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NEW QUESTION 1
You create an Azure Machine Learning workspace named ML-workspace. You also create an Azure Databricks workspace named DB-workspace. DB-workspace contains a cluster named DB-cluster.
You must use DB-cluster to run experiments from notebooks that you import into DB-workspace.
You need to use ML-workspace to track MLflow metrics and artifacts generated by experiments running on DB-cluster. The solution must minimize the need for custom code.
What should you do?
- A. From DB-cluster, configure the Advanced Logging option.
- B. From DB-workspac
- C. configure the Link Azure ML workspace option.
- D. From ML-workspac
- E. create an attached compute.
- F. From ML-workspac
- G. create a compute cluster.
Answer: B
Explanation:
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-use-mlflow-azure-databricks
NEW QUESTION 2
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: Replace each missing value using the Multiple Imputation by Chained Equations (MICE) method. Does the solution meet the goal?
- A. Yes
- B. NO
Answer: A
Explanation:
Replace using MICE: For each missing value, this option assigns a new value, which is calculated by using a method described in the statistical literature as "Multivariate Imputation using Chained Equations" or "Multiple Imputation by Chained Equations". With a multiple imputation method, each variable with missing data is modeled conditionally using the other variables in the data before filling in the missing values.
Note: Multivariate imputation by chained equations (MICE), sometimes called “fully conditional specification” or “sequential regression multiple imputation” has emerged in the statistical literature as one principled method of addressing missing data. Creating multiple imputations, as opposed to single imputations, accounts for the statistical uncertainty in the imputations. In addition, the chained equations approach is very flexible and can handle variables of varying types (e.g., continuous or binary) as well as complexities such as bounds or survey skip patterns.
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 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. Regular Expression Split
- B. Split Rows with the Randomized split parameter set to true
- C. Relative Expression Split
- D. Recommender Split
Answer: B
Explanation:
Split Rows: Use this option if you just want to divide the data into two parts. You can specify the percentage of data to put in each split, but by default, the data is divided 50-50.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/split-data
NEW QUESTION 4
You create a multi-class image classification deep learning experiment by using the PyTorch framework. You plan to run the experiment on an Azure Compute cluster that has nodes with GPU’s.
You need to define an Azure Machine Learning service pipeline to perform the monthly retraining of the image classification model. The pipeline must run with minimal cost and minimize the time required to train the model.
Which three pipeline steps 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.
Solution:
Step 1: Configure a DataTransferStep() to fetch new image data…
Step 2: Configure a PythonScriptStep() to run image_resize.y on the cpu-compute compute target. Step 3: Configure the EstimatorStep() to run training script on the gpu_compute computer target.
The PyTorch estimator provides a simple way of launching a PyTorch training job on a compute target. Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-train-pytorch
Does this meet the goal?
- A. Yes
- B. Not Mastered
Answer: A
NEW QUESTION 5
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 6
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:
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.
Solution:
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
Does this meet the goal?
- A. Yes
- B. Not Mastered
Answer: A
NEW QUESTION 7
You create a new Azure Databricks workspace.
You configure a new cluster for long-running tasks with mixed loads on the compute cluster as shown in the image below.
Use the drop-down menus to select the answer choice that completes each statement based on the information presented in the graphic.
NOTE: Each correct selection is worth one point.
Solution:
Box 1: No
Running user code in separate processes is not possible in Scala. Box 2: No
Autoscaling is enabled. Minimum 2 workers, Maximum 8 workers. Reference:
https://docs.databricks.com/clusters/configure.html
Does this meet the goal?
- A. Yes
- B. Not Mastered
Answer: A
NEW QUESTION 8
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 9
You use Azure Machine Learning to deploy a model as a real-time web service.
You need to create an entry script for the service that ensures that the model is loaded when the service starts and is used to score new data as it is received.
Which functions should you include in the script? To answer, drag the appropriate functions to the correct actions. Each function 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.
Solution:
Box 1: init()
The entry script has only two required functions, init() and run(data). These functions are used to initialize the service at startup and run the model using request data passed in by a client. The rest of the script handles loading and running the model(s).
Box 2: run() Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-deploy-existing-model
Does this meet the goal?
- A. Yes
- B. Not Mastered
Answer: A
NEW QUESTION 10
You are performing feature engineering on a dataset.
You must add a feature named CityName and populate the column value with the text London.
You need to add the new feature to the dataset.
Which Azure Machine Learning Studio module should you use?
- A. Edit Metadata
- B. Preprocess Text
- C. Execute Python Script
- D. Latent Dirichlet Allocation
Answer: A
Explanation:
Typical metadata changes might include marking columns as features. References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/edit-metadata
NEW QUESTION 11
You need to correct the model fit 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.
Solution:
Step 1: Augment the data
Scenario: Columns in each dataset contain missing and null values. The datasets also contain many outliers.
Step 2: Add the Bayesian Linear Regression module.
Scenario: You produce a regression model to predict property prices by using the Linear Regression and Bayesian Linear Regression modules.
Step 3: Configure the regularization weight.
Regularization typically is used to avoid overfitting. For example, in L2 regularization weight, type the value to use as the weight for L2 regularization. We recommend that you use a non-zero value to avoid overfitting.
Scenario:
Model fit: The model shows signs of overfitting. You need to produce a more refined regression model that reduces the overfitting.
Does this meet the goal?
- A. Yes
- B. Not Mastered
Answer: A
NEW QUESTION 12
You define a datastore named ml-data for an Azure Storage blob container. In the container, you have a folder named train that contains a file named data.csv. You plan to use the file to train a model by using the Azure Machine Learning SDK.
You plan to train the model by using the Azure Machine Learning SDK to run an experiment on local compute.
You define a DataReference object by running the following code:
You need to load the training data. Which code segment should you use?
- A. Option A
- B. Option B
- C. Option C
- D. Option D
- E. Option E
Answer: E
Explanation:
Example:
data_folder = args.data_folder
# Load Train and Test data
train_data = pd.read_csv(os.path.join(data_folder, 'data.csv’)) Reference:
https://www.element61.be/en/resource/azure-machine-learning-services-complete-toolbox-ai
NEW QUESTION 13
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 14
You are a data scientist creating a linear regression model.
You need to determine how closely the data fits the regression line. Which metric should you review?
- A. Coefficient of determination
- B. Recall
- C. Precision
- D. Mean absolute error
- E. Root Mean Square Error
Answer: A
Explanation:
Coefficient of determination, often referred to as R2, represents the predictive power of the model as a value between 0 and 1. Zero means the model is random (explains nothing); 1 means there is a perfect fit. However, caution should be used in interpreting R2 values, as low values can be entirely normal and high values can be suspect.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/evaluate-model
NEW QUESTION 15
You create a pipeline in designer to train a model that predicts automobile prices.
Because of non-linear relationships in the data, the pipeline calculates the natural log (Ln) of the prices in the training data, trains a model to predict this natural log of price value, and then calculates the exponential of the scored label to get the predicted price.
The training pipeline is shown in the exhibit. (Click the Training pipeline tab.)
Training pipeline
You create a real-time inference pipeline from the training pipeline, as shown in the exhibit. (Click the Real-time pipeline tab.)
Real-time pipeline
You need to modify the inference pipeline to ensure that the web service returns the exponential of the scored label as the predicted automobile price and that client applications are not required to include a price value in the input values.
Which three modifications must you make to the inference pipeline? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
- A. Connect the output of the Apply SQL Transformation to the Web Service Output module.
- B. Replace the Web Service Input module with a data input that does not include the price column.
- C. Add a Select Columns module before the Score Model module to select all columns other than price.
- D. Replace the training dataset module with a data input that does not include the price column.
- E. Remove the Apply Math Operation module that replaces price with its natural log from the data flow.
- F. Remove the Apply SQL Transformation module from the data flow.
Answer: ACE
NEW QUESTION 16
You use the following code to run a script as an experiment in Azure Machine Learning:
You must identify the output files that are generated by the experiment run. You need to add code to retrieve the output file names.
Which code segment should you add to the script?
- A. files = run.get_properties()
- B. files= run.get_file_names()
- C. files = run.get_details_with_logs()
- D. files = run.get_metrics()
- E. files = run.get_details()
Answer: B
Explanation:
You can list all of the files that are associated with this run record by called run.get_file_names() Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-track-experiments
NEW QUESTION 17
You are hired as a data scientist at a winery. The previous data scientist used Azure Machine Learning. You need to review the models and explain how each model makes decisions.
Which explainer modules should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Solution:
info based on
the given model and data sets. The meta explainers leverage all the libraries (SHAP, LIME, Mimic, etc.) that we have integrated or developed. The following are the meta explainers available in the SDK:
Tabular Explainer: Used with tabular datasets. Text Explainer: Used with text datasets. Image Explainer: Used with image datasets. Box 1: Tabular
Box 2: Text
Box 3: Image Reference:
https://medium.com/microsoftazure/automated-and-interpretable-machine-learning-d07975741298
Does this meet the goal?
- A. Yes
- B. Not Mastered
Answer: A
NEW QUESTION 18
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