Latest May-2024 DSA-C02 Dumps PDF And Certification Training
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NEW QUESTION # 25
Which of the learning methodology applies conditional probability of all the variables with respec-tive the dependent variable?
- A. Artificial learning
- B. Unsupervised learning
- C. Reinforcement learning
- D. Supervised learning
Answer: C
Explanation:
Explanation
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.
NEW QUESTION # 26
Which ones are the key actions in the data collection phase of Machine learning included?
- A. Probability
- B. Label
- C. Ingest and Aggregate
- D. Measure
Answer: B,C
Explanation:
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.
NEW QUESTION # 27
Which one is not Types of Feature Scaling?
- A. Economy Scaling
- B. Standard Scaling
- C. Min-Max Scaling
- D. Robust Scaling
Answer: C
Explanation:
ExplanationFeature Scaling
Feature Scaling is the process of transforming the features so that they have a similar scale. This is important in machine learning because the scale of the features can affect the performance of the model.
Types of Feature Scaling:
Min-Max Scaling: Rescaling the features to a specific range, such as between 0 and 1, by subtracting the minimum value and dividing by the range.
Standard Scaling: Rescaling the features to have a mean of 0 and a standard deviation of 1 by subtracting the mean and dividing by the standard deviation.
Robust Scaling: Rescaling the features to be robust to outliers by dividing them by the interquartile range.
Benefits of Feature Scaling:
Improves Model Performance: By transforming the features to have a similar scale, the model can learn from all features equally and avoid being dominated by a few large features.
Increases Model Robustness: By transforming the features to be robust to outliers, the model can become more robust to anomalies.
Improves Computational Efficiency: Many machine learning algorithms, such as k-nearest neighbors, are sensitive to the scale of the features and perform better with scaled features.
Improves Model Interpretability: By transforming the features to have a similar scale, it can be easier to understand the model's predictions.
NEW QUESTION # 28
What Can Snowflake Data Scientist do in the Snowflake Marketplace as Consumer?
- A. Receive frictionless access to raw data products from vendors.
- B. Use the business intelligence (BI)/ML/Deep learning tools of her choice.
- C. Discover and test third-party data sources.
- D. Combine new datasets with your existing data in Snowflake to derive new business in-sights.
Answer: A,B,C,D
Explanation:
Explanation
As a consumer, you can do the following:
Discover and test third-party data sources.
Receive frictionless access to raw data products from vendors.
Combine new datasets with your existing data in Snowflake to derive new business insights.
Have datasets available instantly and updated continually for users.
Eliminate the costs of building and maintaining various APIs and data pipelines to load and up-date data.
Use the business intelligence (BI) tools of your choice.
NEW QUESTION # 29
Which type of Machine learning Data Scientist generally used for solving classification and regression problems?
- A. Regression Learning
- B. Reinforcement Learning
- C. Instructor Learning
- D. Supervised
- E. Unsupervised
Answer: D
Explanation:
Explanation
Supervised Learning
Overview:
Supervised learning is a type of machine learning that uses labeled data to train machine learning models. In labeled data, the output is already known. The model just needs to map the inputs to the respective outputs.
Algorithms:
Some of the most popularly used supervised learning algorithms are:
Linear Regression
Logistic Regression
Support Vector Machine
K Nearest Neighbor
Decision Tree
Random Forest
Naive Bayes
Working:
Supervised learning algorithms take labelled inputs and map them to the known outputs, which means you already know the target variable.
Supervised Learning methods need external supervision to train machine learning models. Hence, the name supervised. They need guidance and additional information to return the desired result.
Applications:
Supervised learning algorithms are generally used for solving classification and regression problems.
Few of the top supervised learning applications are weather prediction, sales forecasting, stock price analysis.
NEW QUESTION # 30
Select the Data Science Tools which are known to provide native connectivity to Snowflake?
- A. DvSUM
- B. Denodo
- C. HEX
- D. DiYotta
Answer: C
Explanation:
Explanation
Hex - collaborative data science and analytics platform
Denodo - data virtualization and federation platform
DvSum - data catalog and data intelligence platform
Diyotta - data integration and migration
NEW QUESTION # 31
Which one is the incorrect option to share data in Snowflake?
- A. a Direct Marketplace, in which you directly share specific database objects (a share) to another account in your region using Snowflake Marketplace.
- B. a Direct Share, in which you directly share specific database objects (a share) to anoth-er account in your region.
- C. a Data Exchange, in which you set up and manage a group of accounts and offer a share to that group.
- D. a Listing, in which you offer a share and additional metadata as a data product to one or more accounts.
Answer: A
Explanation:
Explanation
Options for Sharing in Snowflake
You can share data in Snowflake using one of the following options:
a Listing, in which you offer a share and additional metadata as a data product to one or more ac-counts,
a Direct Share, in which you directly share specific database objects (a share) to another account in your region,
a Data Exchange, in which you set up and manage a group of accounts and offer a share to that group.
NEW QUESTION # 32
Which of the following is a useful tool for gaining insights into the relationship between features and predictions?
- A. sklearn plots
- B. numpy plots
- C. Partial dependence plots(PDP)
- D. FULL dependence plots (FDP)
Answer: C
Explanation:
Explanation
Partial dependence plots (PDP) is a useful tool for gaining insights into the relationship between features and predictions. It helps us understand how different values of a particular feature impact model's predictions.
NEW QUESTION # 33
Which of the following is a common evaluation metric for binary classification?
- A. Area under the ROC curve (AUC)
- B. F1 score
- C. Mean squared error (MSE)
- D. Accuracy
Answer: A
Explanation:
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.
NEW QUESTION # 34
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. lateral flatten()
- B. METADATA$ACTION ()
- C. Transpose()
- D. He could not apply Transformation on Stream table data.
Answer: A
Explanation:
Explanation
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
NEW QUESTION # 35
Which object records data manipulation language (DML) changes made to tables, including inserts, updates, and deletes, as well as metadata about each change, so that actions can be taken using the changed data of Data Science Pipelines?
- A. Stream
- B. Tags
- C. Task
- D. OFFSET
- E. Dynamic tables
- F. Delta
Answer: A
Explanation:
Explanation
A stream object records data manipulation language (DML) changes made to tables, including inserts, updates, and deletes, as well as metadata about each change,so that actions can be taken using the changed data. This process is referred to as change data capture (CDC). An individual table stream tracks the changes made to rows in a source table. A table stream (also referred to as simply a "stream") makes a "change table" available of what changed, at therow level, between two transactional points of time in a table. This allows querying and consuming a sequence of change records in a transactional fashion.
Streams can be created to query change data on the following objects:
Standard tables, including shared tables.
Views, including secure views
Directory tables
Event tables
NEW QUESTION # 36
Which Python method can be used to Remove duplicates by Data scientist?
- A. clean_duplicates()
- B. drop_duplicates()
- C. remove_duplicates()
- D. duplicates()
Answer: A
Explanation:
Explanation
The drop_duplicates() method removes duplicate rows.
dataframe.drop_duplicates(subset, keep, inplace, ignore_index)
Remove duplicate rows from the DataFrame:
1.import pandas as pd
2.data = {
3."name": ["Peter", "Mary", "John", "Mary"],
4."age": [50, 40, 30, 40],
5."qualified": [True, False, False, False]
6.}
7.
8.df = pd.DataFrame(data)
9.newdf = df.drop_duplicates()
NEW QUESTION # 37
Which type of Python UDFs let you define Python functions that receive batches of input rows as Pandas DataFrames and return batches of results as Pandas arrays or Series?
- A. MPP Python UDFs
- B. Scaler Python UDFs
- C. Vectorized Python UDFs
- D. Hybrid Python UDFs
Answer: C
Explanation:
Explanation
Vectorized Python UDFs let you define Python functions that receive batches of input rows as Pandas DataFrames and return batches of results as Pandas arrays or Series. You call vectorized Py-thon UDFs the same way you call other Python UDFs.
Advantages of using vectorized Python UDFs compared to the default row-by-row processing pat-tern include:
The potential for better performance if your Python code operates efficiently on batches of rows.
Less transformation logic required if you are calling into libraries that operate on Pandas Data-Frames or Pandas arrays.
When you use vectorized Python UDFs:
You do not need to change how you write queries using Python UDFs. All batching is handled by the UDF framework rather than your own code.
As with non-vectorized UDFs, there is no guarantee of which instances of your handler code will see which batches of input.
NEW QUESTION # 38
Which one is not the feature engineering techniques used in ML data science world?
- A. Imputation
- B. One hot encoding
- C. Binning
- D. Statistical
Answer: D
Explanation:
Explanation
Feature engineering is the pre-processing step of machine learning, which is used to transform raw data into features that can be used for creating a predictive model using Machine learning or statistical Modelling.
What is a feature?
Generally, all machine learning algorithms take input data to generate the output. The input data re-mains in a tabular form consisting of rows (instances or observations) and columns (variable or at-tributes), and these attributes are often known as features. For example, an image is an instance in computer vision, but a line in the image could be the feature. Similarly, in NLP, a document can be an observation, and the word count could be the feature. So, we can say a feature is an attribute that impacts a problem or is useful for the problem.
What is Feature Engineering?
Feature engineering is the pre-processing step of machine learning, which extracts features from raw data. It helps to represent an underlying problem to predictive models in a better way, which as a result, improve the accuracy of the model for unseen data. The predictive model contains predictor variables and an outcome variable, and while the feature engineering process selects the most useful predictor variables for the model.
Some of the popular feature engineering techniques include:
1. Imputation
Feature engineering deals with inappropriate data, missing values,human interruption, general errors, insufficient data sources, etc. Missing values within the dataset highly affect the performance of the algorithm, and to deal with them "Imputation" technique is used. Imputation is responsible for handling irregularities within the dataset.
For example, removing the missing values from the complete row or complete column by a huge percentage of missing values. But at the same time, to maintain the data size, it is required to impute the missing data, which can be done as:
For numerical data imputation, a default value can be imputed in a column, and missing values can be filled with means or medians of the columns.
For categorical data imputation, missing values can be interchanged with the maximum occurred value in a column.
2. Handling Outliers
Outliers are the deviated values or data points that are observed too away from other data points in such a way that they badly affect the performance of the model. Outliers can be handled with this feature engineering technique. This technique first identifies the outliers and then remove them out.
Standard deviation can be used to identify the outliers. For example, each value within a space has a definite to an average distance, but if a value is greater distant than acertain value, it can be considered as an outlier.
Z-score can also be used to detect outliers.
3. Log transform
Logarithm transformation or log transform is one of the commonly used mathematical techniques in machine learning. Log transform helps in handling the skewed data, and it makes the distribution more approximate to normal after transformation. It also reduces the effects of outliers on the data, as because of the normalization of magnitude differences, a model becomes much robust.
4. Binning
In machine learning, overfitting is one of the main issues that degrade the performance of the model and which occurs due to a greater number of parameters and noisydata. However, one of the popular techniques of feature engineering, "binning", can be used to normalize the noisy data. This process involves segmenting different features into bins.
5. Feature Split
As the name suggests, feature split is the process of splitting features intimately into two or more parts and performing to make new features. This technique helps the algorithms to better understand and learn the patterns in the dataset.
The feature splitting process enables the new features to be clustered and binned, which results in extracting useful information and improving the performance of the data models.
6. One hot encoding
One hot encoding is the popular encoding technique in machine learning. It is a technique that converts the categorical data in a form so that they can be easily understood by machine learning algorithms and hence can make a good prediction. It enables group theof categorical data without losing any information.
NEW QUESTION # 39
Which is the visual depiction of data through the use of graphs, plots, and informational graphics?
- A. Data Mining
- B. Data Interpretation
- C. Data visualization
- D. Data Virtualization
Answer: A
Explanation:
Explanation
Data visualization is the visual depiction of data through the use of graphs, plots, and informational graphics.
Its practitioners use statistics and data science to conveythe meaning behind data in ethical and accurate ways.
NEW QUESTION # 40
Mark the Incorrect statements regarding MIN / MAX Functions?
- A. NULL values are skipped unless all the records are NULL
- B. NULL values are ignored unless all the records are NULL, in which case a NULL value is returned
- C. The data type of the returned value is the same as the data type of the input values
- D. For compatibility with other systems, the DISTINCT keyword can be specified as an argument for MIN or MAX, but it does not have any effect
Answer: B
Explanation:
Explanation
NULL values are ignored unless all the records are NULL, in which case a NULL value is returned
NEW QUESTION # 41
Which ones are the known limitations of using External function?
- A. External functions have more overhead than internal functions (both built-in functions and internal UDFs) and usually execute more slowly
- B. Currently, external functions must be scalar functions. A scalar external function re-turns a single value for each input row.
- C. Currently, external functions cannot be shared with data consumers via Secure Data Sharing.
- D. An external function accessed through an AWS API Gateway private endpoint can be accessed only from a Snowflake VPC (Virtual Private Cloud) on AWS and in the same AWS region.
Answer: A,B,C,D
NEW QUESTION # 42
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. External tables
- B. Tables
- C. Sequences
- D. Secure UDFs
Answer: C
Explanation:
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.
NEW QUESTION # 43
Skewness of Normal distribution is ___________
- A. Undefined
- B. 0
- C. Positive
- D. Negative
Answer: B
Explanation:
Explanation
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.
NEW QUESTION # 44
Mark the correct steps for saving the contents of a DataFrame to aSnowflake table as part of Moving Data from Spark to Snowflake?
- A. Step 1.Use the writer() method of the DataFrame to construct a DataFrameWriter.
Step 2.Specify SNOWFLAKE_SOURCE_NAME using the format() method.
Step 3.Use the dbtable option to specify the table to which data is written.
Step 4.Specify the connector options using either the option() or options() method.
Step 5.Use the save() method to specify the save mode for the content. - B. Step 1.Use the write() method of the DataFrame to construct a DataFrameWriter.
Step 2.Specify SNOWFLAKE_SOURCE_NAME using the format() method.
Step 3.Specify the connector options using either the option() or options() method.
Step 4.Use the dbtable option to specify the table to which data is written.
Step 5.Use the mode() method to specify the save mode for the content.
(Correct) - C. Step 1.Use the PUT() method of the DataFrame to construct a DataFrameWriter.
Step 2.Specify SNOWFLAKE_SOURCE_NAME using the NAME() method.
Step 3.Use the dbtable option to specify the table to which data is written.
Step 4.Specify the connector options using either the option() or options() method.
Step 5.Use the save() method to specify the save mode for the content. - D. Step 1.Use the PUT() method of the DataFrame to construct a DataFrameWriter.
Step 2.Specify SNOWFLAKE_SOURCE_NAME using the format() method.
Step 3.Specify the connector options using either the option() or options() method.
Step 4.Use the dbtable option to specify the table to which data is written.
Step 5.Use the save() method to specify the save mode for the content.
Answer: B
Explanation:
Explanation
Moving Data from Spark to Snowflake
The steps for saving the contents of a DataFrame to a Snowflake table are similar to writing from Snowflake to Spark:
1. Use the write() method of the DataFrame to construct a DataFrameWriter.
2. Specify SNOWFLAKE_SOURCE_NAME using the format() method.
3. Specify the connector options using either the option() or options() method.
4. Use the dbtable option to specify the table to which data is written.
5. Use the mode() method to specify the save mode for the content.
Examples
1.df.write
2..format(SNOWFLAKE_SOURCE_NAME)
3..options(sfOptions)
4..option("dbtable", "t2")
5..mode(SaveMode.Overwrite)
6..save()
NEW QUESTION # 45
Which is the visual depiction of data through the use of graphs, plots, and informational graphics?
- A. Data Mining
- B. Data Interpretation
- C. Data visualization
- D. Data Virtualization
Answer: A
Explanation:
Explanation
Data visualization is the visual depiction of data through the use of graphs, plots, and informational graphics.
Its practitioners use statistics and data science to conveythe meaning behind data in ethical and accurate ways.
NEW QUESTION # 46
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