Questions (subset) Flashcards
Many but not all confirmed...
You are developing a hands-on workshop to introduce Docker for Windows to attendees. You need to ensure that workshop attendees can install Docker on their devices.
Which two prerequisite components should attendees install on the devices? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
A. Microsoft Hardware-Assisted Virtualization Detection Tool B. Kitematic C. BIOS-enabled virtualization D. VirtualBox E. Windows 10 64-bit Professional
Correct Answer: CE
C - BIOS-enabled virtualization: Make sure your Windows system supports Hardware Virtualization Technology and that virtualization is enabled. Ensure that hardware virtualization support is turned on in the BIOS settings.
E - Windows 10 64-bit Professional: To run Docker, your machine must have a 64-bit operating system running Windows 7 or higher.
References:
https: //docs.docker.com/toolbox/toolbox_install_windows/
https: //blogs.technet.microsoft.com/canitpro/2015/09/08/step-by-step-enabling-hyper-v-for-use-on-windows-10/
Your team is building a data engineering and data science development environment.
The environment must support the following requirements:
✑ support Python and Scala
✑ compose data storage, movement, and processing services into automated data pipelines
✑ the same tool should be used for the orchestration of both data engineering and data science
✑ support workload isolation and interactive workloads
✑ 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.
Correct Answer: B
Azure Databricks is fully integrated with Azure Data Factory. In Azure Databricks, we can create two different types of clusters. Standard, these are the default clusters and can be used with Python, R, Scala and SQL
Incorrect Answers:
D: Azure Container Instances is good for development or testing. Not suitable for production workloads.
References:
https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/data-science-and-machine-learning
You train a model and register it in your Azure Machine Learning workspace. You are ready to deploy the model as a real-time web service.
You deploy the model to an Azure Kubernetes Service (AKS) inference cluster, but the deployment fails because an error occurs when the service runs the entry script that is associated with the model deployment.
You need to debug the error by iteratively modifying the code and reloading the service, without requiring a re-deployment of the service for each code update.
What should you do?
A. Modify the AKS service deployment configuration to enable application insights and re-deploy to AKS.
B. Create an Azure Container Instances (ACI) web service deployment configuration and deploy the model on ACI.
C. Add a breakpoint to the first line of the entry script and redeploy the service to AKS.
D. Create a local web service deployment configuration and deploy the model to a local Docker container.
E. Register a new version of the model and update the entry script to load the new version of the model from its registered path.
Correct Answer: B
How to work around or solve common Docker deployment errors with Azure Container Instances (ACI) and Azure Kubernetes Service (AKS) using Azure Machine Learning.
The recommended and the most up to date approach for model deployment is via the Model.deploy() API using an Environment object as an input parameter. In this case our service will create a base docker image for you during deployment stage and mount the required models all in one call. The basic deployment tasks are:
- Register the model in the workspace model registry.
- Define Inference Configuration:a. Create an Environment object based on the dependencies you specify in the environment yaml file or use one of our procured environments
b. Create an inference configuration (InferenceConfig object) based on the environment and the scoring script. - Deploy the model to Azure Container Instance (ACI) service or to Azure Kubernetes Service (AKS).
You are creating a classification model for a banking company to identify possible instances of credit card fraud. You plan to create the model in Azure Machine Learning by using automated machine learning. The training dataset that you are using is highly unbalanced.
You need to evaluate the classification model. Which primary metric should you use?
A. normalized_mean_absolute_error B. AUC_weighted C. accuracy D. normalized_root_mean_squared_error E. spearman_correlation
Correct Answer: B
AUC_weighted is a Classification metric. Weighted is the arithmetic mean of the score for each class, ** weighted by the number of true instances in each class **.
Incorrect Answers:
A: normalized_mean_absolute_error is a regression metric, not a classification metric.
C: When comparing approaches to imbalanced classification problems, consider using metrics beyond accuracy such as recall, precision, and AUROC. It may be that switching the metric you optimize for during parameter selection or model selection is enough to provide desirable performance detecting the minority class.
D: normalized_root_mean_squared_error is a regression metric, not a classification metric.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-understand-automated-ml
You are a data scientist working for a bank and have used Azure ML to train and register a machine learning model that predicts whether a customer is likely to repay a loan.
You want to understand how your model is making selections and must be sure that the model does not violate government regulations such as denying loans based on where an applicant lives.
You need to determine the extent to which each feature in the customer data is influencing predictions.
What should you do?
A. Enable data drift monitoring for the model and its training dataset.
B. Score the model against some test data with known label values and use the results to calculate a confusion matrix.
C. Use the Hyperdrive library to test the model with multiple hyperparameter values.
D. Use the interpretability package to generate an explainer for the model.
E. Add tags to the model registration indicating the names of the features in the training dataset.
Correct Answer: D
Interpretability is critical for data scientists, auditors, and business decision makers alike to ensure compliance with company policies, industry standards, and government regulations:
Data scientists need the ability to explain their models to executives and stakeholders, so they can understand the value and accuracy of their findings. They also require interpretability to debug their models and make informed decisions about how to improve them.
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-machine-learning-interpretability
Incorrect Answers:
A: In the context of machine learning, data drift is the change in model input data that leads to model performance degradation. It is one of the top reasons where model accuracy degrades over time, thus monitoring data drift helps detect model performance issues.
B: A confusion matrix is used to describe the performance of a classification model. Each row displays the instances of the true, or actual class in your dataset, and each column represents the instances of the class that was predicted by the model.
C: Hyperparameters are adjustable parameters you choose for model training that guide the training process. The HyperDrive package helps you automate choosing these parameters.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-machine-learning-interpretability-automl
You create a multi-class image classification deep learning model that uses the PyTorch deep learning framework. You must configure Azure Machine Learning Hyperdrive to optimize the hyperparameters for the classification model.
You need to define a primary metric to determine the hyperparameter values that result in the model with the best accuracy score.
Which three actions must you perform? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point.
A. Set the primary_metric_goal of the estimator used to run the bird_classifier_train.py script to maximize.
B. Add code to the bird_classifier_train.py script to calculate the validation loss of the model and log it as a float value with the key loss.
C. Set the primary_metric_goal of the estimator used to run the bird_classifier_train.py script to minimize.
D. Set the primary_metric_name of the estimator used to run the bird_classifier_train.py script to accuracy.
E. Set the primary_metric_name of the estimator used to run the bird_classifier_train.py script to loss.
F. Add code to the bird_classifier_train.py script to calculate the validation accuracy of the model and log it as a float value with the key accuracy.
Correct Answer: ADF
AD:
primary_metric_name=”accuracy”,
primary_metric_goal=PrimaryMetricGoal.MAXIMIZE
Make sure to log this value in your training script.
primary_metric_name: The name of the primary metric to optimize. The name of the primary metric needs to exactly match the name of the metric logged by the training script.
primary_metric_goal: It can be either PrimaryMetricGoal.MAXIMIZE or PrimaryMetricGoal.MINIMIZE and determines whether the primary metric will be maximized or minimized when evaluating the runs.
F: The training script calculates the val_accuracy and logs it as “accuracy”, which is used as the primary metric.
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters
You plan to use automated machine learning to train a regression model. You have data that has features which have missing values, and categorical features with few distinct values.
You need to configure automated machine learning to automatically impute missing values and encode categorical features as part of the training task.
Which parameter and value pair should you use in the AutoMLConfig class?
A. featurization = 'auto' B. enable_voting_ensemble = True C. task = 'classification' D. exclude_nan_labels = True E. enable_tf = True
Correct Answer: A
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
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
Correct Answer: B
For PyTorch, TensorFlow and Chainer tasks, Azure Machine Learning provides respective PyTorch, TensorFlow, and Chainer estimators to simplify using these frameworks.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-train-ml-models
You are a lead data scientist for a project that tracks the health and migration of birds.
You create a multi-class image classification deep learning model that uses a set of labeled bird photographs collected by experts.
You have 100,000 photographs of birds. All photographs use the JPG format and are stored in an Azure blob container in an Azure subscription.
You need to access the bird photograph files in the Azure blob container from the Azure Machine Learning service workspace that will be used for deep learning model training. You must minimize data movement.
What should you do?
A. Create an Azure Data Lake store and move the bird photographs to the store.
B. Create an Azure Cosmos DB database and attach the Azure Blob containing bird photographs storage to the database.
C. Create and register a dataset by using TabularDataset class that references the Azure blob storage containing bird photographs.
D. Register the Azure blob storage containing the bird photographs as a datastore in Azure Machine Learning service.
E. Copy the bird photographs to the blob datastore that was created with your Azure Machine Learning service workspace.
Correct Answer: D
We recommend creating a datastore for an Azure Blob container. When you create a workspace, an Azure blob container and an Azure file share are automatically registered to the workspace.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-access-data
You use the Azure Machine Learning service to create a tabular dataset named training_data. You plan to use this dataset in a training script.
You create a variable that references the dataset using the following code:
training_ds = workspace.datasets.get(“training_data”)
You define an estimator to run the script.
You need to set the correct property of the estimator to ensure that your script can access the training_data dataset.
Which property should you set?
A. environment_definition = {“training_data”:training_ds}
B. inputs = [training_ds.as_named_input(‘training_ds’)]
C. script_params = {“–training_ds”:training_ds}
D. source_directory = training_ds
Correct Answer: B
Example:
# Get the training dataset
diabetes_ds = ws.datasets.get("Diabetes Dataset")
# Create an estimator that uses the remote compute
hyper_estimator = SKLearn(source_directory=experiment_folder, inputs=[diabetes_ds.as_named_input('diabetes')],
# Pass the dataset as an input compute_target = cpu_cluster, conda_packages=['pandas','ipykernel','matplotlib'], pip_packages=['azureml-sdk','argparse','pyarrow'], entry_script='diabetes_training.py')
Reference:
https://notebooks.azure.com/GraemeMalcolm/projects/azureml-primers/html/04%20-%20Optimizing%20Model%20Training.ipynb
You are creating a new Azure Machine Learning pipeline using the designer.
The pipeline must train a model using data in a comma-separated values (CSV) file that is published on a website. You have not created a dataset for this file.
You need to ingest the data from the CSV file into the designer pipeline using the minimal administrative effort.
Which module should you add to the pipeline in Designer?
A. Convert to CSV
B. Enter Data Manually
C. Import Data
D. Dataset
Correct Answer: C
Notes:
- “… using the minimal administrative effort.”
- Dataset” is not a module
- Import Data “supports Url via Http”
However:
The preferred way to provide data to a pipeline is a Dataset object. The Dataset object points to data that lives in or is accessible from a datastore or at a Web URL.
Reference:
https: //docs.microsoft.com/en-us/azure/machine-learning/how-to-designer-import-data
https: //docs.microsoft.com/en-us/azure/machine-learning/algorithm-module-reference/import-data
https: //docs.microsoft.com/en-us/azure/machine-learning/how-to-access-data
https: //docs.microsoft.com/en-us/azure/machine-learning/how-to-create-your-first-pipeline
You have a comma-separated values (CSV) file containing data from which you want to train a classification model.
You are using the Automated Machine Learning interface in Azure Machine Learning studio to train the classification model. You set the task type to Classification.
You need to ensure that the Automated Machine Learning process evaluates only linear models.
What should you do?
A. Add all algorithms other than linear ones to the blocked algorithms list.
B. Set the Exit criterion option to a metric score threshold.
C. Clear the option to perform automatic featurization.
D. Clear the option to enable deep learning.
E. Set the task type to Regression.
Correct Answer: A
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-use-automated-ml-for-ml-models
You create a deep learning model for image recognition on Azure Machine Learning service using GPU-based training.
You must deploy the model to a context that allows for real-time GPU-based inferencing.
You need to configure compute resources for model inferencing.
Which compute type should you use?
A. Azure Container Instance
B. Azure Kubernetes Service
C. Field Programmable Gate Array
D. Machine Learning Compute
Correct Answer: B
You can use Azure Machine Learning to deploy a GPU-enabled model as a web service. Deploying a model on Azure Kubernetes Service (AKS) is one option.
The AKS cluster provides a GPU resource that is used by the model for inference.
Inference, or model scoring, is the phase where the deployed model is used to make predictions. Using GPUs instead of CPUs offers performance advantages on highly parallelizable computation.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-deploy-inferencing-gpus
You train and register a model in your Azure Machine Learning workspace.
You must publish a pipeline that enables client applications to use the model for batch inferencing. You must use a pipeline with a single ParallelRunStep step that runs a Python inferencing script to get predictions from the input data.
You need to create the inferencing script for the ParallelRunStep pipeline step.
Which two functions should you include? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point.
A. run(mini_batch) B. main() C. batch() D. init() E. score(mini_batch)
Correct Answer: AD
Reference:
https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/machine-learning-pipelines/parallel-run
You create a multi-class image classification deep learning model. You train the model by using PyTorch version 1.2.
You need to ensure that the correct version of PyTorch can be identified for the inferencing environment when the model is deployed.
What should you do?
A. Save the model locally as a.pt file, and deploy the model as a local web service.
B. Deploy the model on computer that is configured to use the default Azure Machine Learning conda environment.
C. Register the model with a .pt file extension and the default version property.
D. Register the model, specifying the model_framework and model_framework_version properties.
Correct Answer: D
FRAMEWORK_NAME = 'PyTorch' FRAMEWORK_VERSION = ''1.2'
Default=1.4
Reference:
https://docs.microsoft.com/en-us/python/api/azureml-train-core/azureml.train.dnn.pytorch?view=azure-ml-py
DRAG DROP -
You have a model with a large difference between the training and validation error values.
You must create a new model and perform cross-validation.
You need to identify a parameter set for the new model using Azure Machine Learning Studio. Which module you should use for each step? To answer, drag the appropriate modules to the correct steps.
Each module may be used once or more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
Each correct selection is worth one point.
NOTE:
Select and Place:
Partition and Sample
Tune Model Hyperparameters
Split data
Two-Class Boosted Decision Tree
Correct Answer: Explanation
Box 1: Split data
Box 2: Partition and Sample -
Box 3: Two-Class Boosted Decision Tree
Box 4: Tune Model Hyperparameters
Integrated train and tune: You configure a set of parameters to use, and then let the module iterate over multiple combinations, measuring accuracy until it finds a “best” model. With most learner modules, you can choose which parameters should be changed during the training process, and which should remain fixed.
We recommend that you use Cross-Validate Model to establish the goodness of the model given the specified parameters. Use Tune Model Hyperparameters to identify the optimal parameters.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/partition-and-sample
You plan to provision an Azure Machine Learning Basic edition workspace for a data science project.
You need to identify the tasks you will be able to perform in the workspace.
Which three tasks will you be able to perform?
Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
A. Create a Compute Instance and use it to run code in Jupyter notebooks.
B. Create an Azure Kubernetes Service (AKS) inference cluster.
C. Use the designer to train a model by dragging and dropping pre-defined modules.
D. Create a tabular dataset that supports versioning.
E. Use the Automated Machine Learning user interface to train a model.
Correct Answer: ABD
Reference:
https://azure.microsoft.com/en-us/pricing/details/machine-learning/
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
Correct Answer: B
“AUC is a good general summary of the predictive power of a classifier, especially when the dataset is imbalanced.”
"If a model has class imbalance, the confusion matrix will help to detect a biased model" "Micro-average is preferable if there is class imbalance present in the dataset."
References:
https: //adatis.co.uk/evaluating-models-in-azure-machine-learning-part-1-classification/
https: //docs.microsoft.com/en-us/azure/machine-learning/how-to-understand-automated-ml
https: //docs.microsoft.com/en-us/azure/machine-learning/studio/evaluate-model-performance#evaluating-a- binary-classification-model
You are building a 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 overfitted.
Which of the following is correct?
A. 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.
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 increases while the validation loss decreases when training the model.
Correct Answer: B
References:
https://www.tensorflow.org/tutorials/keras/overfit_and_underfit
“we saw that the accuracy of our model on the validation data would peak after training for a number of epochs, and would then stagnate or start decreasing. In other words, our model would overfit to the training data.” remember: a lower validation loss indicates a better model
https://machinelearningmastery.com/diagnose-overfitting-underfitting-lstm-models/
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.
there is a common misconception that if test accuracy on unseen data is lower than training accuracy, the model is over-fitted. However, test accuracy should always be less than training accuracy, and the distinction for over-fit vs. appropriately fit comes down to how much less accurate.
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.
Each correct selection is worth one point.
NOTE:
A. Number of hidden nodes B. Learning Rate C. The type of the normalizer D. Number of learning iterations E. Hidden layer specification
Note hparams set here: https://www.examtopics.com/exams/microsoft/dp-100/view/11/
Correct Answer: DE
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
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
Correct Answer: D
Explanation
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. You can also reduce the size of the grid and run a ***random grid sweep***. Research has shown that this method yields the same results, but is more efficient computationally.
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.
Incorrect Answers:
B: If you are building a clustering model, use Sweep Clustering to automatically determine the optimum number of clusters and other parameters.
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.
E: If you choose a random sweep, you can specify how many times the model should be trained, using a random combination of parameter values.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/tune-model-hyperparameters
You are creating a machine learning model. You have a dataset that contains null rows.
You need to use the Clean Missing Data module in Azure Machine Learning Studio to identify and resolve the null and missing data in the dataset.
Which parameter should you use?
A. Replace with mean
B. Remove entire column
C. Remove entire row
D. Hot Deck
Correct Answer: C
Remove entire row: Completely removes any row in the dataset that has one or more missing values. This is useful if the missing value can be considered randomly missing.
Replace mean only works for Int/Float/Booleans
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/clean-missing-data
HOTSPOT -
You plan to preprocess text from CSV files. You load the Azure Machine Learning Studio default stop words list.
You need to configure the Preprocess Text module to meet the following requirements:
✑ Ensure that multiple related words from a single canonical form.
✑ Remove pipe characters from text.
✑ Remove words to optimize information retrieval.
Which three options should you select? To answer, select the appropriate options in the answer area.
Each correct selection is worth one point.
NOTE:
Hot Area:
Correct Answer:
Box 1: Remove stop words -
Remove words to optimize information retrieval.
Remove stop words: Select this option if you want to apply a predefined stopword list to the text column. Stop word removal is performed before any other processes.
Box 2: Lemmatization -
Ensure that multiple related words from a single canonical form.
Lemmatization converts multiple related words to a single canonical form
Box 3: Remove special characters
Remove special characters: Use this option to replace any non-alphanumeric special characters with the pipe | character.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/preprocess-text
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
Correct Answer: B
The evaluation metrics available for binary classification models are: Accuracy, Precision, Recall, F1 Score, and AUC.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio/evaluate-model-performance#evaluating-a-binary-classification-model
You are using a decision tree algorithm. You have trained a model that generalizes well at a tree depth equal to 10.
You need to select the bias and variance properties of the model with varying tree depth values.
Which properties should you select for each tree depth? To answer, select the appropriate options in the answer area.
Hot Area:
Tree Depth 5:
Bias = high/low; Variance = high/low?
Tree Depth 15
Bias = High/Low? Variance = High/Low?
Tree Depth 5
Bias = High;
Variance = Low
Tree Depth 15
Bias = Low;
Variance = High
In decision trees, the depth of the tree determines the variance. A complicated decision tree (e.g. deep) has low bias and high variance.
Note: In statistics and machine learning, the bias”“variance tradeoff is the property of a set of predictive models whereby models with a lower bias in parameter estimation have a higher variance of the parameter estimates across samples, and vice versa. Increasing the bias will decrease the variance. Increasing the variance will decrease the bias.
References:
https://machinelearningmastery.com/gentle-introduction-to-the-bias-variance-trade-off-in-machine-learning/
You are implementing a machine learning model to predict stock prices.
The model uses a PostgreSQL database and requires GPU processing.
You need to create a virtual machine that is pre-configured with the required tools.
What should you do?
A. Create a Data Science Virtual Machine (DSVM) Windows edition.
B. Create a Geo Al Data Science Virtual Machine (Geo-DSVM) Windows edition.
C. Create a Deep Learning Virtual Machine (DLVM) Linux edition.
D. Create a Deep Learning Virtual Machine (DLVM) Windows edition.
E. Create a Data Science Virtual Machine (DSVM) Linux edition.
Correct Answer: E
https://docs.microsoft.com/en-us/azure/machine-learning/data-science-virtual-machine/linux-dsvm-walkthrough#other-tools
In the DSVM, your training models can use deep learning algorithms on hardware that’s based on graphics processing units (GPUs).
You can switch to a GPU-based VM when you’re training large models, or when you need high-speed computations while keeping the same OS disk.
You can choose any of the N series GPU enabled virtual machine SKUs with DSVM. Please note Azure free accounts do not support GPU enabled virtual machine SKUs.
The Windows editions of the DSVM comes pre-installed with GPU drivers, frameworks, and GPU versions of deep learning frameworks.
** On the Linux edition, deep learning on GPUs is enabled on the Ubuntu DSVMs **
https://docs.microsoft.com/en-us/azure/machine-learning/data-science-virtual-machine/overview
Incorrect Answers:
A, C: PostgreSQL (CentOS) is only available in the Linux Edition.
B: The Azure Geo AI Data Science VM (Geo-DSVM) delivers geospatial analytics capabilities from Microsoft’s Data Science VM. Specifically, this VM extends the
AI and data science toolkits in the Data Science VM by adding ESRI’s market-leading ArcGIS Pro Geographic Information System.
D: DLVM is a template on top of DSVM image. In terms of the packages, GPU drivers etc are all there in the DSVM image. Mostly it is for convenience during creation where we only allow DLVM to be created on GPU VM instances on Azure.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/data-science-virtual-machine/overview
You create an experiment in Azure Machine Learning Studio. You add a training dataset that contains 10,000 rows. The first 9,000 rows represent class 0 (90 percent).
The remaining 1,000 rows represent class 1 (10 percent). The training set is imbalances between two classes. You must increase the number of training examples for class 1 to 4,000 by using 5 data rows.
You add the Synthetic Minority Oversampling Technique (SMOTE) module to the experiment. You need to configure the module.
Which values should you use? To answer, select the appropriate options in the dialog box in the answer area.
Each correct selection is worth one point.
NOTE:
Hot Area:
Percentage
100
200
300
Rows 1 2 4 5 10
Correct Answer:
Box 1: 300
You type 300 (%), the module triples the percentage of minority cases (3000) compared to the original dataset (1000).
“you can set the value of SMOTE percentage, using multiples of 100”
Box 2: 5
We should use 5 data rows.
Use the Number of nearest neighbors option to determine the size of the feature space that the SMOTE algorithm uses when in building new cases. A nearest neighbor is a row of data (a case) that is very similar to some target case. The distance between any two cases is measured by combining the weighted vectors of all features.
By increasing the number of nearest neighbors, you get features from more cases.
By keeping the number of nearest neighbors low, you use features that are more like those in the original sample.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/smote
DRAG DROP -
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.
More than one order of answer choices is correct. You will receive credit for any of the correct orders you select.
NOTE:
Select and Place:
Build Counting Transform Partition and Sample Replace discrete values Import data Latent Direchlet Transformation Clean Missing Data Missing Value Scrubber
Correct Answer
- Import data
- Clean Missing Data
- Partition and Sample
“Partition and Sample creates multiple partitions of a dataset based on sampling”
Incorrect Answers:
✑ Latent Direchlet Transformation: Latent Dirichlet Allocation module in Azure Machine Learning Studio, to group otherwise unclassified text into a number of categories. Latent Dirichlet Allocation (LDA) is often used in natural language processing (NLP) to find texts that are similar. Another common term is topic modeling.
✑ Build Counting Transform: Build Counting Transform module in Azure Machine Learning Studio, to analyze training data. From this data, the module builds a count table as well as a set of count-based features that can be used in a predictive model.
✑ Missing Value Scrubber: The Missing Values Scrubber module is deprecated.
✑ Feature hashing: Feature hashing is used for linguistics, and works by converting unique tokens into integers.
✑ Replace discrete values: the Replace Discrete Values module in Azure Machine Learning Studio is used to generate a probability score that can be used to represent a discrete value. This score can be useful for understanding the information value of the discrete values
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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 model to predict the price of a student’s artwork depending on the following variables: the student’s length of education, degree type, and art form.
You start by creating a linear regression model.
You need to evaluate the linear regression model.
Solution: Use the following metrics: Mean Absolute Error, Root Mean Absolute Error, Relative Absolute Error, Accuracy, Precision, Recall, F1 score, and AUC.
Does the solution meet the goal?
A. Yes
B. No
Correct Answer: B
Accuracy, Precision, Recall, F1 score, and AUC are metrics for evaluating classification models.
Note: Mean Absolute Error, Root Mean Absolute Error, Relative Absolute Error are OK for the linear regression model.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/evaluate-model
HOTSPOT - You have a dataset that contains 2,000 rows. You are building a machine learning classification model by using Azure Learning Studio. You add a Partition and Sample module to the experiment.
You need to configure the module. You must meet the following requirements:
✑ Divide the data into subsets
✑ Assign the rows into folds using a round-robin method
✑ Allow rows in the dataset to be reused
How should you configure the module? To answer, select the appropriate options in the dialog box in the answer area. NOTE: Each correct selection is worth one point.
Hot Area:
Partition or sample mode
- -> Assing to Folds?
- -> Pick a fold?
- -> Sampling?
- -> Head?
Use replacement partitioning?
Randomized split?
Correct Answer:
“Partition or sample mode” –> Assign to Folds
“Use replacement in partitioning” –> Yes/Click
“Randomized Split” –> No/Leave ( Defaults to Round-Robin)
Refrences
Use the Split data into partitions option when you want to divide the dataset into subsets of the data. This option is also useful when you want to create a custom number of folds for cross-validation, or to split rows into several groups.
- Add the Partition and Sample module to your experiment in Studio (classic), and connect the dataset.
- For Partition or sample mode, select ‘Assign to Folds’.
- ‘Use replacement in the partitioning’: Select this option if you want the sampled row to be put back into the pool of rows for potential reuse. As a result, the same row might be assigned to several folds. If you ** do not use replacement (the default option) **, the sampled row is not put back into the pool of rows for potential reuse. As a result, each row can be assigned to only one fold.
- “Randomized split”: Select this option if you want rows to be randomly assigned to folds. ** If you do not select this option, rows are assigned to folds using the round-robin method **
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/partition-and-sample
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
Correct Answer: ABD
–
A. Penalize the classification –> Add a weight column
B. Resample the dataset using undersampling or oversampling –> . Resampling
Note: Use a performance metric that deals better with imbalanced data. For example, the F1 score
https://docs.microsoft.com/en-us/azure/machine-learning/concept-manage-ml-pitfalls#handle-imbalanced-data
–
The best way to prevent over-fitting is to follow ML best-practices including:
Using more training data, and eliminating statistical bias
Preventing target leakage
Using fewer features
Regularization and hyperparameter optimization
Model complexity limitations
Cross-validation
https://docs.microsoft.com/en-us/azure/machine-learning/concept-manage-ml-pitfalls#handle-imbalanced-data
A: Try Penalized Models - You can use the same algorithms but give them a different perspective on the problem. Penalized classification imposes an additional cost on the model for making classification mistakes on the minority class during training. These penalties can bias the model to pay more attention to the minority class.
B: You can change the dataset that you use to build your predictive model to have more balanced data.
This change is called sampling your dataset and there are two main methods that you can use to even-up the classes:
✑ Consider testing undersampling when you have an a lot data (tens- or hundreds of thousands of instances or more)
✑ Consider testing over-sampling when you don’t have a lot of data (tens of thousands of records or less)
D: Try Generate Synthetic Samples
A simple way to generate synthetic samples is to randomly sample the attributes from instances in the minority class.
References:
https://machinelearningmastery.com/tactics-to-combat-imbalanced-classes-in-your-machine-learning-dataset/
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.
Correct Answer: BC
Regularization (e.g., L1/L2) is a process … to prevent overfitting.
… providing a convolutional network with more training examples can reduce overfitting
https://en.wikipedia.org/wiki/Convolutional_neural_network
Correct Answer: BD
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.
D: 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.
References:
https://machinelearningmastery.com/how-to-reduce-overfitting-in-deep-learning-with-weight-regularization/ https://en.wikipedia.org/wiki/Convolutional_neural_network
You are working 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
Correct Answer: C
Relative Expression Split: Use this option whenever you want to apply a condition to a number column. The number could be a ** date/time field **, a column containing age or dollar amounts, or even a percentage. For example, you might want to divide your data set depending on the cost of the items, group people by age ranges, or separate data by a calendar date.
Incorrect Answers:
B: Regular Expression Split: Choose this option when you want to divide your dataset by testing a single column for a value.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/split-data
