Online Questions - Valid Practice To your DSA-C02 Exam (Updated 67 Questions)
Practice To DSA-C02 - Remarkable Practice On your SnowPro Advanced: Data Scientist Certification Exam Exam
NEW QUESTION # 15
There are a couple of different types of classification tasks in machine learning, Choose the Correct Classification which best categorized the below Application Tasks in Machine learning?
To detect whether email is spam or not
To determine whether or not a patient has a certain disease in medicine.
To determine whether or not quality specifications were met when it comes to QA (Quality Assurance).
- A. Multi-Class Classification
- B. Binary Classification
- C. Multi-Label Classification
- D. Logistic Regression
Answer: B
Explanation:
Explanation
The Supervised Machine Learning algorithm can be broadly classified into Regression and Classification Algorithms. In Regression algorithms, we have predicted the output for continuous values, but to predict the categorical values, we need Classification algorithms.
What is the Classification Algorithm?
The Classification algorithm is a Supervised Learning technique that is used to identify the category of new observations on the basis of training data. In Classification, a program learns from the given dataset or observations and then classifies new observation into a number of classes or groups. Such as, Yes or No, 0 or
1, Spam or Not Spam, cat or dog, etc. Classes can be called as targets/labels or categories.
Unlike regression, the output variable of Classification is a category, not a value, such as "Green or Blue",
"fruit or animal", etc. Since the Classification algorithm is a Supervised learning technique, hence it takes labeled input data, which means it contains input with the corresponding output.
In classification algorithm, a discrete output function(y) is mapped to input variable(x).
y=f(x), where y = categorical output
The best example of an ML classification algorithm is Email Spam Detector.
The main goal of the Classification algorithm is to identify the category of a given dataset, and these algorithms are mainly used to predict the output for the categorical data.
The algorithm which implements the classification on a dataset is known as a classifier. There are two types of Classifications:
Binary Classifier: If the classification problem has only two possible outcomes, then it is called as Binary Classifier.
Examples: YES or NO, MALE or FEMALE, SPAM or NOT SPAM, CAT or DOG, etc.
Multi-class Classifier: If a classification problem has more than two outcomes, then it is called as Multi-class Classifier.
Example: Classifications of types of crops, Classification of types of music.
Binary classification in deep learning refers to the type of classification where we have two class labels - one normal and one abnormal. Some examples of binary classification use:
To detect whether email is spam or not
To determine whether or not a patient has a certain disease in medicine.
To determine whether or not quality specifications were met when it comes to QA (Quality Assurance).
For example, the normal class label would be that a patient has the disease, and the abnormal class label would be that they do not, or vice-versa.
As is with every other type of classification, it is only as good as the binary classification dataset that it has - or, in other words, the more training and data it has, the better it is.
NEW QUESTION # 16
Which command manually triggers a single run of a scheduled task (either a standalone task or the root task in a DAG) independent of the schedule defined for the task?
- A. CALL TASK
- B. RUN TASK
- C. EXECUTE TASK
- D. RUN ROOT TASK
Answer: C
Explanation:
Explanation
The EXECUTE TASK command manually triggers a single run of a scheduled task (either a standalone task or the root task in a DAG) independent of the schedule defined for the task. A successful run of a roottask triggers a cascading run of child tasks in the DAG as their precedent task completes, as though the root task had run on its defined schedule.
This SQL command is useful for testing new or modified standalone tasks and DAGs before you enable them to execute SQL code in production.
Call this SQL command directly in scripts or in stored procedures. In addition, this command sup-ports integrating tasks in external data pipelines. Any third-party services that can authenticate into your Snowflake account and authorize SQL actions can execute the EXECUTE TASK command to run tasks.
NEW QUESTION # 17
Select the correct mappings:
I. W Weights or Coefficients of independent variables in the Linear regression model --> Model Pa-rameter II. K in the K-Nearest Neighbour algorithm --> Model Hyperparameter III. Learning rate for training a neural network --> Model Hyperparameter IV. Batch Size --> Model Parameter
- A. II,III,IV
- B. III,IV
- C. I,II,III
- D. I,II
Answer: C
Explanation:
Explanation
Hyperparameters in Machine learning are those parameters that are explicitly defined by the user to control the learning process. These hyperparameters are used to improve the learning of the model, and their values are set before starting the learning process of the model.
What are hyperparameters?
In Machine Learning/Deep Learning, a model is represented by its parameters. In contrast, a training process involves selecting the best/optimal hyperparameters that are used by learning algorithms to provide the best result. So, what are these hyperparameters? The answer is, "Hyperparameters are defined as the parameters that are explicitly defined by the user to control the learning process." Here the prefix "hyper" suggests that the parameters are top-level parameters that are used in con-trolling the learning process. The value of the Hyperparameter is selected and set by the machine learning engineer before the learning algorithm begins training the model. Hence, these are external to the model, and their values cannot be changed during the training process.
Some examples of Hyperparameters in Machine Learning
The k in kNN or K-Nearest Neighbour algorithm
Learning rate for training a neural network
Train-test split ratio
Batch Size
Number of Epochs
Branches in Decision Tree
Number of clusters in Clustering Algorithm
Model Parameters:
Model parameters are configuration variables that are internal to the model, and a model learns them on its own. For example, W Weights or Coefficients of independentvariables in the Linear regression model. or Weights or Coefficients of independent variables in SVM, weight, and biases of a neural network, cluster centroid in clustering. Some key points for model parameters are as follows:
They are used by the model for making predictions.
They are learned by the model from the data itself
These are usually not set manually.
These are the part of the model and key to a machine learning Algorithm.
Model Hyperparameters:
Hyperparameters are those parameters that are explicitly defined by the user to control the learning process.
Some key points for model parameters are as follows:
These are usually defined manually by the machine learning engineer.
One cannot know the exact best value for hyperparameters for the given problem. The best value can be determined either by the rule of thumb or by trial and error.
Some examples of Hyperparameters are the learning rate for training a neural network, K in the KNN algorithm.
NEW QUESTION # 18
Which of the following Functions do Support Windowing?
- A. ENCRYPT
- B. LISTAGG
- C. EXTRACT
- D. HASH_AGG
Answer: B
Explanation:
Explanation
What is a Window?
A window is a group of related rows. For example, a window might be defined based on timestamps, with all rows in the same month grouped in the same window. Or a window might be defined based on location, with all rows from a particular city grouped in the same window.
A window can consist of zero, one, or multiple rows. For simplicity, Snowflake documentation usually says that a window contains multiple rows.
What is a Window Function?
A window function is any function that operates over a window of rows.
A window function is generally passed two parameters:
A row. More precisely, a window function is passed 0 or more expressions. In almost all cases, at least one of those expressions references a column in that row. (Most window functions require at least one column or expression, but a few window functions, such as some rank-related functions, do not required an explicit column or expression.) A window of related rows that includes that row. The window can be the entire table, or a subset of the rows in the table.
For non-window functions, all arguments are usually passed explicitly to the function, for example:
MY_FUNCTION(argument1, argument2, ...)
Window functions behave differently; although the current row is passed as an argument the normal way, the window is passed through a separate clause, called an OVER clause. The syntax of the OVER clause is documented later.
LISTAGG
Returns the concatenated input values, separated by the delimiter string.
Window function
1.LISTAGG( [ DISTINCT ] <expr1> [, <delimiter> ] )
2.[ WITHIN GROUP ( <orderby_clause> ) ]
3.OVER ( [ PARTITION BY <expr2> ] )
HASH_AGG
Returns an aggregate signed 64-bit hash value over the (unordered) set of input rows. HASH_AGG never returns NULL, even if no input is provided. Empty input "hashes" to 0.
Window function
HASH_AGG( [ DISTINCT ] <expr> [ , <expr2> ... ] ) OVER ( [ PARTITION BY <expr3> ] ) HASH_AGG(*) OVER ( [ PARTITION BY <expr3> ] )
NEW QUESTION # 19
Which type of Machine learning Data Scientist generally used for solving classification and regression problems?
- A. Unsupervised
- B. Supervised
- C. Regression Learning
- D. Reinforcement Learning
- E. Instructor Learning
Answer: B
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 # 20
What is the risk with tuning hyper-parameters using a test dataset?
- A. Model will underfit the test set
- B. Model will overfit the test set
- C. Model will perform balanced
- D. Model will overfit the training set
Answer: B
Explanation:
Explanation
The model will not generalize well to unseen data because it overfits the test set. Tuning model hyper-parameters to a test set means that the hyper-parameters may overfit to that test set. If the same test set is used to estimate performance, it will produce an overestimate. The test set should be used only for testing, not for parameter tuning.
Using a separate validation set for tuning and test set for measuring performance provides unbiased, realistic measurement of performance.
What are hyper-parameters?
Hyper-parameters are parameters whose values control the learning process and determine the values of model parameters that a learning algorithm ends up learning. We can't calculate their values from the data.
Example: Number of clusters in clustering, number of hidden layers in a neural network, and depth of a tree are some of the examples of hyper-parameters.
What is the hyper-parameter tuning?
Hyper-parameter tuning is the process of choosing the right combination of hyper-parameters that maximizes the model performance. It works by running multiple trials in a single training process. Each trial is a complete execution of your training application with values for your chosen hyper-parameters, set within the limits you specify. This process once finished will give you the set of hyper-parameter values that are best suited for the model to give optimal results.
NEW QUESTION # 21
Which of the following metrics are used to evaluate classification models?
- A. Confusion matrix
- B. All of the above
- C. F1 score
- D. Area under the ROC curve
Answer: B
Explanation:
Explanation
Evaluation metrics are tied to machine learning tasks. There are different metrics for the tasks of classification and regression. Some metrics, like precision-recall, are useful for multiple tasks. Classification and regression are examples of supervised learning, which constitutes a majority of machine learning applications. Using different metrics for performance evaluation, we should be able to im-prove our model's overall predictive power before we roll it out for production on unseen data. Without doing a proper evaluation of the Machine Learning model by using different evaluation metrics, and only depending on accuracy, can lead to a problemwhen the respective model is deployed on unseen data and may end in poor predictions.
Classification metrics are evaluation measures used to assess the performance of a classification model.
Common metrics include accuracy (proportion of correct predictions), precision (true positives over total predicted positives), recall (true positives over total actual positives), F1 score (har-monic mean of precision and recall), and area under the receiver operating characteristic curve (AUC-ROC).
Confusion Matrix
Confusion Matrix is a performance measurement for the machine learning classification problems where the output can be two or more classes. It is a table with combinations of predicted and actual values.
It is extremely useful for measuring the Recall, Precision, Accuracy, and AUC-ROC curves.
The four commonly used metrics for evaluating classifier performance are:
1. Accuracy: The proportion of correct predictions out of the total predictions.
2. Precision: The proportion of true positive predictions out of the total positive predictions (precision = true positives / (true positives + false positives)).
3. Recall (Sensitivity or True Positive Rate): The proportion of true positive predictions out of the total actual positive instances (recall = true positives / (true positives + false negatives)).
4. F1 Score: The harmonic mean of precision and recall, providing a balance between the two metrics (F1 score = 2 * ((precision * recall) / (precision + recall))).
These metrics help assess the classifier's effectiveness in correctly classifying instances of different classes.
Understanding how well a machine learning model will perform on unseen data is the main purpose behind working with these evaluation metrics. Metrics like accuracy, precision, recall are good ways to evaluate classification models for balanced datasets, but if the data is imbalanced then other methods like ROC/AUC perform better in evaluating the model performance.
ROC curve isn't just a single number but it's a whole curve that provides nuanced details about the behavior of the classifier. It is also hard to quickly compare many ROC curves to each other.
NEW QUESTION # 22
Which of the following is a Python-based web application framework for visualizing data and analyzing results in a more efficient and flexible way?
- A. StreamBI
- B. Streamsets
- C. Rapter
- D. Streamlit
Answer: D
Explanation:
Explanation
Streamlit is a Python-based web application framework for visualizing data and analyzing results in a more efficient and flexible way. It is an open source library that assists data scientists and academics to develop Machine Learning (ML) visualization dashboards in a short period of time. We can build and deploy powerful data applications with just a few lines of code.
Why Streamlit?
Currently, real-world applications are in high demand and developers are developing new libraries and frameworks to make on-the-go dashboards easier to build and deploy. Streamlit is a library that reduces your dashboard development time from days to hours. Following are some reasons to choose the Streamlit:
It is a free and open-source library.
Installing Streamlit is as simple as installing any other python package It is easy to learn because you won't need any web development experience, only a basic under-standing of Python is enough to build a data application.
It is compatible with almost all machine learning frameworks, including Tensorflow and Pytorch, Scikit-learn, and visualization libraries such as Seaborn, Altair, Plotly, and many others.
NEW QUESTION # 23
Which of the following process best covers all of the following characteristics?
Collecting descriptive statistics like min, max, count and sum.
Collecting data types, length and recurring patterns.
Tagging data with keywords, descriptions or categories.
Performing data quality assessment, risk of performing joins on the data.
Discovering metadata and assessing its accuracy.
Identifying distributions, key candidates, foreign-key candidates,functional dependencies, embedded value dependencies, and performing inter-table analysis.
- A. Data Virtualization
- B. Data Profiling
- C. Data Visualization
- D. Data Collection
Answer: B
Explanation:
Explanation
Data processing and analysis cannot happen without data profiling-reviewing source data for con-tent and quality. As data gets bigger and infrastructure moves to the cloud, data profiling is increasingly important.
What is data profiling?
Data profiling is the process of reviewing source data, understanding structure, content and interrelationships, and identifying potential for data projects.
Data profiling is a crucial part of:
Data warehouse and business intelligence (DW/BI) projects-dataprofiling can uncover data quality issues in data sources, and what needs to be corrected in ETL.
Data conversion and migration projects-data profiling can identify data quality issues, which you can handle in scripts and data integration tools copying data from source to target. It can also un-cover new requirements for the target system.
Source system data quality projects-data profiling can highlight data which suffers from serious or numerous quality issues, and the source of the issues (e.g. user inputs, errors in interfaces, data corruption).
Data profiling involves:
Collecting descriptive statistics like min, max, count and sum.
Collecting data types, length and recurring patterns.
Tagging data with keywords, descriptions or categories.
Performing data quality assessment, risk of performing joins on the data.
Discovering metadata and assessing its accuracy.
Identifying distributions, key candidates, foreign-key candidates, functional dependencies, embedded value dependencies, and performing inter-table analysis.
NEW QUESTION # 24
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. Secure UDFs
- D. Sequences
Answer: D
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 # 25
Consider a data frame df with columns ['A', 'B', 'C', 'D'] and rows ['r1', 'r2', 'r3']. What does the ex-pression df[lambda x : x.index.str.endswith('3')] do?
- A. Results in Error
- B. Returns the row name r3
- C. Filters the row labelled r3
- D. Returns the third column
Answer: C
Explanation:
Explanation
It will Filters the row labelled r3.
NEW QUESTION # 26
Which of the following is a common evaluation metric for binary classification?
- A. Mean squared error (MSE)
- B. Area under the ROC curve (AUC)
- C. Accuracy
- D. F1 score
Answer: B
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 # 27
Consider a data frame df with 10 rows and index [ 'r1', 'r2', 'r3', 'row4', 'row5', 'row6', 'r7', 'r8', 'r9', 'row10'].
What does the aggregate method shown in below code do?
g = df.groupby(df.index.str.len())
g.aggregate({'A':len, 'B':np.sum})
- A. Computes length of column A and Sum of Column B values of each group
- B. Computes length of column A
- C. Computes Sum of column A values
- D. Computes length of column A and Sum of Column B values
Answer: A
Explanation:
Explanation
Computes length of column A and Sum of Column B values of each group
NEW QUESTION # 28
You previously trained a model using a training dataset. You want to detect any data drift in the new data collected since the model was trained.
What should you do?
- A. Retrained your training dataset after correcting data outliers & no need to introduce new data.
- B. Create a new dataset using the new data and a timestamp column and create a data drift monitor that uses the training dataset as a baseline and the new dataset as a target.
- C. Add the new data to the existing dataset and enable Application Insights for the service where the model is deployed.
- D. Create a new version of the dataset using only the new data and retrain the model.
Answer: B
Explanation:
Explanation
To track changing data trends, create a data drift monitor that uses the training data as a baseline and the new data as a target.
Model drift and decay are concepts that describe the process during which the performance of a model deployed to production degrades on new, unseen data or the underlying assumptions about the data change.
These are important metrics to track once models are deployed toproduction. Models must be regularly re-trained on new data. This is referred to as refitting the model. This can be done either on a periodic basis, or, in an ideal scenario, retraining can be triggered when the performance of the model degrades below a certain pre-defined threshold.
NEW QUESTION # 29
Consider a data frame df with 10 rows and index [ 'r1', 'r2', 'r3', 'row4', 'row5', 'row6', 'r7', 'r8', 'r9', 'row10'].
What does the expression g = df.groupby(df.index.str.len()) do?
- A. Groups df based on index strings
- B. Groups df based on length of each index value
- C. Data frames cannot be grouped by index values. Hence it results in Error.
- D. Groups df based on index values
Answer: C
Explanation:
Explanation
Data frames cannot be grouped by index values. Hence it results in Error.
NEW QUESTION # 30
Which of the following Snowflake parameter can be used to Automatically Suspend Tasks which are running Data science pipelines after specified Failed Runs?
- A. There is none as such available.
- B. SUSPEND_TASK_AUTO_NUM_FAILURES
- C. SUSPEND_TASK_AFTER_NUM_FAILURES
- D. SUSPEND_TASK
Answer: C
Explanation:
Explanation
Automatically Suspend Tasks After Failed Runs
Optionally suspend tasks automatically after a specified number of consecutive runs that either fail or time out.
This feature can reduce costs by suspending tasks that consume Snowflake credits but fail to run to completion. Failed task runs include runs in which the SQL code in the task body either produces a user error or times out. Task runs that are skipped, canceled, or that fail due to a sys-tem error are considered indeterminate and are not included in the count of failed task runs.
Set the SUSPEND_TASK_AFTER_NUM_FAILURES = num parameter on a standalone task or the root task in a DAG. When the parameter is set to a value greater than 0, the following behavior applies to runs of the standalone task or DAG:
Standalone tasks are automatically suspended after the specified number of consecutive task runs either fail or time out.
The root task is automatically suspended after the run of any single task in a DAG fails or times out the specified number of times in consecutive runs.
The parameter can be set when creating a task (using CREATE TASK) or later (using ALTER TASK). The setting applies to tasks that rely on either Snowflake-managed compute resources (i.e. serverless compute model) or user-managed compute resources (i.e. a virtual warehouse).
The SUSPEND_TASK_AFTER_NUM_FAILURES parameter can also be set at the account, database, or schema level. The setting applies to all standalone or root tasks contained in the modified object. Note that explicitly setting the parameter at a lower (i.e. more granular) level overrides the parameter value set at a higher level.
NEW QUESTION # 31
Which is the visual depiction of data through the use of graphs, plots, and informational graphics?
- A. Data Virtualization
- B. Data Mining
- C. Data visualization
- D. Data Interpretation
Answer: B
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 # 32
Which one is the incorrect option to share data in Snowflake?
- A. a Direct Share, in which you directly share specific database objects (a share) to anoth-er account in your region.
- B. a Direct Marketplace, in which you directly share specific database objects (a share) to another account in your region using Snowflake Marketplace.
- 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: B
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 # 33
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. Transpose()
- C. METADATA$ACTION ()
- 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 # 34
All aggregate functions except _____ ignore null values in their input collection
- A. Avg
- B. Count(*)
- C. Count(attribute)
- D. Sum
Answer: B
Explanation:
Explanation
Count(*)
* is used to select all values including null.
NEW QUESTION # 35
All Snowpark ML modeling and preprocessing classes are in the ________ namespace?
- A. snowflake.ml.modeling
- B. snowpark.ml.modeling
- C. snowflake.sklearn.modeling
- D. snowflake.scikit.modeling
Answer: A
Explanation:
Explanation
All Snowpark ML modeling and preprocessing classes are in the snowflake.ml.modeling namespace. The Snowpark ML modules have the same name as the corresponding module from the sklearn namespace. For example, the Snowpark ML module corresponding to sklearn.calibration is snow-flake.ml.modeling.calibration.
The xgboost and lightgbm modules correspond to snowflake.ml.modeling.xgboost and snow-flake.ml.modeling.lightgbm, respectively.
Not all of the classes from scikit-learn are supported in Snowpark ML.
NEW QUESTION # 36
The most widely used metrics and tools to assess a classification model are:
- A. Confusion matrix
- B. All of the above
- C. Cost-sensitive accuracy
- D. Area under the ROC curve
Answer: B
NEW QUESTION # 37
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