Snowflake Snowflake Certifications DSA-C02 Questions & Answers

• Question 61:

  Which ones are the key actions in the data collection phase of Machine learning included? Choose 2.

  A. Label

  B. Ingest and Aggregate

  C. Probability

  D. Measure

  Correct Answer: AB

  Explanation:

  The key actions in the data collection phase include:

  Label: Labeled data is the raw data that was processed by adding one or more meaningful tags so that a model can learn from it. It will take some work to label it if such information is missing (manually or automatically).

  Ingest and Aggregate: Incorporating and combining data from many data sources is part of data collection in AI.

  Data collection

  Collecting data for training the ML model is the basic step in the machine learning pipeline. The predictions made by ML systems can only be as good as the data on which they have been trained. Following are some of the problems that can

  arise in data collection:

  Inaccurate data. The collected data could be unrelated to the problem statement. Missing data. Sub-data could be missing. That could take the form of empty values in columns or missing images for some class of prediction. Data imbalance.

  Some classes or categories in the data may have a disproportionately high or low number of corresponding samples. As a result, they risk being under- represented in the model.

  Data bias. Depending on how the data, subjects and labels themselves are chosen, the model could propagate inherent biases on gender, politics, age or region, for example.

  Data bias is difficult to detect and remove.

  Several techniques can be applied to address those problems:

  Pre-cleaned, freely available datasets. If the problem statement (for example, image classification, object recognition) aligns with a clean, pre-existing, properly formulated dataset, then take ad-vantage of existing, open-source expertise. Web

  crawling and scraping. Automated tools, bots and headless browsers can crawl and scrape websites for data.

  Private data. ML engineers can create their own data. This is helpful when the amount of data required to train the model is small and the problem statement is too specific to generalize over an open-source dataset.

  Custom data. Agencies can create or crowdsource the data for a fee.

• Question 62:

  Which of the following is a common evaluation metric for binary classification?

  A. Accuracy

  B. F1 score

  C. Mean squared error (MSE)

  D. Area under the ROC curve (AUC)

  Correct Answer: D

  Explanation:

  The area under the ROC curve (AUC) is a common evaluation metric for binary classification, which measures the performance of a classifier at different threshold values for the predicted probabilities. Other common metrics include

  accuracy, precision, recall, and F1 score, which are based on the confusion matrix of true positives, false positives, true negatives, and false negatives.

• Question 63:

  Mark the Incorrect understanding of Data Scientist about Streams? Choose 2.

  A. Streams on views support both local views and views shared using Snowflake Secure Data Sharing, including secure views.

  B. Streams can track changes in materialized views.

  C. Streams itself does not contain any table data.

  D. Streams do not support repeatable read isolation.

  Correct Answer: BD

  Explanation: Streams on views support both local views and views shared using Snowflake Secure Data Sharing, including secure views. Currently, streams cannot track changes in materialized views. stream itself does not contain any table data. A stream only stores an offset for the source object and returns CDC records by leveraging the versioning history for the source object. When the first stream for a table is created, several hidden columns are added to the source table and begin storing change tracking metadata. These columns consume a small amount of storage. The CDC records returned when querying a stream rely on a combination of the offset stored in the stream and the change tracking metadata stored in the table. Note that for streams on views, change tracking must be enabled explicitly for the view and underlying tables to add the hidden columns to these tables. Streams support repeatable read isolation. In repeatable read mode, multiple SQL statements within a transaction see the same set of records in a stream. This differs from the read committed mode supported for tables, in which statements see any changes made by previous statements executed within the same transaction, even though those changes are not yet committed. The delta records returned by streams in a transaction is the range from the current position of the stream until the transaction start time. The stream position advances to the transaction start time if the transaction commits; otherwise it stays at the same position.

• Question 64:

  How do you handle missing or corrupted data in a dataset?

  A. Drop missing rows or columns

  B. Replace missing values with mean/median/mode

  C. Assign a unique category to missing values

  D. All of the above

  Correct Answer: D

• Question 65:

  Secure Data Sharing do not let you share which of the following selected objects in a database in your account with other Snowflake accounts?

  A. Sequences

  B. Tables

  C. External tables

  D. Secure UDFs

  Correct Answer: A

  Explanation: Secure Data Sharing lets you share selected objects in a database in your account with other Snow-flake accounts. You can share the following Snowflake database objects: Tables External tables Secure views Secure materialized views Secure UDFs Snowflake enables the sharing of databases through shares, which are created by data providers and "imported" by data consumers.