW QUESTION # 139
......
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