Fast2test DSA-C02リアル試験問題解答は更新された[2023年11月28日]
お手軽に合格させる 最新Snowflake DSA-C02問題集には67問があります
質問 # 38
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. Add the new data to the existing dataset and enable Application Insights for the service where the model is deployed.
- B. Create a new version of the dataset using only the new data and retrain the model.
- C. Retrained your training dataset after correcting data outliers & no need to introduce new data.
- D. 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.
正解：D
解説：
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.
質問 # 39
You are training a binary classification model to support admission approval decisions for a college degree program.
How can you evaluate if the model is fair, and doesn't discriminate based on ethnicity?
- A. Evaluate each trained model with a validation datasetand use the model with the highest accuracy score.
- B. Remove the ethnicity feature from the training dataset.
- C. None of the above.
- D. Compare disparity between selection rates and performance metrics across ethnicities.
正解：D
解説：
Explanation
By using ethnicity as a sensitive field, and comparing disparity between selection rates and performance metrics for each ethnicity value, you can evaluate the fairness of the model.
質問 # 40
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. All of the above
- C. Holdout method
- D. Leave-one-out cross-validation
正解：C
解説：
Explanation
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.
質問 # 41
Which ones are the type of visualization used for Data exploration in Data Science?
- A. Feature Distribution by Class
- B. 2D-Density Plots
- C. Newton AI
- D. Sand Visualization
- E. Heat Maps
正解：B、D、E
解説：
Explanation
Type of visualization used for exploration:
Correlation heatmap
Class distributions by feature
Two-Dimensional density plots.
All the visualizations are interactive, as is standard for Plotly.
For More details, please refer the below link:
https://towardsdatascience.com/data-exploration-understanding-and-visualization-72657f5eac41
質問 # 42
Which one of the following is not the key component while designing External functions within Snowflake?
- A. UDF Service
- B. Remote Service
- C. Proxy Service
- D. API Integration
正解：A
解説：
Explanation
What is an External Function?
An external function calls code that is executed outside Snowflake.
The remotely executed code is known as a remote service.
Information sent to a remote service is usually relayed through a proxy service.
Snowflake stores security-related external function information in an API integration.
External Function:
An external function is a type of UDF. Unlike other UDFs, an external function does not contain its own code; instead, the external function calls code that is stored and executed outside Snowflake.
Inside Snowflake, the external function is stored as a database object that contains information that Snowflake uses to call the remote service. This stored information includes the URL of the proxy service that relays information to and from the remote service.
Remote Service:
The remotely executed code is known as a remote service.
The remote service must act like a function. For example, it must return a value.
Snowflake supports scalar external functions; the remote service must return exactly one row for each row received.
Proxy Service:
Snowflake does not call a remote service directly. Instead, Snowflake calls a proxy service, which relays the data to the remote service.
The proxy service can increase security by authenticating requests to the remote service.
The proxy service can support subscription-based billing for a remote service. For example, the proxy service can verify that a caller to the remote service is a paid subscriber.
The proxy service also relays the response from the remote service back to Snowflake.
Examples of proxy services include:
Amazon API Gateway.
Microsoft Azure API Management service.
API Integration:
An integration is a Snowflake object that provides an interface between Snowflake and third-party services.
An API integration stores information, such as security information, that is needed to work with a proxy service or remote service.
An API integration is created with the CREATE API INTEGRATION command.
Users can write and call their own remote services, or call remote services written by third parties. These remote services can be written using any HTTP server stack,including cloud serverless compute services such as AWS Lambda.
質問 # 43
Which of the Following is not type of Windows function in Snowflake?
- A. Association functions.
- B. Aggregation window functions.
- C. Rank-related functions.
- D. Window frame functions.
正解：A、B
解説：
Explanation
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.
質問 # 44
Which is the visual depiction of data through the use of graphs, plots, and informational graphics?
- A. Data Interpretation
- B. Data Mining
- C. Data Virtualization
- D. Data visualization
正解：B
解説：
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.
質問 # 45
What Can Snowflake Data Scientist do in the Snowflake Marketplace as Consumer?
- A. Discover and test third-party data sources.
- B. Combine new datasets with your existing data in Snowflake to derive new business in-sights.
- C. Receive frictionless access to raw data products from vendors.
- D. Use the business intelligence (BI)/ML/Deep learning tools of her choice.
正解：A、B、C、D
解説：
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.
質問 # 46
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. Streamlit
- C. Rapter
- D. Streamsets
正解：B
解説：
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.
質問 # 47
Which of the following Functions do Support Windowing?
- A. LISTAGG
- B. EXTRACT
- C. ENCRYPT
- D. HASH_AGG
正解：A
解説：
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> ] )
質問 # 48
Which are the following additional Metadata columns Stream contains that could be used for creating Efficient Data science Pipelines & helps in transforming only the New/Modified data only?
- A. METADATA$ROW_ID
- B. METADATA$ISUPDATE
- C. METADATA$ACTION
- D. METADATA$FILE_ID
- E. METADATA$DELETE
正解：A、B、C
解説：
Explanation
A stream stores an offset for the source object and not any actual table columns or data. When que-ried, a stream accesses and returns the historic data in the same shape as the source object (i.e. the same column names and ordering) with the following additional columns:
METADATA$ACTION
Indicates the DML operation (INSERT, DELETE) recorded.
METADATA$ISUPDATE
Indicates whether the operation was part of an UPDATE statement. Updates to rows in the source object are represented as a pair of DELETE and INSERT records inthe stream with a metadata column METADATA$ISUPDATE values set to TRUE.
Note that streams record the differences between two offsets. If a row is added and then updated in the current offset, the delta change is a new row. The METADATA$ISUPDATE row records a FALSE value.
METADATA$ROW_ID
Specifies the unique and immutable ID for the row, which can be used to track changes to specific rows over time.
質問 # 49
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. Returns the third column
- B. Filters the row labelled r3
- C. Results in Error
- D. Returns the row name r3
正解：B
解説：
Explanation
It will Filters the row labelled r3.
質問 # 50
To return the contents of a DataFrame as a Pandas DataFrame, Which of the following method can be used in SnowPark API?
- A. REPLACE_TO_PANDAS
- B. TO_PANDAS
- C. CONVERT_TO_PANDAS
- D. SNOWPARK_TO_PANDAS
正解：B
解説：
Explanation
To return the contents of a DataFrame as a Pandas DataFrame, use the to_pandas method.
For example:
1.>>> python_df = session.create_dataframe(["a", "b", "c"])
2.>>> pandas_df = python_df.to_pandas()
質問 # 51
Which of the following cross validation versions may not be suitable for very large datasets with hundreds of thousands of samples?
- A. Leave-one-out cross-validation
- B. k-fold cross-validation
- C. All of the above
- D. Holdout method
正解：A
解説：
Explanation
Leave-one-out cross-validation (LOO cross-validation) is not suitable for very large datasets due to the fact that this validation technique requires one model for every sample in the training set to be created and evaluated.
Cross validation
It is a technique to evaluate a machine learning model and it is the basis for whole class of model evaluation methods. The goal of cross-validation is to test the model's ability to predict new data that was not used in estimating it. It works by the idea of splitting dataset into number of subsets, keep a subset aside, train the model, and test the model on the holdout subset.
Leave-one-out cross validation
Leave-one-out cross validation is K-fold cross validation taken to its logical extreme, with K equal to N, the number of data points in the set. That means that N separate times, the function approximator is trained on all the data except for one point and a prediction is made for that point. As be-fore the average error is computed and used to evaluate the model. The evaluation given by leave-one-out cross validation is very expensive to compute at first pass.
質問 # 52
Mark the Incorrect statements regarding MIN / MAX Functions?
- A. NULL values are ignored unless all the records are NULL, in which case a NULL value is returned
- B. The data type of the returned value is the same as the data type of the input values
- C. 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
- D. NULL values are skipped unless all the records are NULL
正解：A
解説：
Explanation
NULL values are ignored unless all the records are NULL, in which case a NULL value is returned
質問 # 53
All Snowpark ML modeling and preprocessing classes are in the ________ namespace?
- A. snowflake.sklearn.modeling
- B. snowflake.ml.modeling
- C. snowflake.scikit.modeling
- D. snowpark.ml.modeling
正解：B
解説：
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.
質問 # 54
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 Visualization
- B. Data Virtualization
- C. Data Collection
- D. Data Profiling
正解：D
解説：
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.
質問 # 55
What is the formula for measuring skewness in a dataset?
- A. (MEAN - MODE)/ STANDARD DEVIATION
- B. MEAN - MEDIAN
- C. MODE - MEDIAN
- D. (3(MEAN - MEDIAN))/ STANDARD DEVIATION
正解：D
解説：
Explanation
Since the normal curve is symmetric about its mean, its skewness is zero. This is a theoretical expla-nation for mathematical proofs, you can refer to books or websites that speak on the same in detail.
質問 # 56
Which ones are the key actions in the data collection phase of Machine learning included?
- A. Probability
- B. Ingest and Aggregate
- C. Label
- D. Measure
正解：B、C
解説：
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.
質問 # 57
Mark the incorrect statement regarding Python UDF?
- A. A scalar function (UDF) returns a tabular value for each input row
- B. Python UDFs can contain both new code and calls to existing packages
- C. For each row passed to a UDF, the UDF returns either a scalar (i.e. single) value or, if defined as a table function, a set of rows.
- D. A UDF also gives you a way to encapsulate functionality so that you can call it repeatedly from multiple places in code
正解：A
解説：
Explanation
A scalar function (UDF) returns one output row for each input row. The returned row consists of a single column/value
質問 # 58
Which one is not the feature engineering techniques used in ML data science world?
- A. Statistical
- B. One hot encoding
- C. Imputation
- D. Binning
正解：A
解説：
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.
質問 # 59
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