Snowflake DSA-C02 Deluxe Study Guide with Online Test Engine
DSA-C02 dumps review - Professional Quiz Study Materials
NEW QUESTION # 36
To return the contents of a DataFrame as a Pandas DataFrame, Which of the following method can be used in SnowPark API?
- A. TO_PANDAS
- B. CONVERT_TO_PANDAS
- C. SNOWPARK_TO_PANDAS
- D. REPLACE_TO_PANDAS
Answer: A
Explanation:
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()
NEW QUESTION # 37
Which of the following cross validation versions is suitable quicker cross-validation for very large datasets with hundreds of thousands of samples?
- A. All of the above
- B. Holdout method
- C. k-fold cross-validation
- D. Leave-one-out cross-validation
Answer: B
Explanation:
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.
NEW QUESTION # 38
What Can Snowflake Data Scientist do in the Snowflake Marketplace as Consumer?
- A. Receive frictionless access to raw data products from vendors.
- B. Discover and test third-party data sources.
- C. Use the business intelligence (BI)/ML/Deep learning tools of her choice.
- 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 # 39
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. Retrained your training dataset after correcting data outliers & no need to introduce new data.
- C. 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.
- D. Create a new version of the dataset using only the new data and retrain the model.
Answer: C
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 # 40
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 Listing, in which you offer a share and additional metadata as a data product to one or more accounts.
- D. a Data Exchange, in which you set up and manage a group of accounts and offer a share to that group.
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 # 41
How do you handle missing or corrupted data in a dataset?
- A. Assign a unique category to missing values
- B. Drop missing rows or columns
- C. Replace missing values with mean/median/mode
- D. All of the above
Answer: D
NEW QUESTION # 42
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. RUN ROOT TASK
- B. CALL TASK
- C. RUN TASK
- D. EXECUTE TASK
Answer: D
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 # 43
What Can Snowflake Data Scientist do in the Snowflake Marketplace as Provider?
- A. Share live datasets securely and in real-time without creating copies of the data or im-posing data integration tasks on the consumer.
- B. Eliminate the costs of building and maintaining APIs and data pipelines to deliver data to customers.
- C. Publish listings for datasets that can be customized for the consumer.
- D. Publish listings for free-to-use datasets to generate interest and new opportunities among the Snowflake customer base.
Answer: A,B,C,D
Explanation:
Explanation
All are correct!
About the Snowflake Marketplace
You can use the Snowflake Marketplace to discover and access third-party data and services, as well as market your own data products across the Snowflake Data Cloud.
As a data provider, you can use listings on the Snowflake Marketplace to share curated data offer-ings with many consumers simultaneously, rather than maintain sharing relationships with each indi-vidual consumer.
With Paid Listings, you can also charge for your data products.
As a consumer, you might use the data provided on the Snowflake Marketplace to explore and ac-cess the following:
Historical data for research, forecasting, and machine learning.
Up-to-date streaming data, such as current weather and traffic conditions.
Specialized identity data for understanding subscribers and audience targets.
New insights from unexpected sources of data.
The Snowflake Marketplace is available globally to all non-VPS Snowflake accounts hosted on Amazon Web Services, Google Cloud Platform, and Microsoft Azure, with the exception of Mi-crosoft Azure Government.
Support for Microsoft Azure Government is planned.
NEW QUESTION # 44
Which of the Following is not type of Windows function in Snowflake?
- A. Aggregation window functions.
- B. Association functions.
- C. Rank-related functions.
- D. Window frame functions.
Answer: A,B
Explanation:
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.
NEW QUESTION # 45
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 Sum of column A values
- B. Computes length of column A and Sum of Column B values of each group
- C. Computes length of column A and Sum of Column B values
- D. Computes length of column A
Answer: B
Explanation:
Explanation
Computes length of column A and Sum of Column B values of each group
NEW QUESTION # 46
Which ones are the correct rules while using a data science model created via External function in Snowflake?
- A. External functions return a value. The returned value can be a compound value, such as a VARIANT that contains JSON.
- B. An external function can appear in any clause of a SQL statement in which other types of UDF can appear.
- C. External functions can be overloaded.
- D. External functions can accept Model parameters.
Answer: A,B,C,D
Explanation:
Explanation
From the perspective of a user running a SQL statement, an external function behaves like any other UDF .
External functions follow these rules:
External functions return a value.
External functions can accept parameters.
An external function can appear in any clause of a SQL statement in which other types of UDF can appear. For example:
1.select my_external_function_2(column_1, column_2)
2.from table_1;
1.select col1
2.from table_1
3.where my_external_function_3(col2) < 0;
1.create view view1 (col1) as
2.select my_external_function_5(col1)
3.from table9;
An external function can be part of a more complex expression:
1.select upper(zipcode_to_city_external_function(zipcode))
2.from address_table;
The returned value can be a compound value, such as a VARIANT that contains JSON.
External functions can be overloaded; two different functions can have the same name but different signatures (different numbers or data types of input parameters).
NEW QUESTION # 47
Which metric is not used for evaluating classification models?
- A. Recall
- B. Precision
- C. Accuracy
- D. Mean absolute error
Answer: D
Explanation:
Explanation
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))).
Root Mean Squared Error (RMSE)and Mean Absolute Error (MAE) are metrics used to evaluate a Regression Model. These metrics tell us how accurate our predictions are and, what is the amount of deviation from the actual values.
NEW QUESTION # 48
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. METADATA$ACTION ()
- B. Transpose()
- C. He could not apply Transformation on Stream table data.
- D. lateral flatten()
Answer: D
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 # 49
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. Streamlit
- B. StreamBI
- C. Streamsets
- D. Rapter
Answer: A
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 # 50
Skewness of Normal distribution is ___________
- A. Negative
- B. Positive
- C. 0
- D. Undefined
Answer: C
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 # 51
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 values
- B. Data frames cannot be grouped by index values. Hence it results in Error.
- C. Groups df based on length of each index value
- D. Groups df based on index strings
Answer: B
Explanation:
Explanation
Data frames cannot be grouped by index values. Hence it results in Error.
NEW QUESTION # 52
Select the Data Science Tools which are known to provide native connectivity to Snowflake?
- A. DvSUM
- B. HEX
- C. DiYotta
- D. Denodo
Answer: B
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 # 53
Which type of Machine learning Data Scientist generally used for solving classification and regression problems?
- A. Instructor Learning
- B. Supervised
- C. Unsupervised
- D. Regression Learning
- E. Reinforcement 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 # 54
Which of the following is a useful tool for gaining insights into the relationship between features and predictions?
- A. Partial dependence plots(PDP)
- B. FULL dependence plots (FDP)
- C. numpy plots
- D. sklearn plots
Answer: A
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 # 55
Which of the following Snowflake parameter can be used to Automatically Suspend Tasks which are running Data science pipelines after specified Failed Runs?
- A. SUSPEND_TASK_AUTO_NUM_FAILURES
- B. There is none as such available.
- 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 # 56
A Data Scientist as data providers require to allow consumers to access all databases and database objects in a share by granting a single privilege on shared databases. Which one is incorrect SnowSQL command used by her while doing this task?
Assuming:
A database named product_db exists with a schema named product_agg and a table named Item_agg.
The database, schema, and table will be shared with two accounts named xy12345 and yz23456.
1.USE ROLE accountadmin;
2.CREATE DIRECT SHARE product_s;
3.GRANT USAGE ON DATABASE product_db TO SHARE product_s;
4.GRANT USAGE ON SCHEMA product_db. product_agg TO SHARE product_s;
5.GRANT SELECT ON TABLE sales_db. product_agg.Item_agg TO SHARE product_s;
6.SHOW GRANTS TO SHARE product_s;
7.ALTER SHARE product_s ADD ACCOUNTS=xy12345, yz23456;
8.SHOW GRANTS OF SHARE product_s;
- A. GRANT USAGE ON DATABASE product_db TO SHARE product_s;
- B. GRANT SELECT ON TABLE sales_db. product_agg.Item_agg TO SHARE product_s;
- C. CREATE DIRECT SHARE product_s;
- D. ALTER SHARE product_s ADD ACCOUNTS=xy12345, yz23456;
Answer: B
Explanation:
Explanation
CREATE SHARE product_s is the correct Snowsql command to create Share object.
Rest are correct ones.
https://docs.snowflake.com/en/user-guide/data-sharing-provider#creating-a-share-using-sql
NEW QUESTION # 57
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$DELETE
- B. METADATA$FILE_ID
- C. METADATA$ROW_ID
- D. METADATA$ISUPDATE
- E. METADATA$ACTION
Answer: C,D,E
Explanation:
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.
NEW QUESTION # 58
......
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