December/2021 Latest Braindump2go DP-100 Exam Dumps with PDF and VCE Free Updated Today! Following are some new DP-100 Real Exam Questions!
QUESTION 264
You need to consider the underlined segment to establish whether it is accurate.
To improve the amount of low incidence cases in a dataset, you should make use of the SMOTE module.
Select “No adjustment required” if the underlined segment is accurate. If the underlined segment is inaccurate, select the accurate option.
A. No adjustment required.
B. Remove Duplicate Rows
C. Join Data
D. Edit Metadata
Answer: A
Explanation:
Use the SMOTE module in Azure Machine Learning Studio to increase the number of underrepresented cases in a dataset used for machine learning. SMOTE is a better way of increasing the number of rare cases than simply duplicating existing cases.
Reference: https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/smote
QUESTION 265
You are planning to host practical training to acquaint learners with data visualization creation using Python. Learner devices are able to connect to the internet.
Learner devices are currently NOT configured for Python development. Also, learners are unable to install software on their devices as they lack administrator permissions. Furthermore, they are unable to access Azure subscriptions.
It is imperative that learners are able to execute Python-based data visualization code.
Which of the following actions should you take?
A. You should consider configuring the use of Azure Container Instance.
B. You should consider configuring the use of Azure BatchAI.
C. You should consider configuring the use of Azure Notebooks.
D. You should consider configuring the use of Azure Kubernetes Service.
Answer: C
QUESTION 266
You have recently concluded the construction of a binary classification machine learning model.
You are currently assessing the model. You want to make use of a visualization that allows for precision to be used as the measurement for the assessment.
Which of the following actions should you take?
A. You should consider using Venn diagram visualization.
B. You should consider using Receiver Operating Characteristic (ROC) curve visualization.
C. You should consider using Box plot visualization.
D. You should consider using the Binary classification confusion matrix visualization.
Answer: D
Explanation:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-understand-automated-ml#confusion-matrix
QUESTION 267
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=1.
Will the requirements be satisfied?
A. Yes
B. No
Answer: B
QUESTION 268
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
QUESTION 269
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 Join Data Azure Machine Learning Studio module.
Will the requirements be satisfied?
A. Yes
B. No
Answer: B
QUESTION 270
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 Edit Metadata Azure Machine Learning Studio module.
Will the requirements be satisfied?
A. Yes
B. No
Answer: A
Explanation:
Typical metadata changes might include marking columns as features.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/edit-metadata
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/join-data
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/group-categorical-values
QUESTION 271
You have been tasked with ascertaining if two sets of data differ considerably. You will make use of Azure Machine Learning Studio to complete your task.
You plan to perform a paired t-test.
Which of the following are conditions that must apply to use a paired t-test? (Choose all that apply.)
A. All scores are independent from each other.
B. You have a matched pairs of scores.
C. The sampling distribution of d is normal.
D. The sampling distribution of x1- x2 is normal.
Answer: BC
Explanation:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/test-hypothesis-using-t-test
QUESTION 272
You want to train a classification model using data located in a comma-separated values (CSV) file.
The classification model will be trained via the Automated Machine Learning interface using the Classification task type.
You have been informed that only linear models need to be assessed by the Automated Machine Learning.
Which of the following actions should you take?
A. You should disable deep learning.
B. You should enable automatic featurization.
C. You should disable automatic featurization.
D. You should set the task type to Forecasting.
Answer: C
Explanation:
https://econml.azurewebsites.net/spec/estimation/dml.html
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-use-automated-ml-for-ml-models
QUESTION 273
You are preparing to train a regression model via automated machine learning. The data available to you has features with missing values, as well as categorical features with little discrete values.
You want to make sure that automated machine learning is configured as follows:
– missing values must be automatically imputed.
– categorical features must be encoded as part of the training task.
Which of the following actions should you take?
A. You should make use of the featurization parameter with the ‘auto’ value pair.
B. You should make use of the featurization parameter with the ‘off’ value pair.
C. You should make use of the featurization parameter with the ‘on’ value pair.
D. You should make use of the featurization parameter with the ‘FeaturizationConfig’ value pair.
Answer: A
Explanation:
Featurization str or FeaturizationConfig
Values: ‘auto’ / ‘off’ / FeaturizationConfig
Indicator for whether featurization step should be done automatically or not, or whether customized featurization should be used.
Column type is automatically detected. Based on the detected column type preprocessing/featurization is done as follows:
Categorical: Target encoding, one hot encoding, drop high cardinality categories, impute missing values.
Numeric: Impute missing values, cluster distance, weight of evidence.
DateTime: Several features such as day, seconds, minutes, hours etc.
Text: Bag of words, pre-trained Word embedding, text target encoding.
Reference:
https://docs.microsoft.com/en-us/python/api/azureml-train-automl-client/azureml.train.automl.automlconfig.automlconfig
QUESTION 274
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. Fast Forest Quantile Regression
B. Poisson Regression
C. Boosted Decision Tree Regression
D. Linear Regression
Answer: C
Explanation:
Mean absolute error (MAE) measures how close the predictions are to the actual outcomes; thus, a lower score is better.
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
QUESTION 275
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 constructing a machine learning model that translates language text into a different language text.
The machine learning model must be constructed and trained to learn the sequence of the.
Recommendation: You make use of Convolutional Neural Networks (CNNs).
Will the requirements be satisfied?
A. Yes
B. No
Answer: B
QUESTION 276
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 constructing a machine learning model that translates language text into a different language text.
The machine learning model must be constructed and trained to learn the sequence of the.
Recommendation: You make use of Generative Adversarial Networks (GANs).
Will the requirements be satisfied?
A. Yes
B. No
Answer: B
QUESTION 277
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 constructing a machine learning model that translates language text into a different language text.
The machine learning model must be constructed and trained to learn the sequence of the.
Recommendation: You make use of Recurrent Neural Networks (RNNs).
Will the requirements be satisfied?
A. Yes
B. No
Answer: A
Explanation:
Note: RNNs are designed to take sequences of text as inputs or return sequences of text as outputs, or both. They’re called recurrent because the network’s hidden layers have a loop in which the output and cell state from each time step become inputs at the next time step. This recurrence serves as a form of memory. It allows contextual information to flow through the network so that relevant outputs from previous time steps can be applied to network operations at the current time step.
Reference:
https://towardsdatascience.com/language-translation-with-rnns-d84d43b40571
QUESTION 278
You make use of Azure Machine Learning Studio to create a binary classification model.
You are preparing to carry out a parameter sweep of the model to tune hyperparameters. You have to make sure that the sweep allows for every possible combination of hyperparameters to be iterated. Also, the computing resources needed to carry out the sweep must be reduced.
Which of the following actions should you take?
A. You should consider making use of the Selective grid sweep mode.
B. You should consider making use of the Measured grid sweep mode.
C. You should consider making use of the Entire grid sweep mode.
D. You should consider making use of the Random grid sweep mode.
Answer: D
Explanation:
Maximum number of runs on random grid: This option also controls the number of iterations over a random sampling of parameter values, but the values are not generated randomly from the specified range; instead, a matrix is created of all possible combinations of parameter values and a random sampling is taken over the matrix. This method is more efficient and less prone to regional oversampling or undersampling.
If you are training a model that supports an integrated parameter sweep, you can also set a range of seed values to use and iterate over the random seeds as well. This is optional, but can be useful for avoiding bias introduced by seed selection.
C: Entire grid: When you select this option, the module loops over a grid predefined by the system, to try different combinations and identify the best learner. This option is useful for cases where you don’t know what the best parameter settings might be and want to try all possible combination of values.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/tune-model-hyperparameters
QUESTION 279
You are in the process of constructing a deep convolutional neural network (CNN). The CNN will be used for image classification.
You notice that the CNN model you constructed displays hints of overfitting.
You want to make sure that overfitting is minimized, and that the model is converged to an optimal fit.
Which of the following is TRUE with regards to achieving your goal?
A. You have to add an additional dense layer with 512 input units, and reduce the amount of training data.
B. You have to add L1/L2 regularization, and reduce the amount of training data.
C. You have to reduce the amount of training data and make use of training data augmentation.
D. You have to add L1/L2 regularization, and make use of training data augmentation.
E. You have to add an additional dense layer with 512 input units, and add L1/L2 regularization.
Answer: B
Explanation:
B: Weight regularization provides an approach to reduce the overfitting of a deep learning neural network model on the training data and improve the performance of the model on new data, such as the holdout test set.
Keras provides a weight regularization API that allows you to add a penalty for weight size to the loss function.
Three different regularizer instances are provided; they are:
L1: Sum of the absolute weights.
L2: Sum of the squared weights.
L1L2: Sum of the absolute and the squared weights.
Because a fully connected layer occupies most of the parameters, it is prone to overfitting. One method to reduce overfitting is dropout. At each training stage, individual nodes are either “dropped out” of the net with probability 1-p or kept with probability p, so that a reduced network is left; incoming and outgoing edges to a dropped-out node are also removed.
By avoiding training all nodes on all training data, dropout decreases overfitting.
Reference:
https://machinelearningmastery.com/how-to-reduce-overfitting-in-deep-learning-with-weight-regularization/
https://en.wikipedia.org/wiki/Convolutional_neural_network
QUESTION 280
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 planning to make use of Azure Machine Learning designer to train models.
You need choose a suitable compute type.
Recommendation: You choose Attached compute.
Will the requirements be satisfied?
A. Yes
B. No
Answer: B
Explanation:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-create-attach-compute-studio
QUESTION 281
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 planning to make use of Azure Machine Learning designer to train models.
You need choose a suitable compute type.
Recommendation: You choose Inference cluster.
Will the requirements be satisfied?
A. Yes
B. No
Answer: B
Explanation:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-create-attach-compute-studio
QUESTION 282
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 planning to make use of Azure Machine Learning designer to train models.
You need choose a suitable compute type.
Recommendation: You choose Compute cluster.
Will the requirements be satisfied?
A. Yes
B. No
Answer: A
Explanation:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-create-attach-compute-studio
QUESTION 283
You are making use of the Azure Machine Learning to designer construct an experiment.
After dividing a dataset into training and testing sets, you configure the algorithm to be Two-Class Boosted Decision Tree.
You are preparing to ascertain the Area Under the Curve (AUC).
Which of the following is a sequential combination of the models required to achieve your goal?
A. Train, Score, Evaluate.
B. Score, Evaluate, Train.
C. Evaluate, Export Data, Train.
D. Train, Score, Export Data.
Answer: A
QUESTION 284
Hotspot Question
You have an Azure Machine Learning workspace named workspace1 that is accessible from a public endpoint. The workspace contains an Azure Blob storage datastore named store1 that represents a blob container in an Azure storage account named account1. You configure workspace1 and account1 to be accessible by using private endpoints in the same virtual network.
You must be able to access the contents of store1 by using the Azure Machine Learning SDK for Python. You must be able to preview the contents of store1 by using Azure Machine Learning studio.
You need to configure store1.
What should you do? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Box 1: Regenerate the keys of account1.
Azure Blob Storage support authentication through Account key or SAS token.
To authenticate your access to the underlying storage service, you can provide either your account key, shared access signatures (SAS) tokens, or service principal
Box 2: Update the authentication for store1.
For Azure Machine Learning studio users, several features rely on the ability to read data from a dataset; such as dataset previews, profiles and automated machine learning. For these features to work with storage behind virtual networks, use a workspace managed identity in the studio to allow Azure Machine Learning to access the storage account from outside the virtual network.
Note: Some of the studio’s features are disabled by default in a virtual network. To re-enable these features, you must enable managed identity for storage accounts you intend to use in the studio.
The following operations are disabled by default in a virtual network:
Preview data in the studio.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-access-data
QUESTION 285
Hotspot Question
You create an Azure Databricks workspace and a linked Azure Machine Learning workspace.
You have the following Python code segment in the Azure Machine Learning workspace:
import mlflow
import mlflow.azureml
import azureml.mlflow
import azureml.core
from azureml.core import Workspace
subscription_id = ‘subscription_id’
resourse_group = ‘resource_group_name’
workspace_name = ‘workspace_name’
ws = Workspace.get(name=workspace_name,
subscription_id=subscription_id,
resource_group=resource_group)
experimentName = “/Users/{user_name}/{experiment_folder}/{experiment_name}”
mlflow.set_experiment(experimentName)
uri = ws.get_mlflow_tracking_uri()
mlflow.set_tracking_uri(uri)
Instructions: For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Box 1: No
The Workspace.get method loads an existing workspace without using configuration files.
ws = Workspace.get(name=”myworkspace”,
subscription_id='<azure-subscription-id>’,
resource_group=’myresourcegroup’)
Box 2: Yes
MLflow Tracking with Azure Machine Learning lets you store the logged metrics and artifacts from your local runs into your Azure Machine Learning workspace.
The get_mlflow_tracking_uri() method assigns a unique tracking URI address to the workspace, ws, and set_tracking_uri() points the MLflow tracking URI to that address.
Box 3: Yes
Note: In Deep Learning, epoch means the total dataset is passed forward and backward in a neural network once.
Reference:
https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.workspace.workspace
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-use-mlflow
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