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.

Reduce the amount of training data.

Add an additional dense layer with 512 input units.

Use training data augmentation.

Reduce the amount of training data.

Add an additional dense layer with 64 input units.

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.

Resample the dataset using undersampling or oversampling

Generate synthetic samples in the minority class

Resample the dataset using undersampling or oversampling

Normalize the training feature set

Generate synthetic samples in the minority class

Use accuracy as the evaluation metric of the model

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.

Microsoft Hardware-Assisted Virtualization Detection Tool

Your team is building a data engineering and data science development environment.The environment must support the following requirements:support Python and Scalacompose data storage, movement, and processing services into automated data pipelinesthe same tool should be used for the orchestration of both data engineering and data sciencesupport workload isolation and interactive workloadsenable scaling across a cluster of machinesYou need to create the environment.What should you do?

Build the environment in Azure Databricks and use Azure Data Factory for orchestration.

Build the environment in Apache Hive for HDInsight and use Azure Data Factory for orchestration.

Build the environment in Azure Databricks and use Azure Data Factory for orchestration.

Build the environment in Apache Spark for HDInsight and use Azure Container Instances for orchestration.

Build the environment in Azure Databricks and use Azure Container Instances for orchestration.

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?

Create a Data Science Virtual Machine (DSVM) Windows edition.

Create a Geo Al Data Science Virtual Machine (Geo-DSVM) Windows edition.

Create a Deep Learning Virtual Machine (DLVM) Linux edition.

Create a Deep Learning Virtual Machine (DLVM) Windows edition.

You are developing deep learning models to analyze semi-structured, unstructured, and structured data types.You have the following data available for model building:Video recordings of sporting eventsTranscripts of radio commentary about eventsLogs from related social media feeds captured during sporting eventsYou need to select an environment for creating the model.Which environment should you use?

Azure HDInsight with Spark MLib

You plan to use a Deep Learning Virtual Machine (DLVM) to train deep learning models using Compute Unified Device Architecture (CUDA) computations.You need to configure the DLVM to support CUDA.What should you implement?

Computer Processing Unit (CPU) speed increase by using overclocking

High Random Access Memory (RAM) configuration

Intel Software Guard Extensions (Intel SGX) technology

You plan to use a Data Science Virtual Machine (DSVM) with the open source deep learning frameworks Caffe2 and PyTorch.You need to select a pre-configured DSVM to support the frameworks.What should you create?

Data Science Virtual Machine for Linux (Ubuntu)

Data Science Virtual Machine for Windows 2012

Data Science Virtual Machine for Linux (CentOS)

Geo AI Data Science Virtual Machine with ArcGIS

Data Science Virtual Machine for Windows 2016

Data Science Virtual Machine for Linux (Ubuntu)

You are solving a classification task.The dataset is imbalanced.You need to select an Azure Machine Learning Studio module to improve the classification accuracy.Which module should you use?

Synthetic Minority Oversampling Technique (SMOTE)

Fisher Linear Discriminant Analysis

Synthetic Minority Oversampling Technique (SMOTE)

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.

Create a Compute Instance and use it to run code in Jupyter notebooks.

Create an Azure Kubernetes Service (AKS) inference cluster.

Create a tabular dataset that supports versioning.

Create a Compute Instance and use it to run code in Jupyter notebooks.

Create an Azure Kubernetes Service (AKS) inference cluster.

Use the designer to train a model by dragging and dropping pre-defined modules.

Create a tabular dataset that supports versioning.

Use the Automated Machine Learning user interface to train a model.

A set of CSV files contains sales records. All the CSV files have the same data schema.Each CSV file contains the sales record for a particular month and has the filename sales.csv. Each file in stored in a folder that indicates the month and year when the data was recorded. The folders are in an Azure blob container for which a datastore has been defined in an Azure Machine Learning workspace. The folders are organized in a parent folder named sales to create the following hierarchical structure: At the end of each month, a new folder with that month's sales file is added to the sales folder.You plan to use the sales data to train a machine learning model based on the following requirements:You must define a dataset that loads all of the sales data to date into a structure that can be easily converted to a dataframe.You must be able to create experiments that use only data that was created before a specific previous month, ignoring any data that was added after that month.You must register the minimum number of datasets possible.You need to register the sales data as a dataset in Azure Machine Learning service workspace.What should you do?

Create a tabular dataset that references the datastore and specifies the path 'sales/*/sales.csv', register the dataset with the name sales_dataset and a tag named month indicating the month and year it was registered, and use this dataset for all experiments.

Create a tabular dataset that references the datastore and explicitly specifies each 'sales/mm-yyyy/sales.csv' file every month. Register the dataset with the name sales_dataset each month, replacing the existing dataset and specifying a tag named month indicating the month and year it was registered. Use this dataset for all experiments.

Create a tabular dataset that references the datastore and specifies the path 'sales/*/sales.csv', register the dataset with the name sales_dataset and a tag named month indicating the month and year it was registered, and use this dataset for all experiments.

Create a new tabular dataset that references the datastore and explicitly specifies each 'sales/mm-yyyy/sales.csv' file every month. Register the dataset with the name sales_dataset_MM-YYYY each month with appropriate MM and YYYY values for the month and year. Use the appropriate month-specific dataset for experiments.

Create a tabular dataset that references the datastore and explicitly specifies each 'sales/mm-yyyy/sales.csv' file. Register the dataset with the name sales_dataset each month as a new version and with a tag named month indicating the month and year it was registered. Use this dataset for all experiments, identifying the version to be used based on the month tag as necessary.

You are creating a new experiment in Azure Machine Learning Studio. You have a small dataset that has missing values in many columns. The data does not require the application of predictors for each column. You plan to use the CleanMissing Data.You need to select a data cleaning method.Which method should you use?

Replace using Probabilistic PCA

Synthetic Minority Oversampling Technique (SMOTE)

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