Your success in Microsoft DP-100 is our sole target and we develop all our DP-100 braindumps in a way that facilitates the attainment of this target. Not only is our DP-100 study material the best you can find, it is also the most detailed and the most updated. DP-100 Practice Exams for Microsoft DP-100 are written to the highest standards of technical accuracy.
Microsoft DP-100 Free Dumps Questions Online, Read and Test Now.
NEW QUESTION 1
You have a Python script that executes a pipeline. The script includes the following code:
from azureml.core import Experiment
pipeline_run = Experiment(ws, 'pipeline_test').submit(pipeline) You want to test the pipeline before deploying the script.
You need to display the pipeline run details written to the STDOUT output when the pipeline completes. Which code segment should you add to the test script?
- A. pipeline_run.get.metrics()
- B. pipeline_run.wait_for_completion(show_output=True)
- C. pipeline_param = PipelineParameter(name="stdout", default_value="console")
- D. pipeline_run.get_status()
Answer: B
Explanation:
wait_for_completion: Wait for the completion of this run. Returns the status object after the wait. Syntax: wait_for_completion(show_output=False, wait_post_processing=False, raise_on_error=True) Parameter: show_output
Indicates whether to show the run output on sys.stdout.
NEW QUESTION 2
You plan to use a Data Science Virtual Machine (DSVM) with the open source deep learning frameworks Caffe2 and Theano. You need to select a pre configured DSVM to support the framework.
What should you create?
- A. Data Science Virtual Machine for Linux (CentOS)
- B. Data Science Virtual Machine for Windows 2012
- C. Data Science Virtual Machine for Windows 2016
- D. Geo AI Data Science Virtual Machine with ArcGIS
- E. Data Science Virtual Machine for Linux (Ubuntu)
Answer: E
NEW QUESTION 3
You use Azure Machine Learning Studio to build a machine learning experiment. You need to divide data into two distinct datasets.
Which module should you use?
- A. Split Data
- B. Load Trained Model
- C. Assign Data to Clusters
- D. Group Data into Bins
Answer: D
Explanation:
The Group Data into Bins module supports multiple options for binning data. You can customize how the bin edges are set and how values are apportioned into the bins.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/group-data-into-bins
NEW QUESTION 4
You need to set up the Permutation Feature Importance module according to the model training requirements. Which properties should you select? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Solution:
Box 1: Accuracy
Scenario: You want to configure hyperparameters in the model learning process to speed the learning phase by using hyperparameters. In addition, this configuration should cancel the lowest performing runs at each evaluation interval, thereby directing effort and resources towards models that are more likely to be successful.
Box 2: R-Squared
Does this meet the goal?
- A. Yes
- B. Not Mastered
Answer: A
NEW QUESTION 5
You plan to use Hyperdrive to optimize the hyperparameters selected when training a model. You create the following code to define options for the hyperparameter experiment
For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.
Solution:
Box 1: No
max_total_runs (50 here)
The maximum total number of runs to create. This is the upper bound; there may be fewer runs when the sample space is smaller than this value.
Box 2: Yes
Policy EarlyTerminationPolicy
The early termination policy to use. If None - the default, no early termination policy will be used. Box 3: No
Discrete hyperparameters are specified as a choice among discrete values. choice can be: one or more comma-separated values
a range object any arbitrary list object Reference:https://docs.microsoft.com/en-us/python/api/azureml-train-core/azureml.train.hyperdrive.hyperdriveconfig https://docs.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters
Does this meet the goal?
- A. Yes
- B. Not Mastered
Answer: A
NEW QUESTION 6
You retrain an existing model.
You need to register the new version of a model while keeping the current version of the model in the registry.
What should you do?
- A. Register a model with a different name from the existing model and a custom property named versionwith the value 2.
- B. Register the model with the same name as the existing model.
- C. Save the new model in the default datastore with the same name as the existing mode
- D. Do not register the new model.
- E. Delete the existing model and register the new one with the same name.
Answer: B
Explanation:
Model version: A version of a registered model. When a new model is added to the Model Registry, it is added as Version 1. Each model registered to the same model name increments the version number.
Reference:
https://docs.microsoft.com/en-us/azure/databricks/applications/mlflow/model-registry
NEW QUESTION 7
You are building a machine learning model for translating English language textual content into French language textual content.
You need to build and train the machine learning model to learn the sequence of the textual content. Which type of neural network should you use?
- A. Multilayer Perceptions (MLPs)
- B. Convolutional Neural Networks (CNNs)
- C. Recurrent Neural Networks (RNNs)
- D. Generative Adversarial Networks (GANs)
Answer: C
Explanation:
To translate a corpus of English text to French, we need to build a recurrent neural network (RNN).
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.
References:
https://towardsdatascience.com/language-translation-with-rnns-d84d43b40571
NEW QUESTION 8
You have a dataset that includes home sales data for a city. The dataset includes the following columns.
Each row in the dataset corresponds to an individual home sales transaction.
You need to use automated machine learning to generate the best model for predicting the sales price based on the features of the house.
Which values should you use? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.
Solution:
Box 1: Regression
Regression is a supervised machine learning technique used to predict numeric values. Box 2: Price
Reference:
https://docs.microsoft.com/en-us/learn/modules/create-regression-model-azure-machine-learning-designer
Does this meet the goal?
- A. Yes
- B. Not Mastered
Answer: A
NEW QUESTION 9
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
Explanation:
The linear discriminant analysis method works only on continuous variables, not categorical or ordinal variables.
Linear discriminant analysis is similar to analysis of variance (ANOVA) in that it works by comparing the means of the variables.
Scenario:
Data scientists must build notebooks in a local environment using automatic feature engineering and model building in machine learning pipelines.
Experiments for local crowd sentiment models must combine local penalty detection data. All shared features for local models are continuous variables.
NEW QUESTION 10
You are tuning a hyperparameter for an algorithm. The following table shows a data set with different hyperparameter, training error, and validation errors.
Use the drop-down menus to select the answer choice that answers each question based on the information presented in the graphic.
Solution:
Box 1: 4
Choose the one which has lower training and validation error and also the closest match. Minimize variance (difference between validation error and train error).
Box 2: 5
Minimize variance (difference between validation error and train error). Reference:
https://medium.com/comet-ml/organizing-machine-learning-projects-project-management-guidelines-2d2b8565
Does this meet the goal?
- A. Yes
- B. Not Mastered
Answer: A
NEW QUESTION 11
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.
Solution:
Text, table Description automatically generated
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
Does this meet the goal?
- A. Yes
- B. Not Mastered
Answer: A
NEW QUESTION 12
You create a binary classification model by using Azure Machine Learning Studio.
You must tune hyperparameters by performing a parameter sweep of the model. The parameter sweep must meet the following requirements:
iterate all possible combinations of hyperparameters
minimize computing resources required to perform the sweep You need to perform a parameter sweep of the model.
Which parameter sweep mode should you use?
- A. Random sweep
- B. Sweep clustering
- C. Entire grid
- D. Random grid
- E. Random seed
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.
NEW QUESTION 13
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 token_auth_enabled parameter of the target configuration object to true
- B. Deploy the model to Azure Container Instance
- C. During deployment, set the auch_enabled parameter of the target configuration object to true
- D. Deploy the model to Azure Container Instance
- E. During deployment, set the coken_auch_enabled parameter of the target configuration object to true
- F. Deploy the model to Azure Kubernetes Service (AKS). During deployment, set the auc
- G. enabled parameter of the target configuration object to true
Answer: A
Explanation:
To control token authentication, use the token_auth_enabled parameter when you create or update a deployment
Token authentication is disabled by default when you deploy to Azure Kubernetes Service.
Note: The model deployments created by Azure Machine Learning can be configured to use one of two authentication methods:
key-based: A static key is used to authenticate to the web service.
token-based: A temporary token must be obtained from the Azure Machine Learning workspace (using Azure Active Directory) and used to authenticate to the web service.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-authenticate-web-service
NEW QUESTION 14
You create a Python script that runs a training experiment in Azure Machine Learning. The script uses the Azure Machine Learning SDK for Python.
You must add a statement that retrieves the names of the logs and outputs generated by the script. You need to reference a Python class object from the SDK for the statement.
Which class object should you use?
- A. Run
- B. ScripcRunConfig
- C. Workspace
- D. Experiment
Answer: A
Explanation:
A run represents a single trial of an experiment. Runs are used to monitor the asynchronous execution of a trial, log metrics and store output of the trial, and to analyze results and access artifacts generated by the trial.
The run Class get_all_logs method downloads all logs for the run to a directory. Reference:
https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.run(class)
NEW QUESTION 15
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
An IT department creates the following Azure resource groups and resources:
The IT department creates an Azure Kubernetes Service (AKS)-based inference compute target named
aks-cluster in the Azure Machine Learning workspace.
You have a Microsoft Surface Book computer with a GPU. Python 3.6 and Visual Studio Code are installed. You need to run a script that trains a deep neural network (DNN) model and logs the loss and accuracy
metrics.
Solution: Attach the mlvm virtual machine as a compute target in the Azure Machine Learning workspace. Install the Azure ML SDK on the Surface Book and run Python code to connect to the workspace. Run the training script as an experiment on the mlvm remote compute resource.
- A. Yes
- B. No
Answer: A
Explanation:
Use the VM as a compute target.
Note: A compute target is a designated compute resource/environment where you run your training script or host your service deployment. This location may be your local machine or a cloud-based compute resource.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/concept-compute-target
NEW QUESTION 16
You need to define an evaluation strategy for the crowd sentiment models.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
Solution:
Step 1: Define a cross-entropy function activation
When using a neural network to perform classification and prediction, it is usually better to use cross-entropy error than classification error, and somewhat better to use cross-entropy error than mean squared error to evaluate the quality of the neural network.
Step 2: Add cost functions for each target state. Step 3: Evaluated the distance error metric. References:
https://www.analyticsvidhya.com/blog/2018/04/fundamentals-deep-learning-regularization-techniques/
Does this meet the goal?
- A. Yes
- B. Not Mastered
Answer: A
NEW QUESTION 17
You are creating an experiment by using Azure Machine Learning Studio.
You must divide the data into four subsets for evaluation. There is a high degree of missing values in the data. You must prepare the data for analysis.
You need to select appropriate methods for producing the experiment.
Which three modules should you run in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
NOTE: More than one order of answer choices is correct. You will receive credit for any of the correct orders you select.
Solution:
The Clean Missing Data module in Azure Machine Learning Studio, to remove, replace, or infer missing values.
Does this meet the goal?
- A. Yes
- B. Not Mastered
Answer: A
NEW QUESTION 18
An organization creates and deploys a multi-class image classification deep learning model that uses a set of labeled photographs.
The software engineering team reports there is a heavy inferencing load for the prediction web services during the summer. The production web service for the model fails to meet demand despite having a fully-utilized compute cluster where the web service is deployed.
You need to improve performance of the image classification web service with minimal downtime and minimal administrative effort.
What should you advise the IT Operations team to do?
- A. Increase the minimum node count of the compute cluster where the web service is deployed.
- B. Create a new compute cluster by using larger VM sizes for the nodes, redeploy the web service to that cluster, and update the DNS registration for the service endpoint to point to the new cluster.
- C. Increase the VM size of nodes in the compute cluster where the web service is deployed.
- D. Increase the node count of the compute cluster where the web service is deployed.
Answer: D
Explanation:
The Azure Machine Learning SDK does not provide support scaling an AKS cluster. To scale the nodes in the cluster, use the UI for your AKS cluster in the Azure Machine Learning studio. You can only change the node count, not the VM size of the cluster.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-create-attach-kubernetes
NEW QUESTION 19
You use the Two-Class Neural Network module in Azure Machine Learning Studio to build a binary classification model. You use the Tune Model Hyperparameters module to tune accuracy for the model.
You need to select the hyperparameters that should be tuned using the Tune Model Hyperparameters module. Which two hyperparameters should you use? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
- A. Number of hidden nodes
- B. Learning Rate
- C. The type of the normalizer
- D. Number of learning iterations
- E. Hidden layer specification
Answer: DE
Explanation:
D: For Number of learning iterations, specify the maximum number of times the algorithm should process the training cases.
E: For Hidden layer specification, select the type of network architecture to create.
Between the input and output layers you can insert multiple hidden layers. Most predictive tasks can be accomplished easily with only one or a few hidden layers.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/two-class-neural-network
NEW QUESTION 20
......
P.S. Easily pass DP-100 Exam with 349 Q&As Certleader Dumps & pdf Version, Welcome to Download the Newest Certleader DP-100 Dumps: https://www.certleader.com/DP-100-dumps.html (349 New Questions)