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DP-100 Day 2

Total questions: 10

Worksheet time: 6mins

Name
Class
Date
1.

You are using automated machine learning to train a model that predicts the species of an iris based on its petal and sepal measurements. Which kind of task should you specify for automated machine learning?

a)

Regression

b)

Classification

c)

Time Series Forecasting

2.

You want to use automated machine learning with car sales data to train a machine learning model that predicts the price of a car based on its make, model, engine size, and mileage.

What task type should you select?

a)

Regression

b)

Classification

c)

Time -Series Forecasting

3.

You are creating a training pipeline using a dataset that has multiple numeric columns. You want to transform the numeric columns so that the values are all on a similar scale.

Which module should you add to the pipeline?

a)

Select Columns in a Dataset

b)

Clean Missing Data

c)

Normalize Data

4.

AutoMl is used for which task types

a)

Regression

b)

Classification

c)

Time-Series Forecasting

d)

Clustering

5.

There are three main steps to run an automated machine learning experiment using the user interface

a)

Select Dataset

b)

Configure run

c)

Split Dataset

d)

Task type and Setting

6.

Which evaluation Metric you set for regression task type automated machine learning experiment

a)

Coefficient of determination

b)

Precision

c)

Recall

d)

AUC

7.

Which evaluation Metric you set for Classification task type automated machine learning experiment (You may select multiple)

a)

RMSE

b)

R Squared

c)

AUC

d)

F1 Score

8.

In.......................................................you define a dataflow for training a machine learning model as a pipeline.

a)

AutoML

b)

Azure CLI

c)

Designer

9.

Training Pipeline execute Train Score and ...................... step in sequence

a)

Split Dataset

b)

Select Algorithm

c)

Evaluate

d)

Output

10.

For real-time or batch-prediction which pipeline is used

a)

Inference

b)

Training

c)

Deployment

d)

None