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The DP-100 exam covers various topics related to data science, including designing and implementing data ingestion and processing solutions, designing and implementing machine learning models, designing and implementing data storage solutions, and designing and implementing data visualization solutions. DP-100 exam also evaluates the candidate's knowledge of Azure services such as Azure Machine Learning, Azure Databricks, Azure Stream Analytics, and Azure Data Factory.

Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q151-Q156):

NEW QUESTION # 151
You collect data from a nearby weather station. You have a pandas dataframe named weather_df that includes the following data:

The data is collected every 12 hours: noon and midnight.
You plan to use automated machine learning to create a time-series model that predicts temperature over the next seven days. For the initial round of training, you want to train a maximum of 50 different models.
You must use the Azure Machine Learning SDK to run an automated machine learning experiment to train these models.
You need to configure the automated machine learning run.
How should you complete the AutoMLConfig definition? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation

Box 1: forcasting
Task: The type of task to run. Values can be 'classification', 'regression', or 'forecasting' depending on the type of automated ML problem to solve.
Box 2: temperature
The training data to be used within the experiment. It should contain both training features and a label column (optionally a sample weights column).
Box 3: observation_time
time_column_name: The name of the time column. This parameter is required when forecasting to specify the datetime column in the input data used for building the time series and inferring its frequency. This setting is being deprecated. Please use forecasting_parameters instead.
Box 4: 7
"predicts temperature over the next seven days"
max_horizon: The desired maximum forecast horizon in units of time-series frequency. The default value is 1.
Units are based on the time interval of your training data, e.g., monthly, weekly that the forecaster should predict out. When task type is forecasting, this parameter is required.
Box 5: 50
"For the initial round of training, you want to train a maximum of 50 different models." Iterations: The total number of different algorithm and parameter combinations to test during an automated ML experiment.
Reference:
https://docs.microsoft.com/en-us/python/api/azureml-train-automl-client/azureml.train.automl.automlconfig.auto

NEW QUESTION # 152
YOU have a data-set that contains over 150 features. You use the dataset to train a Support Vector Machine (SVM) binary classifirer.
You need to use the Permutation Feature Importance module in Azure Machine Learning Studio to compute a set of feature importance scores for the dataset.
In which order should you perform the actions? To answer move al actions from from the list of Actions to the answer area and arrange them in the correct order.

Answer:

Explanation:

NEW QUESTION # 153
You plan to explore demographic data for home ownership in various cities. The data is in a CSV file with the following format:
age,city,income,home_owner
21,Chicago,50000,0
35,Seattle,120000,1
23,Seattle,65000,0
45,Seattle,130000,1
18,Chicago,48000,0
You need to run an experiment in your Azure Machine Learning workspace to explore the data and log the results. The experiment must log the following information:
the number of observations in the dataset
a box plot of income by home_owner
a dictionary containing the city names and the average income for each city You need to use the appropriate logging methods of the experiment's run object to log the required information.
How should you complete the code? To answer, drag the appropriate code segments to the correct locations.
Each code segment may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation

Box 1: log
The number of observations in the dataset.
run.log(name, value, description='')
Scalar values: Log a numerical or string value to the run with the given name. Logging a metric to a run causes that metric to be stored in the run record in the experiment. You can log the same metric multiple times within a run, the result being considered a vector of that metric.
Example: run.log("accuracy", 0.95)
Box 2: log_image
A box plot of income by home_owner.
log_image Log an image to the run record. Use log_image to log a .PNG image file or a matplotlib plot to the run. These images will be visible and comparable in the run record.
Example: run.log_image("ROC", plot=plt)
Box 3: log_table
A dictionary containing the city names and the average income for each city.
log_table: Log a dictionary object to the run with the given name.

NEW QUESTION # 154
You need to select a feature extraction method.
Which method should you use?

A. Spearman correlation
B. Pearson's correlation
C. Mutual information
D. Fisher Linear Discriminant Analysis

Answer: A

Explanation:
Spearman's rank correlation coefficient assesses how well the relationship between two variables can be described using a monotonic function.
Note: Both Spearman's and Kendall's can be formulated as special cases of a more general correlation coefficient, and they are both appropriate in this scenario.
Scenario: The MedianValue and AvgRoomsInHouse columns both hold data in numeric format. You need to select a feature selection algorithm to analyze the relationship between the two columns in more detail.
Incorrect Answers:
B: The Spearman correlation between two variables is equal to the Pearson correlation between the rank values of those two variables; while Pearson's correlation assesses linear relationships, Spearman's correlation assesses monotonic relationships (whether linear or not).
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/feature-selection-modules Perform Feature Engineering Question Set 3

NEW QUESTION # 155
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. limits
B. instance_type
C. compute
D. environment

Answer: B

Explanation:
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="<Kubernetes-compute_target_name>",
instance_type="<instance_type_name>"

NEW QUESTION # 156
......

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