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Free DP-100 Demo Online For Microsoft Certifitcation:

NEW QUESTION 1

You need to modify the inputs for the global penalty event model to address the bias and variance issue.
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.
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  • A. Mastered
  • B. Not Mastered

Answer: A

Explanation:
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NEW QUESTION 2

You need to identify the methods for dividing the data according, to the testing requirements.
Which properties should you select? To answer, select the appropriate option-, m the answer area. NOTE: Each correct selection is worth one point.
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  • A. Mastered
  • B. Not Mastered

Answer: A

Explanation:
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NEW QUESTION 3

You are using C-Support Vector classification to do a multi-class classification with an unbalanced training dataset. The C-Support Vector classification using Python code shown below:
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You need to evaluate the C-Support Vector classification code.
Which evaluation statement should you use? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.
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  • A. Mastered
  • B. Not Mastered

Answer: A

Explanation:
Box 1: Automatically adjust weights inversely proportional to class frequencies in the input data
The “balanced” mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as n_samples / (n_classes * np.bincount(y)).
Box 2: Penalty parameter
Parameter: C : float, optional (default=1.0)
Penalty parameter C of the error term. References:
https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html

NEW QUESTION 4

You are evaluating a completed binary classification machine. You need to use the precision as the evaluation metric.
Which visualization should you use?

  • A. scatter plot
  • B. coefficient of determination
  • C. Receiver Operating Characteristic CROC) curve
  • D. Gradient descent

Answer: C

NEW QUESTION 5

You configure a Deep Learning Virtual Machine for Windows.
You need to recommend tools and frameworks to perform the following: Build deep rwur.il network (DNN) models.
Perform interactive data exploration and visualization.
Which tools and frameworks should you recommend? To answer, drag the appropriate tools to the correct tasks. Each tool may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
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  • B. Not Mastered

Answer: A

Explanation:
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NEW QUESTION 6

You are tuning a hyperparameter for an algorithm. The following table shows a data set with different hyperparameter, training error, and validation errors.
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Use the drop-down menus to select the answer choice that answers each question based on the information presented in the graphic.
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  • B. Not Mastered

Answer: A

Explanation:
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

NEW QUESTION 7

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.
You are creating a new experiment in Azure Machine Learning Studio.
One class has a much smaller number of observations than the other classes in the training set. You need to select an appropriate data sampling strategy to compensate for the class imbalance. Solution: You use the Stratified split for the sampling mode.
Does the solution meet the goal?

  • A. Yes
  • B. No

Answer: B

Explanation:
Instead use the Synthetic Minority Oversampling Technique (SMOTE) sampling mode.
Note: SMOTE is used to increase the number of underepresented 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.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/smote

NEW QUESTION 8

You are creating a binary classification by using a two-class logistic regression model. You need to evaluate the model results for imbalance.
Which evaluation metric should you use?

  • A. Relative Absolute Error
  • B. AUC Curve
  • C. Mean Absolute Error
  • D. Relative Squared Error

Answer: B

Explanation:
One can inspect the true positive rate vs. the false positive rate in the Receiver Operating Characteristic (ROC) curve and the corresponding Area Under the Curve (AUC) value. The closer this curve is to the upper left corner, the better the classifier’s performance is (that is maximizing the true positive rate while minimizing the false positive rate). Curves that are close to the diagonal of the plot, result from classifiers that tend to make predictions that are close to random guessing.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio/evaluate-model-performance#evaluating-a-bina

NEW QUESTION 9

You are working on a classification task. You have a dataset indicating whether a student would like to play soccer and associated attributes. The dataset includes the following columns:
You need to classify variables by type.
Which variable should you add to each category? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
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  • B. Not Mastered

Answer: A

Explanation:
References: https://www.edureka.co/blog/classification-algorithms/

NEW QUESTION 10

Your team is building a data engineering and data science development environment. The environment must support the following requirements:
DP-100 dumps exhibit support Python and Scala
DP-100 dumps exhibit compose data storage, movement, and processing services into automated data pipelines
DP-100 dumps exhibit the same tool should be used for the orchestration of both data engineering and data science
DP-100 dumps exhibit support workload isolation and interactive workloads
DP-100 dumps exhibit enable scaling across a cluster of machines You need to create the environment.
What should you do?

  • A. Build the environment in Apache Hive for HDInsight and use Azure Data Factory for orchestration.
  • B. Build the environment in Azure Databricks and use Azure Data Factory for orchestration.
  • C. Build the environment in Apache Spark for HDInsight and use Azure Container Instances for orchestration.
  • D. Build the environment in Azure Databricks and use Azure Container Instances for orchestration.

Answer: B

Explanation:
In Azure Databricks, we can create two different types of clusters.
DP-100 dumps exhibit Standard, these are the default clusters and can be used with Python, R, Scala and SQL
DP-100 dumps exhibit High-concurrency
Azure Databricks is fully integrated with Azure Data Factory.

NEW QUESTION 11

You are building an intelligent solution using machine learning models. The environment must support the following requirements:
DP-100 dumps exhibit Data scientists must build notebooks in a cloud environment
DP-100 dumps exhibit Data scientists must use automatic feature engineering and model building in machine learning pipelines.
DP-100 dumps exhibit Notebooks must be deployed to retrain using Spark instances with dynamic worker allocation.
DP-100 dumps exhibit Notebooks must be exportable to be version controlled locally.
You need to create the environment.
Which four 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.
DP-100 dumps exhibit

  • A. Mastered
  • B. Not Mastered

Answer: A

Explanation:
Step 1: Create an Azure HDInsight cluster to include the Apache Spark Mlib library Step 2: Install Microsot Machine Learning for Apache Spark
You install AzureML on your Azure HDInsight cluster.
Microsoft Machine Learning for Apache Spark (MMLSpark) provides a number of deep learning and data science tools for Apache Spark, including seamless integration of Spark Machine Learning pipelines with Microsoft Cognitive Toolkit (CNTK) and OpenCV, enabling you to quickly create powerful, highly-scalable predictive and analytical models for large image and text datasets.
Step 3: Create and execute the Zeppelin notebooks on the cluster
Step 4: When the cluster is ready, export Zeppelin notebooks to a local environment. Notebooks must be exportable to be version controlled locally.
References:
https://docs.microsoft.com/en-us/azure/hdinsight/spark/apache-spark-zeppelin-notebook https://azuremlbuild.blob.core.windows.net/pysparkapi/intro.html

NEW QUESTION 12

You must store data in Azure Blob Storage to support Azure Machine Learning. You need to transfer the data into Azure Blob Storage.
What are three possible ways to achieve the goal? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.

  • A. Bulk Insert SQL Query
  • B. AzCopy
  • C. Python script
  • D. Azure Storage Explorer
  • E. Bulk Copy Program (BCP)

Answer: BCD

Explanation:
You can move data to and from Azure Blob storage using different technologies: Azure Storage-Explorer
AzCopy Python SSIS
References:
https://docs.microsoft.com/en-us/azure/machine-learning/team-data-science-process/move-azure-blob

NEW QUESTION 13

You are conducting feature engineering to prepuce data for further analysis. The data includes seasonal patterns on inventory requirements.
You need to select the appropriate method to conduct feature engineering on the data. Which method should you use?

  • A. Exponential Smoothing (ETS) function.
  • B. One Class Support Vector Machine module
  • C. Time Series Anomaly Detection module
  • D. Finite Impulse Response (FIR) Filter module.

Answer: D

NEW QUESTION 14

You are building recurrent neural network to perform a binary classification.
The training loss, validation loss, training accuracy, and validation accuracy of each training epoch has been provided. You need to identify whether the classification model is over fitted.
Which of the following is correct?

  • A. The training loss increases while the validation loss decreases when training the model.
  • B. The training loss decreases while the validation loss increases when training the model.
  • C. The training loss stays constant and the validation loss decreases when training the model.
  • D. The training loss .stays constant and the validation loss stays on a constant value and close to the training loss value when training the model.

Answer: B

Explanation:
An overfit model is one where performance on the train set is good and continues to improve, whereas performance on the validation set improves to a point and then begins to degrade.
References:
https://machinelearningmastery.com/diagnose-overfitting-underfitting-lstm-models/

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.
You are analyzing a numerical dataset which contains missing values in several columns.
You must clean the missing values using an appropriate operation without affecting the dimensionality of the feature set.
You need to analyze a full dataset to include all values.
Solution: Calculate the column median value and use the median value as the replacement for any missing value in the column.
Does the solution meet the goal?

  • A. Yes
  • B. No

Answer: B

Explanation:
Use the Multiple Imputation by Chained Equations (MICE) method. References: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3074241/
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/clean-missing-data

NEW QUESTION 16
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