Snowflake DSA-C02 Online Practice Questions and Exam Preparation

Snowflake DSA-C02 Online Questions & Answers

• Question 1:

  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
  A. Sequences

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  explanation:

  Explanation/Reference:

  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.

• Question 2:

  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
  D. All of the above

• Question 3:

  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.
  B. Streams can track changes in materialized views.
  D. Streams do not support repeatable read isolation.

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  explanation:

  Explanation/Reference:

  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 4:

  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)
  D. Area under the ROC curve (AUC)

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  explanation:

  Explanation/Reference:

  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 5:

  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
  A. Label
  B. Ingest and Aggregate

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  explanation:

  Explanation/Reference:

  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 6:

  Skewness of Normal distribution is ___________

  A. Negative
  B. Positive
  D. Undefined
  C

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  explanation:

  Explanation/Reference:

  Since the normal curve is symmetric about its mean, its skewness is zero. This is a theoretical explanation for mathematical proofs, you can refer to books or websites that speak on the same in detail.

• Question 7:

  Data Scientist used streams in ELT (extract, load, transform) processes where new data inserted in-to a staging table is tracked by a stream. A set of SQL statements transform and insert the stream contents into a set of production tables. Raw data is coming in the JSON format, but for analysis he needs to transform it into relational columns in the production tables. Which of the following Data transformation SQL function he can used to achieve the same?

  A. He could not apply Transformation on Stream table data.
  B. lateral flatten()
  C. METADATA$ACTION ()
  D. Transpose()
  B. lateral flatten()

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  explanation:

  Explanation/Reference:

  To know about lateral flatten SQL Function, please refer: https://docs.snowflake.com/en/sql-reference/constructs/join-lateral#example-of-using- lateral-with-flatten

• Question 8:

  Which of the following cross validation versions is suitable quicker cross-validation for very large datasets with hundreds of thousands of samples?

  A. k-fold cross-validation
  B. Leave-one-out cross-validation
  C. Holdout method
  D. All of the above
  C. Holdout method

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  explanation:

  Explanation/Reference:

  Holdout cross-validation method is suitable for very large dataset because it is the simplest and quicker to compute version of cross-validation.

  Holdout method

  In this method, the dataset is divided into two sets namely the training and the test set with the basic property that the training set is bigger than the test set. Later, the model is trained on the training dataset and evaluated using the test

  dataset.

• Question 9:

  Which of the learning methodology applies conditional probability of all the variables with respec-tive the dependent variable?

  A. Reinforcement learning
  B. Unsupervised learning
  C. Artificial learning
  D. Supervised learning
  A. Reinforcement learning

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  explanation:

  Explanation/Reference:

  Supervised learning methodology applies conditional probability of all the variables with respective the dependent variable and generally conditional probability of variables is nothing but a basic method of estimating the statistics for few random experiments. Conditional probability is thus the likelihood of an event or outcome occurring based on the occurrence of some other event or prior outcome. Two events are said tobe independent if one event occurring does not affect the probability that the other event will occur.

• Question 10:

  Which of the Following is not type of Windows function in Snowflake? Choose 2.

  A. Rank-related functions.
  B. Window frame functions.
  C. Aggregation window functions.
  D. Association functions.
  C. Aggregation window functions.
  D. Association functions.

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  explanation:

  Explanation/Reference:

  Window Functions

  A window function operates on a group ("window") of related rows. Each time a window function is called, it is passed a row (the current row in the window) and the window of rows that contain the current row. The window function returns one

  output row for each input row. The output depends on the individual row passed to the function and the values of the other rows in the window passed to the function. Some window functions are order-sensitive. There are two main types of

  order-sensitive window functions:

  Rank-related functions.

  Window frame functions.

  Rank-related functions list information based on the "rank" of a row. For example, if you rank stores in descending order by profit per year, the store with the most profit will be ranked 1; the second-most profitable store will be ranked 2, etc.

  Window frame functions allow you to perform rolling operations, such as calculating a running total or a moving average, on a subset of the rows in the window.