Microsoft DP-100 Training Questions

Set up an Azure Machine Learning workspace (30-35%)

Run experiments and train models (25-30%)

Optimize and manage models (20-25%)

Deploy and consume models (20-25%)

1.You create a multi-class image classification deep learning model that uses a set of labeled images. You create a script file named train.py that uses the PyTorch 1.3 framework to train the model.You must run the script by using an estimator. The code must not require any additional Python libraries to be installed in the environment for the estimator. The time required for model training must be minimized.You need to define the estimator that will be used to run the script.Which estimator type should you use?

A. TensorFlow

B. PyTorch

C. SKLearn

D. Estimator

Answer: B

2.You are building a binary classification model by using a supplied training set.The training set is imbalanced between two classes.You need to resolve the data imbalance.What are three possible ways to achieve this goal? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point.

A. Penalize the classification

B. Resample the dataset using undersampling or oversampling

C. Normalize the training feature set

D. Generate synthetic samples in the minority class

E. Use accuracy as the evaluation metric of the model

Answer: ABD

3.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. Recommender Split

B. Regular Expression Split

C. Relative Expression Split

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

Answer: D

4.You are a data scientist building a deep convolutional neural network (CNN) for image classification.The CNN model you build shows signs of overfitting.You need to reduce overfitting and converge the model to an optimal fit.Which two actions should you perform? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point.

A. Add an additional dense layer with 512 input units.

B. Add L1/L2 regularization.

C. Use training data augmentation.

D. Reduce the amount of training data.

E. Add an additional dense layer with 64 input units.

Answer: BD

5.You are performing clustering by using the K-means algorithm. You need to define the possible termination conditions.Which three conditions can you use? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point.

A. Centroids do not change between iterations.

B. The residual sum of squares (RSS) rises above a threshold.

C. The residual sum of squares (RSS) falls below a threshold.

D. A fixed number of iterations is executed.

E. The sum of distances between centroids reaches a maximum.

Answer: ACD