Azure Exam Question 1

True is correct because both a pipeline created with the visual designer and an Automated Machine Learning experiment run as jobs when you submit them.

When you submit a visual designer pipeline it runs as a pipeline job that is tracked by the Azure Machine Learning service and can target compute, capture outputs, and be monitored like other jobs. Automated Machine Learning experiments also run as jobs and they create tracked runs that record metrics, models, and artifacts for review and deployment.

False is incorrect because the statement is accurate and both submission types are executed and tracked as jobs rather than as simple one off tasks without job metadata.

Cameron’s DP-100 Data Science Certification Exam Tip

When a question mentions you submit or run work in Azure Machine Learning think about whether the platform treats them as jobs. That mapping usually tells you if designer pipelines and Automated ML produce tracked runs.

Azure Exam Question 2

Match the VM machine type memory CPU GPU count and disk size to the workload and account for cost trade offs is the correct option.

This choice is correct because provisioning a VM that aligns with your experiment workload ensures you have enough CPU and memory for preprocessing and training and the right GPU type and count for model acceleration. It also means sizing disk capacity and I O performance to match dataset size and training checkpoint frequency while balancing cost so you do not overpay for unused capacity. Start with a reasonable estimate based on profiling, monitor resource utilization during runs, and iterate so that the VM meets performance targets without unnecessary expense.

Use default VM settings and avoid any custom tuning is wrong because default settings are generic and often underprovisioned or improperly balanced for tasks that need GPUs or heavy I O. Relying on defaults can lead to slow experiments or unexpected failures when the workload demands more specialized resources.

Choose a high end VM without regard to cost or precise resource needs is wrong because blindly selecting the largest instance wastes budget and may still not match the right resource profile for your workload. It is better to size to needs and scale up only when monitoring shows a bottleneck.

Use preemptible or spot VMs for batch runs and attach SSD persistent disks is wrong as a general essential step because while preemptible or spot VMs can reduce cost for tolerant batch jobs, they are interruptible and require checkpointing and retry logic. Attaching SSD persistent disks can help I O but does not eliminate the risks of preemption and is not universally appropriate for all experiments.

Azure Exam Question 3

[A] Jupyter Notebook and [B] JupyterLab are correct.

Compute Instances in Azure Machine Learning provide managed development VMs that include installations of Jupyter Notebook and JupyterLab so practitioners can write and run code and use the Azure Machine Learning SDK to access workspace assets such as datasets, models, and experiments.

[A] Dataverse and [B] IoT Hub is incorrect because Dataverse is a data platform and IoT Hub is a service for device messaging and neither one is a local interactive development environment or notebook installation on a compute instance.

[A] Cloud Shell Editor and [B] Cloud Code is incorrect because these are tooling options for command line and IDE integration and they are not the preinstalled notebook interfaces provided by Azure Machine Learning compute instances.

[A] Anaconda Navigator and [B] RStudio is incorrect because Anaconda Navigator is a desktop GUI for package and environment management and RStudio is an IDE for R which is not the default notebook interface shipped on Azure ML compute instances, although you can configure custom images if you need other tools.

Cameron’s DP-100 Data Science Certification Exam Tip

When a question mentions compute instances look for answers that reference interactive notebook environments and rule out items that are platform services or tooling plugins rather than built in notebook interfaces.

Azure Exam Question 4

The correct option is mini_batch_size.

mini_batch_size specifies how many input records are grouped into each mini batch for a batch scoring run, and setting it to 90 guarantees that each execution receives 90 records to process. This parameter is used by batch inference and parallel run configurations to control the unit of work handed to the scoring code so it is the right place to set a fixed per-run record count.

instance_count controls how many compute instances are allocated to the job and it does not guarantee how many records each execution will handle. Adjusting instance_count changes parallelism but not the per-execution batch size.

output_action determines how results are returned or stored and it does not set the number of records processed per run. That option affects output handling rather than the size of each mini batch.

scoring_script is the code that processes incoming records and it defines the processing logic but it is not the orchestration parameter that fixes how many records are passed in each run. You still need to set mini_batch_size to control the number of records delivered to the script.

Azure Exam Question 5

The correct option is Enable Azure Application Insights for the service endpoint and review telemetry in the Azure portal.

Azure Application Insights is purpose built to collect request and response telemetry for web and API endpoints and it integrates with Azure services so you can view live metrics and traces in the portal with minimal operational overhead. You can enable automatic request collection and add lightweight custom telemetry to capture input payloads and model outputs while using sampling and other controls to keep performance impact low. The portal gives you built in tools to query and visualize telemetry and you can forward data to other stores if you need longer retention or advanced analytics.

Configure an MLflow tracking server that targets the endpoint and inspect the logged runs is not the best choice because MLflow is designed for experiment tracking and model lifecycle metadata rather than lightweight production request and response telemetry. Running a dedicated MLflow tracking server also adds infrastructure and operational work that contradicts the requirement to keep overhead to a minimum.

Send metrics and logs to Azure Monitor and a Log Analytics workspace for the deployment is technically possible but it usually requires more custom plumbing to capture full request and response payloads and to correlate traces. This approach can increase operational complexity compared to enabling Application Insights which is already instrumented to collect endpoint telemetry and integrate with the portal.

Examine the registered model explanations in Azure Machine Learning studio is not appropriate because registered explanations provide interpretability artifacts and not a continuous capture of runtime input payloads and model predictions. Those artifacts are useful for understanding model behavior but they do not replace real time telemetry for a mission critical inference endpoint.

Azure Exam Question 6

The correct options are The Docker base image and Python interpreter version and library list.

The Docker base image is part of an Azure Machine Learning environment because it defines the container image that provides the underlying operating system layer and system packages used during training and inference.

Python interpreter version and library list are part of the environment because environments capture language runtimes and dependency manifests so experiments and deployed models run with consistent packages and versions.

Azure Kubernetes Service cluster is incorrect because it is a compute or deployment target where workloads run rather than a description of software dependencies and runtime.

A compute target such as a virtual machine size is incorrect because it specifies the hardware or resource allocation for training or inference and not the environment that defines packages or base images.

Azure Exam Question 7

The correct option is One-hot encoding.

One-hot encoding creates a separate binary feature for each size so the model does not assume any numeric ordering or spacing between categories. This works well for a small set of distinct labels like XXS, XS, S, M, and L because it preserves category identity without introducing artificial numeric relationships.

Target encoding is not appropriate because it replaces categories with statistics derived from the target and can leak information and cause overfitting, and it is mainly used for very high cardinality features.

Standardization is not suitable because it rescales continuous numeric features to zero mean and unit variance and does not convert categorical labels into usable numeric features.

Ordinal encoding is not the best choice here because it assigns integer values that impose an order and assume equal spacing between sizes, which can mislead models unless you have a validated numeric scale for the differences between sizes.

Normalization is also inappropriate because it rescales numeric vectors to a fixed norm and does not provide a method to encode categorical labels into distinct numeric features.

Cameron’s DP-100 Data Science Certification Exam Tip

When a categorical feature has a few distinct labels and no reliable numeric spacing, prefer one-hot encoding. Reserve ordinal encoding for cases where an ordered feature has meaningful and comparable numeric gaps.

Azure Exam Question 8

The correct option is Generate predictions.

Generate predictions means applying a trained model and the same preprocessing steps to new input records so the model can produce label values or scores for those records. This step is commonly called scoring or inference and it is exactly what a production scoring workflow performs.

Measure correlation refers to quantifying relationships between variables and not to using a trained model to assign labels, so it is incorrect.

Compute a sum describes a basic arithmetic aggregation and not the process of running a model to produce predictions, so it is incorrect.

Make an estimate is an informal phrase that could loosely describe prediction in everyday language but it is not the precise machine learning term the question asks for, so it is incorrect.

Calculate an average is a statistical aggregation operation and does not describe model inference or scoring, so it is incorrect.

Azure Exam Question 9

The correct option is Import Data.

The Import Data module in Azure Machine Learning Designer is designed to pull data directly from external sources and it can read a CSV hosted on a public website with minimal setup. You can drop the Import Data module into your pipeline, configure the HTTP or HTTPS URL and format settings, and the module outputs data that downstream components can consume.

Convert CSV to Dataset is not the standard Designer module name for ingesting a remote CSV and it does not describe the built in module that reads from a web URL, so it is not the correct choice.

Create Dataset from Files refers to creating and registering a dataset in the workspace from file storage or uploads and it usually requires selecting storage or uploading files outside the pipeline, so it is not the minimal inline import from a public URL.

Enter Data Manually is for small manual tables entered directly in the interface and it is not appropriate for fetching a CSV file from a public website.

Cameron’s DP-100 Data Science Certification Exam Tip

When a question mentions ingesting a CSV from a public URL in Designer look for the module that accepts a web address and outputs a dataset. Import Data is the module that does this with the least configuration.

Azure Exam Question 10

The correct options are Regression and Classification.

Regression is correct because AutoML automates the selection and hyperparameter tuning of models that predict continuous numeric targets and it evaluates models using regression metrics while trying algorithms such as linear models and tree ensembles.

Classification is correct because AutoML also handles predicting categorical labels and it evaluates and optimizes classifiers using metrics like accuracy and AUC while testing a range of classification algorithms.

Clustering is not correct because clustering is an unsupervised grouping task that does not use labeled targets and it is not the focus of a supervised AutoML training workflow.

Dimensionality reduction is not correct because dimensionality reduction is a preprocessing or feature engineering technique rather than a target predictive task that AutoML selects as the model objective.

Time series forecasting is not correct for this training task because forecasting is a specialized scenario that requires different setup and was not included among the target tasks for this AutoML workflow.

Azure Exam Question 11

Apache Parquet is the correct choice for maximizing processing efficiency in Azure Machine Learning.

Apache Parquet is a columnar file format and it reduces I O by reading only the columns that are needed instead of whole rows. It supports efficient compression and encoding schemes which lowers storage size and speeds up data transfer which is important for files larger than 3 GB. The format is also splittable which enables parallel reads by distributed compute engines and that improves throughput for large scale machine learning workflows.

TFRecords is optimized for TensorFlow sequential record consumption and it is not a columnar, schema rich format. It can be efficient for TensorFlow training pipelines but it does not provide the same column pruning and wide ecosystem support for analytics engines as Parquet.

XLSX is a spreadsheet format that is not designed for large scale distributed processing. It has significant parsing overhead and it is not splittable which makes it unsuitable for multi node reads of multi gigabyte files.

CSV is a simple row based text format and it lacks an explicit schema and efficient columnar storage. CSV files often require more I O to scan and more CPU to parse which slows distributed processing compared with a compressed, columnar format like Parquet.

Azure Exam Question 12

The correct option is Register the updated files using the same dataset name as the previously registered dataset.

Registering updated files under the same dataset name causes Azure Machine Learning to create a new version of the dataset while preserving prior versions. The registry records the data paths and metadata and increments the dataset version so you can reference the specific version used in experiments and reproduce results.

Datasets will version automatically on a schedule that you configure is incorrect because Azure Machine Learning does not provide built in scheduled versioning. You can automate registration with scripts or pipelines to mimic a schedule but the service only creates a new version at registration time.

Start a new training experiment that references the prior dataset and save the output as a separate dataset is incorrect because running training that uses an existing dataset does not produce a new version of that dataset. The outputs of a run are separate artifacts and do not increment the original dataset version unless you explicitly register the updated files with the same dataset name.

Load the updated data during a run and then register it as a dataset is misleading and therefore incorrect in this context because simply loading data in a run does not by itself version the prior dataset. You must explicitly register the updated files using the same dataset name if you want a new version to be recorded in the dataset registry.

Azure Exam Question 13

The correct answer is Regression model. A Regression model predicts continuous numeric outcomes and it is the best fit for forecasting a yearly harvest quantity from inputs such as rainfall totals soil nutrient indices and daily sunlight hours.

Regression model is a supervised learning approach that learns the relationship between input features and a continuous target. Common algorithms include linear regression decision tree regression and gradient boosted trees which can model linear and non linear effects in the data to produce a numeric prediction for harvest size.

Classification model is designed to assign discrete labels or categories rather than predict a continuous numeric value so it is not appropriate for estimating a harvest quantity.

Reinforcement learning model is meant for agents that learn by taking actions and receiving rewards in an environment. It is not the standard approach for supervised forecasting from historical measurement data.

Unsupervised learning model is used to discover patterns or groupings in unlabeled data such as clusters or principal components. It does not directly produce a labeled numeric target like yearly harvest unless it is combined with a separate supervised method.

Azure Exam Question 14

The correct option is Local feature importance.

Local feature importance provides an explanation for a single prediction and shows how each feature contributed to that specific output. This type of explanation gives attribution scores for the instance you care about so you can see whether a particular feature increased or decreased the predicted value.

Global feature importance is incorrect because it summarizes feature effects across the whole dataset and does not tell you how a feature influenced one particular prediction.

Partial dependence plot is incorrect because it shows the average relationship between a feature and the prediction across many samples and it is not a per instance attribution method.

Label influence analysis is incorrect because it focuses on how training labels or training examples affect model behavior and not on attributing an individual prediction to its features.

Azure Exam Question 15

The correct answer is None of the listed statements is correct.

This is correct because the code uses GridParameterSampling with discrete choices for the parameters and not a continuous range. The parameter expressions call pe.choice(5,7,9,11) for max_depth and pe.choice(0.06,0.12,0.18) for learning_rate so the grid contains 4 times 3 which equals 12 distinct combinations. The None of the listed statements is correct option is accurate because none of the other statements properly reflect how HyperDriveConfig and GridParameterSampling behave.

The experiment will produce trials for every numeric value in the 0.006 to 0.18 range for learning_rate is wrong because the code specifies discrete choices of 0.06, 0.12, and 0.18. It does not define a continuous range and 0.006 is not one of the provided choices.

The run will perform 60 trials for this hyperparameter search is wrong because max_total_runs is an upper bound and not a guarantee of run count. Grid sampling here yields 12 combinations so at most 12 trials will be created unless other sampling or limits change that number.

They can assign a security policy to the policy argument of HyperDriveConfig is wrong because the policy parameter is intended for early termination policies such as BanditPolicy or None. Security controls are managed separately in Azure Machine Learning and are not passed to HyperDriveConfig as the trial termination policy.

Cameron’s DP-100 Data Science Certification Exam Tip

When evaluating hyperparameter tuning questions multiply the counts of discrete choices to get the total possible trials and remember that max_total_runs is only an upper bound and policy refers to early termination policies.

Azure Exam Question 16

The correct answer is Ensemble models.

Ensemble models refer to approaches that combine many base learners to produce better predictive performance than single models. The description in the question that these constructs are not just one decision tree but a large number of trees fits ensemble methods such as random forests and gradient boosted trees. These methods are widely used because they often deliver strong predictions on complex data sets by averaging or aggregating the outputs of many trees.

Vertex AI is incorrect because it is a Google Cloud platform and managed service for building and deploying machine learning models rather than a specific model family made up of many trees.

Least squares regression is incorrect because it is a linear estimation method that fits a single linear model to minimize squared errors and it does not consist of many decision trees.

Linear regression is incorrect because it is a single parametric model that assumes a linear relationship between inputs and outputs and it does not match the description of an ensemble of trees.

Azure Exam Question 17

The correct answer is The endpoint accepts a batch of records in a single request and the payload may be JSON or CSV.

This option is correct because online prediction endpoints are designed to accept multiple instances in one HTTP request so clients can send a batch of records for throughput and efficiency. The endpoints accept both JSON and CSV formatted payloads when the data follows the service request schema for instances or inputs.

The endpoint accepts a single record per request and the payload may be JSON or CSV is incorrect because the service allows a batch of records in a single request rather than being limited to one record per call.

The endpoint only accepts multiple records in a single call and the body must be JSON is incorrect because although multiple records are accepted the payload is not restricted to JSON and CSV is also supported when formatted correctly.

The endpoint supports only one record per request and the body must be JSON is incorrect because the endpoint is not limited to a single record and it also accepts CSV formatted inputs in addition to JSON when following the expected request structure.

Cameron’s DP-100 Data Science Certification Exam Tip

When testing online prediction endpoints send a batch of instances when possible and verify the exact request body format in the docs. Confirm whether the service expects JSON arrays or newline separated CSV rows before sending requests.

Azure Exam Question 18

Enable Application Insights and associate it with the workspace and the deployed service is the correct choice.

Enabling Application Insights and associating it with the workspace and the deployed service lets Azure Machine Learning collect request level telemetry for the AKS hosted online endpoint. This captures request rates, latency, exceptions, and application logs and it lets you query traces and metrics in the portal for troubleshooting and performance analysis.

Application Insights integrates with the deployed service and the workspace so telemetry is correlated with the model deployment and you can view both high level metrics and detailed request traces without changing the cluster type.

Enable Azure Monitor for containers is incorrect because that feature focuses on node and container resource metrics and pod level performance rather than request level inference telemetry. It is useful for infrastructure monitoring but it does not provide the detailed application traces and request logs that Application Insights provides for model endpoints.

Redeploy the model to Azure Container Instances is incorrect because moving the deployment to ACI is not required to capture telemetry. ACI is typically used for testing or low scale scenarios and it does not substitute for enabling application level telemetry on the deployed service.

Move the AKS cluster into the same region as the Azure Machine Learning workspace is incorrect because relocating the cluster is unnecessary for telemetry collection. Application Insights and workspace association work across regions and there is no need to move AKS to collect inference logs and metrics.

Azure Exam Question 19

The correct answer is Unsupervised learning.

K-Means is a clustering algorithm that groups examples based on similarity without using labeled outputs. Because it discovers structure from unlabeled data, it is an instance of Unsupervised learning. Clustering methods like K-Means aim to partition data into cohesive groups and they do not require ground truth labels during training.

Reinforcement learning is about an agent learning to take actions to maximize cumulative rewards over time. It is not a clustering technique and does not describe what K-Means does, so it is incorrect.

K Nearest Neighbors is an instance based supervised method used for classification and regression that relies on labeled examples. It is not a clustering algorithm and therefore it is not the right category for K-Means.

Supervised learning involves learning a mapping from inputs to known outputs using labeled training data. Since K-Means does not use labels during training, it does not fall under supervised learning and that option is incorrect.

Cameron’s DP-100 Data Science Certification Exam Tip

When a question mentions grouping or clustering of data without labeled outputs look for unsupervised. If the problem refers to known labels look for supervised and if it mentions agents, actions, or rewards think reinforcement.

Azure Exam Question 20

The correct option is Register the model in the workspace model registry.

Register the model in the workspace model registry is the right choice because it stores model artifacts and the associated metadata while creating explicit versioned records inside the Azure Machine Learning workspace. This approach supports reproducibility and lets Dr. Patel track different iterations, attach tags and descriptions, and reference specific versions when promoting or deploying models.

Deploy the trained model to an endpoint is incorrect. Deployment publishes a model for serving and does not by itself catalog or version experiment artifacts inside the workspace. You can deploy from a registered model but deployment is not the mechanism for persisting different experiment versions.

Use child runs to organize experiment trials is incorrect. Child runs help structure and compare trials and they record metrics and outputs but they do not provide a central, versioned model registry for persistent artifact cataloging and lifecycle management.

Enable Application Insights telemetry is incorrect. Application Insights collects telemetry and monitoring data for deployed services and it does not store model artifacts or provide versioned model management inside the workspace.

Azure Exam Question 21

The correct option is Apply L1 and L2 penalty terms during training and augment the training images.

L1 and L2 penalties act as weight regularizers that constrain model complexity and reduce the tendency to memorize training noise. L1 promotes sparsity and L2 discourages large weights so they help the model generalize better by limiting capacity in a principled way. Image augmentation increases the effective size and diversity of the training set so the model sees more varied examples and learns more robust features.

Using both regularization and augmentation addresses overfitting from two angles because the penalties control complexity and augmentation reduces variance by broadening the data distribution.

Add dropout layers and enable batch normalization is not the best choice because dropout and batch normalization can help in some cases but they do not replace explicit data augmentation and targeted weight regularization. Batch normalization mainly stabilizes learning and dropout can interact poorly with convolutional feature maps if applied without care.

Perform transfer learning with a pretrained backbone and freeze most layers is not correct because freezing most layers limits the model’s ability to adapt to the new dataset and does not directly solve overfitting on the validation set. Transfer learning can help when data are very limited but it should be combined with augmentation and regularization to prevent overfitting.

Increase the network capacity by adding a 1024 neuron dense layer and reduce the number of training examples is wrong because increasing model capacity while reducing training data will make overfitting worse. Larger networks can memorize the training set and fewer examples increase variance and degrade validation performance.

Cameron’s DP-100 Data Science Certification Exam Tip

When a question asks how to reduce overfitting look for choices that either expand the effective training data or constrain model complexity and be cautious about answers that increase capacity or reduce data.

Azure Exam Question 22

The correct option is Use SMOTE sampling mode.

Use SMOTE sampling mode creates synthetic examples of the minority class so the model can learn its patterns more effectively during training. It balances the training data without discarding majority examples and it helps reduce bias toward the majority class which improves recall for rare labels when compared to no resampling.

Stratified split sampling preserves the original class proportions when creating training and test sets so it maintains the imbalance rather than fixing it. It is useful for fair evaluation but it does not address the need to increase minority class representation for training.

Random undersampling can balance classes by removing majority examples but it discards information and can hurt model performance when the majority class contains useful variety. It is a valid technique in some situations but it is not as appropriate as SMOTE when you want to augment the minority class.

Random split sampling simply splits the dataset without regard to class distribution and does not solve class imbalance. It risks producing training sets with even fewer minority examples and it does not generate new minority samples.

Azure Exam Question 23

The correct options are Use the RunDetails widget in a notebook by running RunDetails(pipeline_run).show() and Call pipeline_run.wait_for_completion(show_output=True) and watch the console output.

The Use the RunDetails widget in a notebook by running RunDetails(pipeline_run).show() option is correct because the RunDetails widget provides an interactive view in notebooks that shows pipeline and step status, linked logs, and metrics. It is designed for quick visual monitoring while a pipeline run is active and it updates as the run progresses.

The Call pipeline_run.wait_for_completion(show_output=True) and watch the console output option is correct because the PipelineRun class exposes a wait_for_completion method that blocks until the run finishes and streams run output and logging to the console when show_output is set to True. This is a simple and useful way to follow progress from a script or terminal.

Check metrics and logs from the Kubernetes cluster in Azure Monitor is incorrect because Azure Machine Learning pipeline runs are monitored through the Azure ML run APIs, widgets, and the Studio experience. The cluster level metrics in Azure Monitor do not provide the step level run details and linked logs that the AML run monitoring surfaces.

Open the Inference Clusters tab in Machine Learning Designer is incorrect because the Machine Learning Designer inference clusters view is not the place to observe Azure ML pipeline execution. Designer is a different authoring tool and the Inference Clusters tab does not show pipeline run progress or step logs for SDK started pipelines.

Cameron’s DP-100 Data Science Certification Exam Tip

When you see questions about observing pipeline progress think about the SDK and notebook features that stream run information. The RunDetails widget and the wait_for_completion(show_output=True) call are the most direct ways to watch a pipeline from code or a notebook.

Azure Exam Question 24

The correct option is Call the fit method on the LinearRegression instance with the feature matrix and the target vector.

The fit method is the scikit learn estimator API call that trains the model by estimating coefficients from the provided feature matrix and target vector. Calling fit on a LinearRegression instance updates the model parameters so that it can later make predictions.

The option Call the predict method with the training feature matrix and the training labels is incorrect because predict is used to generate predictions from an already trained model and it does not update or train the model parameters.

The option Invoke the corr method on the model object and supply the feature and target arrays is incorrect because estimators do not provide a corr method for training. Correlation functions are data analysis operations and are not how scikit learn estimators learn parameters.

The option Call the score method on the estimator and pass the training feature matrix and the target array is incorrect because score evaluates a trained model by returning a performance metric such as R squared. It does not perform training or change model parameters.

Azure Exam Question 25

Open a terminal on the Azure Machine Learning compute instance is the correct action to take before cloning the repository.

You must open a shell on the compute instance because the repository will be cloned into that instance’s local file system and the clone command is executed from a terminal. Opening the terminal gives you direct access to run git clone, to check the working directory, and to perform any required local setup before pulling code.

Generate a new SSH key pair is not the immediate first step because you need a terminal to run the key generation command and to place the keys in the right location on the compute instance. Generating keys can be necessary later, but it is performed from the compute instance terminal.

Launch an Azure Cloud Shell session is incorrect because Cloud Shell runs in a separate ephemeral environment in Azure and does not operate directly on the compute instance’s local file system. Cloud Shell could be useful for other tasks, but it does not replace opening a terminal on the compute instance itself when you intend to clone into that instance.

Add the public SSH key to the remote Git hosting account is not the very first action because you must first access the compute instance to generate or locate the public key and then copy it. Also some repositories use HTTPS or personal access tokens instead of SSH, so adding a key may not be required at all before cloning.

Azure Exam Question 26

Randomized grid sweep mode is the correct choice for sampling many hyperparameter