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DP-100 Exam For Microsoft Certified: Azure Data Scientist Associate Certification
Exam DP-100: Designing and Implementing a Data Science Solution on Azure is for candidates who wish to become Azure Data Scientist Associate. So, one must qualify the DP-100 exam to acquire this certification. DP-100 exam duration is 210 minutes. But candidates are allowed only 180 minutes to answer the questions. The left 30 minutes are only for reading instructions, signing the non-disclosure agreement, and giving feedback. The DP-100 Exam is only available in four languages i.e. English, Japanese, simplified Chinese and lastly Korean.
DP-100 Exam Details
Exam Name: DP-100: Designing and Implementing an Azure Data Solution
Technology: Microsoft Azure
Prerequisites: None
Registration Fee: 165 USD (without taxes)
Total Questions: 40-60 Questions
Exam Language: English, Korean, Chinese (simplified), Japanese
Learning Objectives
The content of this exam was updated on May 20, 2021. Please download the exam skills outline below to see what changed.
Manage Azure resources for machine learning (25–30%)
Run experiments and train models (20–25%)
Deploy and operationalize machine learning solutions (35–40%)
Implement responsible machine learning (5–10%)
Share Update Microsoft Azure Data Scientist Associate DP-100 Sample Questions
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.
Answer: A
You need to resolve the local machine learning pipeline performance issue.
What should you do?
A. Increase Graphic Processing Units (GPUs).
B. Increase the learning rate.
C. Increase the training iterations,
D. Increase Central Processing Units (CPUs).
Answer: A
You need to select an environment that will meet the business and data requirements.
Which environment should you use?
A. Azure HDInsight with Spark MLlib
B. Azure Cognitive Services
C. Azure Machine Learning Studio
D. Microsoft Machine Learning Server
Answer: D
You need to implement a scaling strategy for the local penalty detection data.
Which normalization type should you use?
A. Streaming
B. Weight
C. Batch
D. Cosine
Answer: C
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.
Answer: D
You need to select a feature extraction method.
Which method should you use?
A. Spearman correlation
B. Mutual information
C. Mann-Whitney test
D. Pearson’s correlation
Answer: A
You need to select a feature extraction method.
Which method should you use?
A. Mutual information
B. Mood’s median test
C. Kendall correlation
D. Permutation Feature Importance
Answer: C
You train and register a machine learning model. You create a batch inference pipeline that uses the model to generate predictions from multiple data files.
You must publish the batch inference pipeline as a service that can be scheduled to run every night.
You need to select an appropriate compute target for the inference service.
Which compute target should you use?
A. Azure Machine Learning compute instance
B. Azure Machine Learning compute cluster
C. Azure Kubernetes Service (AKS)-based inference cluster
D. Azure Container Instance (ACI) compute target
Answer: B
You create and register a model in an Azure Machine Learning workspace.
You must use the Azure Machine Learning SDK to implement a batch inference pipeline that uses a ParallelRunStep to score input data using the model. You must specify a value for the ParallelRunConfig compute_target setting of the pipeline step.
You need to create the compute target.
Which class should you use?
A. BatchCompute
B. AdlaCompute
C. AmlCompute
D. Aks Compute
Answer: C
You use the Azure Machine Learning Python SDK to define a pipeline to train a model.
The data used to train the model is read from a folder in a datastore.
You need to ensure the pipeline runs automatically whenever the data in the folder changes.
What should you do?
A. Set the regenerate_outputs property of the pipeline to True
B. Create a ScheduleRecurrance object with a Frequency of auto. Use the object to create a Schedule for the pipeline
C. Create a PipelineParameter with a default value that references the location where the training data is stored
D. Create a Schedule for the pipeline. Specify the datastore in the datastore property, and the folder containing the training data in the path_on_datascore property
Answer: D
