Azure Data Scientist Associate (DP-100) Exam Dumps & Questions 2025

Azure Data Scientist Associate (DP-100) Exam Dumps & Questions 2025

Azure Data Scientist Associate (DP-100) Exam Questions 2025 Contains 850+ exam questions to pass the exam in first attempt.

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Below are the free 10 sample questions.

Question 1:

This question is included in a number of questions that depicts the identical set-up. However, every question has a distinctive result. Establish if the recommendation satisfies the requirements.

You have been tasked with evaluating your model on a partial data sample via k-fold cross-validation.

You have already configured a k parameter as the number of splits. You now have to configure the k parameter for the cross-validation with the usual value choice.

Recommendation: You configure the use of the value k=3.

Will the requirements be satisfied?

A.Yes

B. No


Answer: B

Explanation:

No

The recommendation to use k = 3 in k-fold cross-validation is a common practice, but it does not necessarily satisfy all requirements in every scenario. The choice of k should be based on the specific task and data characteristics rather than a fixed recommendation.

Key Considerations:

✔ Small Data Samples: Using k = 3 may not provide enough training data for the model, leading to high variance in evaluation metrics. In such cases, a larger k (e.g., k = 5 or 10) may be more appropriate.

✔ Large Data Samples: When dealing with large datasets, using k = 3 might result in low bias but high variance. In such cases, a smaller k may be preferable to reduce computational cost while maintaining robust evaluation.

Question 2:

You use an Azure Machine Learning workspace.

You have a trained model that must be deployed as a web service. Users must authenticate by using Azure Active Directory.

What should you do?

A. Deploy the model to Azure Kubernetes Service (AKS). During deployment, set the auth.enabled parameter of the target configuration object to true

B. Deploy the model to Azure Kubernetes Service (AKS). During deployment, set the token_auth_enabled parameter of the target configuration object to true

C. Deploy the model to Azure Container Instances. During deployment, set the auth_enabled parameter of the target configuration object to true

D. Deploy the model to Azure Container Instances. During deployment, set the token_auth_enabled parameter of the target configuration object to true

Answer: B

Explanation:

Controlling Token Authentication in Azure Machine Learning Deployments

To manage token authentication, use the token_auth_enabled parameter when creating or updating a deployment.

Key Considerations:

✔ Default Behavior: Token authentication is disabled by default when deploying to Azure Kubernetes Service (AKS).

✔ Authentication Methods for Model Deployments:

Key-based authentication: Uses a static key to authenticate the web service.

Token-based authentication: Requires obtaining a temporary token from the Azure Machine Learning workspace via Azure Active Directory (AAD) to authenticate.

Incorrect Answer:

❌ C: Token authentication is not supported when deploying to Azure Container Instances (ACI).

🔗 Reference: Azure Machine Learning Authentication Guide :

    https://learn.microsoft.com/en-us/azure/machine-learning/how-to-authenticate-online-endpoint?view=azureml-api-2&tabs=azure-cli

Question 3:

You are creating a compute target to train a machine learning experiment.

The compute target must support automated machine learning, machine learning pipelines, and Azure Machine Learning designer training.

You need to configure the compute target.

Which option should you use?

A.Azure Batch

B. Remote VM

C. Azure HDInsight

D. Azure Machine Learning compute cluster

Answer: D

Explanation:

Azure Machine Learning compute cluster is the correct choice.

Here’s why:

Automated machine learning: It’s optimized for hyperparameter tuning and model selection.

Machine learning pipelines: It supports multi-step workflows and orchestration.

Azure Machine Learning designer training: It integrates seamlessly with the designer for model training.

While other options like Azure Batch and Azure HDInsight can be used for certain machine learning tasks, they don’t offer the same level of integration and optimization for Azure Machine Learning as a dedicated compute cluster. Remote VM is suitable for smaller scale or development environments, but not for the full range of requirements mentioned.

Therefore, an Azure Machine Learning compute cluster is the most suitable option for this scenario.

Question 4:

You manage an Azure Machine Learning workspace by using the Azure CLI ml extension v2.

You need to define a YAML schema to create a compute cluster.

Which schema should you use?

A. https://azuremlschemas.azureedge.net/latest/kubernetesCompute.schema.json

B. https://azuremlschemas.azureedge.net/latest/computeInstance.schema.json

C. https://azuremlschemas.azureedge.net/latest/amlCompute.schema.json

D. https://azuremlschemas.azureedge.net/latest/vmCompute.schema.json

Answer: C

Explanation:

The correct schema for defining a compute cluster in Azure Machine Learning workspace using the Azure CLI ml extension v2 is:

https://azuremlschemas.azureedge.net/latest/amlCompute.schema.json

Here’s why:

KubernetesCompute.schema.json: This schema defines a Kubernetes compute target, which is not suitable for creating a traditional Azure Machine Learning compute cluster.

ComputeInstance.schema.json: This schema defines a compute target for a single virtual machine, whereas a compute cluster typically consists of multiple VMs.

VMCompute.schema.json: This schema is outdated and has been superseded by the amlCompute.schema.json.

amlCompute.schema.json: This schema is the most recent and widely used for defining Azure Machine Learning compute clusters. It includes configurations for VM size, number of nodes, and other relevant settings.

Therefore, https://azuremlschemas.azureedge.net/latest/amlCompute.schema.json is the appropriate choice for your scenario.

Question 5:

You use the Azure Machine Learning service to create a tabular dataset named training_data. You plan to use this dataset in a training script.

You create a variable that references the dataset using the following code:

training_ds = workspace.datasets.get(“training_data“)

You define an estimator to run the script.

You need to set the correct property of the estimator to ensure that your script can access the training_data dataset.

Which property should you set?

A. script_params = {“--training_ds“:training_ds}

B. environment_definition = {“training_data“:training_ds}

C. source_directory = training_ds

D. inputs = [training_ds.as_named_input(‘training_ds‘)]

Answer: D

Explanation:

Answer is inputs = [training_ds.as_named_input(‘training_ds‘)]

Example:

# Get the training dataset

diabetes_ds = ws.datasets.get(“Diabetes Dataset“)

# Create an estimator that uses the remote compute

hyper_estimator = SKLearn(source_directory=experiment_folder, inputs=[diabetes_ds.as_named_input(‘diabetes‘)],

# Pass the dataset as an input

compute_target = cpu_cluster, conda_packages=[‘pandas‘,‘ipykernel‘,‘matplotlib‘], pip_packages=[‘azureml-sdk‘,‘argparse‘,‘pyarrow‘], entry_script=‘diabetes_training.py‘)

Reference:

https://notebooks.azure.com/GraemeMalcolm/projects/azureml-primers/html/04%20-%20Optimizing%20Model%20Training.ipynb

• For a full set of 880 questions. Go to

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• SkillCertPro offers detailed explanations to each question which helps to understand the concepts better.

• It is recommended to score above 85% in SkillCertPro exams before attempting a real exam.

• SkillCertPro updates exam questions every 2 weeks.

• You will get life time access and life time free updates  

• SkillCertPro assures 100% pass guarantee in first attempt.

Question 6:

You train and register a model in your Azure Machine Learning workspace.

You must publish a pipeline that enables client applications to use the model for batch inferencing. You must use a pipeline with a single

ParallelRunStep step that runs a Python inferencing script to get predictions from the input data.

You need to create the inferencing script for the ParallelRunStep pipeline step.

Which two functions should you include?

A.main()

B. run(mini_batch)

C. init()

D. score(mini_batch)

E. batch()

Answer: B and C

Explanation:

For a ParallelRunStep in Azure Machine Learning, you’ll need two essential functions in your Python script:

init(): This function is executed once at the beginning of the script. It’s used to initialize resources, load the model, and set up any necessary configurations.

run(mini_batch): This function is called repeatedly for each mini-batch of data. It takes a mini-batch as input, processes it using the loaded model, and returns the predictions.

Here’s a basic example of how these functions might look:

Python

import logging

import azureml.core

def init():

global model

model_path = os.path.join(os.getenv('AZUREML_MODEL_DIR'), 'model.pkl')

model = joblib.load(model_path)

def run(mini_batch):  

logging.info("Processing mini-batch...")

predictions = model.predict(mini_batch)

return predictions

The main() function is typically used for standalone script execution and isn’t necessary for a ParallelRunStep. The batch() function is not a standard function used in this context.

By defining init() and run(), you provide the necessary structure for a ParallelRunStep to efficiently process data in batches, ensuring optimal performance and scalability.

Reference:

https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/machine-learning-pipelines/parallel-run

Question 7:

You are with a time series dataset in Azure Machine Learning Studio.

You need to split your dataset into training and testing subsets by using the Split Data module.

Which splitting mode should you use?

A. Split Rows with the Randomized split parameter set to true

B. Relative Expression Split

C. Recommender Split

D. Regular Expression Split

Answer: B

Explanation:

For splitting a time series dataset in Azure Machine Learning Studio to create training and testing subsets, the most suitable option is:

Relative Expression Split

Here’s why:

Time Series Data: Time series data has inherent sequential relationships. Random splitting (Split Rows with Randomized split parameter set to true) can disrupt these relationships, leading to inaccurate model performance.

Recommender Split: This is typically used for recommender system tasks and might not be appropriate for general time series splitting.

Regular Expression Split: While useful for splitting based on specific patterns, it may not be ideal for time series data unless you have a specific pattern to target for the split.

Relative Expression Split: This option allows you to define a condition based on a date or time column. You can set a threshold on the date/time to separate the data into training and testing sets. This approach preserves the sequential nature of your time series data.

Here’s an example of how to use Relative Expression Split with a date column named “Date”:

Splitting Expression: Date < '2024-11-01'

This expression will split the data such that rows with dates before November 1st, 2024, are allocated to the training set, while rows with dates on or after November 1st, 2024, are sent to the testing set.

Therefore, Relative Expression Split provides a more robust and time series-aware approach for splitting your dataset in Azure Machine Learning Studio.

References:

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

Question 8:

You are creating a new Azure Machine Learning pipeline using the designer.

The pipeline must train a model using data in a comma-separated values (CSV) file that is published on a website. You have not created a dataset for this file.

You need to ingest the data from the CSV file into the designer pipeline using the minimal administrative effort.

Which module should you add to the pipeline in Designer?

A. Enter Data Manually

B. Convert to CSV

C. Import Data

D. Dataset

Answer: C

Explanation:

Import Data is the correct module to use in this scenario.

Here’s why:

Direct Data Ingestion: The Import Data module allows you to directly ingest data from various sources, including web URLs.

Flexible Data Formats: It supports various data formats, including CSV, which is ideal for the given scenario.

Easy Configuration: You can simply provide the URL of the CSV file as the data source, and the module will automatically fetch and process the data.

By using the Import Data module, you can efficiently ingest the data from the website without the need for creating a separate dataset. This streamlined approach minimizes administrative effort and allows you to focus on building and training your machine learning model.

Reference:

https://docs.microsoft.com/en-us/azure/machine-learning/how-to-create-your-first-pipeline

Question 9:

This question is included in a number of questions that depicts the identical set-up. However, every question has a distinctive result. Establish if the recommendation satisfies the requirements.

You are in the process of carrying out feature engineering on a dataset.

You want to add a feature to the dataset and fill the column value.

Recommendation: You must make use of the Group Categorical Values Azure Machine Learning Studio module.

Will the requirements be satisfied?

A. Yes

B. No

Answer: B

Explanation:

B. No

The Group Categorical Values module in Azure Machine Learning Studio is specifically designed to group categorical data, not to add new features or fill column values. Its primary function is to aggregate data based on categorical columns.

To add a new feature and fill its values, you would typically use modules like:

Add Columns: To add a new column to the dataset.

Derived Column: To create a new column based on calculations or transformations of existing columns.

Fill Missing Values: To fill missing values in a specific column.

Therefore, using the Group Categorical Values module would not fulfill the requirement of adding a new feature and filling its column value.

Question 10:

You make use of Azure Machine Learning Studio to develop a linear regression model. You perform an experiment to assess various algorithms.

Which of the following is an algorithm that reduces the variances between actual and predicted values?

A. Poisson Regression

B. Boosted Decision Tree Regression

C. Linear Regression

D. Fast Forest Quantile Regression

Answer: C

Explanation:

Linear Regression is the algorithm that reduces the variance between actual and predicted values.

Here’s a breakdown of how each algorithm works:

Linear Regression:

Fits a linear equation to the data, aiming to minimize the sum of squared errors (SSE).

This directly addresses the variance between predicted and actual values.

Boosted Decision Tree Regression:

Combines multiple decision trees to make predictions.

While effective for complex relationships, it might not directly minimize the variance. It focuses on reducing bias and improving overall model accuracy.

Fast Forest Quantile Regression:

Used for quantile regression, which estimates conditional quantiles of the response variable.

It’s not specifically designed to minimize variance but rather to estimate quantiles.

Poisson Regression:

Used for count data, where the dependent variable represents the number of occurrences of an event.

It’s not directly related to minimizing variance between predicted and actual values.

Therefore, Linear Regression is the most suitable algorithm for reducing the variance between actual and predicted values in this scenario.

Reference:

https://docs.microsoft.com/en-us/azure/machine-learning/algorithm-module-reference/boosted-decision-tree-regression

https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/evaluate-model

https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/linear-regression

• For a full set of 880 questions. Go to

https://skillcertpro.com/product/azure-data-scientist-associate-dp-100-practice-exam-set/

• SkillCertPro offers detailed explanations to each question which helps to understand the concepts better.

• It is recommended to score above 85% in SkillCertPro exams before attempting a real exam.

• SkillCertPro updates exam questions every 2 weeks.

• You will get life time access and life time free updates 

• SkillCertPro assures 100% pass guarantee in first attempt.