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DP-100 Practice Questions

Question # 1
You create a Python script that runs a training experiment in Azure Machine Learning. The script uses the Azure Machine Learning SDK for Python. You must add a statement that retrieves the names of the logs and outputs generated by the script. You need to reference a Python class object from the SDK for the statement. Which class object should you use?
A. Run
B. ScripcRunConfig
C. Workspace
D. Experiment


A. Run

Explanation:

A run represents a single trial of an experiment. Runs are used to monitor the asynchronous execution of a trial, log metrics and store output of the trial, and to analyze results and access artifacts generated by the trial.

The run Class get_all_logs method downloads all logs for the run to a directory.

[Reference:, https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.run(class), , ]

Question # 2
You need to implement a feature engineering strategy for the crowd sentiment local models. What should you do?
A. Apply an analysis of variance (ANOVA).
B. Apply a Pearson correlation coefficient.
C. Apply a Spearman correlation coefficient.
D. Apply a linear discriminant analysis.


D. Apply a linear discriminant analysis.

Explanation:

The linear discriminant analysis method works only on continuous variables, not categorical or ordinal variables.

Linear discriminant analysis is similar to analysis of variance (ANOVA) in that it works by comparing the means of the variables.

Scenario:

Data scientists must build notebooks in a local environment using automatic feature engineering and model building in machine learning pipelines.

Experiments for local crowd sentiment models must combine local penalty detection data.

All shared features for local models are continuous variables.


Question # 3
You manage an Azure Machine learning workspace. The workspace includes an Azure Machine Learning kubernetes compute target configured as an Azure Kubemetes Service (AKS) cluster named AKS1 AKS1 is configured to enable the targeting of different nodes to train workloads.

You must run a command job on AK51 by using the Azure ML Python SDK v2? The command job must select different types of compute nodes. The compare node types must be specified by using a command parameter.

You need to configure the command parameter.

Which parameter should you use?

A. compute
B. environment
C. instance_type
D. limits
Explanation:

from azure.ai.ml import command

# define the command

command_job = command(

command="python -c "print('Hello world!')"",

environment="AzureML-lightgbm-3.2-ubuntu18.04-py37-cpu@latest",

compute="",

instance_type=""


Question # 4
You arc creating a new experiment in Azure Machine Learning Studio. You have a small dataset that has missing values in many columns. The data does not require the application of predictors for each column. You plan to use the Clean Missing Data module to handle the missing data. You need to select a data cleaning method. Which method should you use?
A. Synthetic Minority
B. Replace using Probabilistic PAC
C. Replace using MICE
D. Normalization


B. Replace using Probabilistic PAC


Question # 5
You are implementing hyperparameter tuning by using Bayesian sampling for an Azure ML Python SDK v2-based model training from a notebook. The notebook is in an Azure Machine Learning workspace. The notebook uses a training script that runs on a compute cluster with 20 nodes.

The code implements Bandit termination policy with slack_factor set to 02 and a sweep job with max_concurrent_trials set to 10.

You must increase effectiveness of the tuning process by improving sampling convergence. You need to select which sampling convergence to use. What should you select?

A. Set the value of slack. factor of earty. termination policy to 0.1.
B. Set the value of max_concurrent_trials to 4.
C. Set the value of slack_factor of eartyjermination policy to 0.9.
D. Set the value of max. concurrentjrials to 20.


B. Set the value of max_concurrent_trials to 4.


Question # 6
You train and publish a machine teaming model. You need to run a pipeline that retrains the model based on a trigger from an external system. What should you configure?
A. Azure Data Catalog
B. Azure Batch
C. Azure logic App


C. Azure logic App


Question # 7
You use Azure Machine Learning to tram a model. You must use Baylean sampling to Tune hyperparaters. You need to select a learning_rate parameter distribution. Which two distributions can you use? Each correct answer presents a complete solution. NOTE Each correct selection is worth one point.
A. Normal
B. Uniform
C. Choice
D. LogUniform
E. QNormal


B. Uniform
C. Choice


Question # 8
You need to implement a model development strategy to determine a user’s tendency to respond to an ad. Which technique should you use?
A. Use a Relative Expression Split module to partition the data based on centroid distance.
B. Use a Relative Expression Split module to partition the data based on distance travelled to the event.
C. Use a Split Rows module to partition the data based on distance travelled to the event.
D. Use a Split Rows module to partition the data based on centroid distance.


A. Use a Relative Expression Split module to partition the data based on centroid distance.

Explanation:

Split Data partitions the rows of a dataset into two distinct sets.

The Relative Expression Split option in the Split Data module of Azure Machine Learning Studio is helpful when you need to divide a dataset into training and testing datasets using a numerical expression.

Relative Expression Split: Use this option whenever you want to apply a condition to a number column. The number could be a date/time field, a column containing age or dollar amounts, or even a percentage. For example, you might want to divide your data set depending on the cost of the items, group people by age ranges, or separate data by a calendar date.

Scenario:

Local market segmentation models will be applied before determining a user’s propensity to respond to an advertisement.

The distribution of features across training and production data are not consistent

References:

https://docs.microsoft.co m/en-us/azure/machine-learning/studio-module-reference/split-data


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Exam Name: Designing and Implementing a Data Science Solution on Azure Exam
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