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You have been asked to build a model using a dataset that is stored in a medium-sized (~10 GB) BigQuery table. You need to quickly determine whether this data is suitable for model development. You want to create a one-time report that includes both informative visualizations of data distributions and more sophisticated statistical analyses to share with other ML engineers on your team. You require maximum flexibility to create your report. What should you do?

A.

Use Vertex AI Workbench user-managed notebooks to generate the report.

B.

Use the Google Data Studio to create the report.

C.

Use the output from TensorFlow Data Validation on Dataflow to generate the report.

D.

Use Dataprep to create the report.

You built a deep learning-based image classification model by using on-premises data. You want to use Vertex Al to deploy the model to production Due to security concerns you cannot move your data to the cloud. You are aware that the input data distribution might change over time You need to detect model performance changes in production. What should you do?

A.

Use Vertex Explainable Al for model explainability Configure feature-based explanations.

B.

Use Vertex Explainable Al for model explainability Configure example-based explanations.

C.

Create a Vertex Al Model Monitoring job. Enable training-serving skew detection for your model.

D.

Create a Vertex Al Model Monitoring job. Enable feature attribution skew and dnft detection for your model.

You work for a bank with strict data governance requirements. You recently implemented a custom model to detect fraudulent transactions You want your training code to download internal data by using an API endpoint hosted in your projects network You need the data to be accessed in the most secure way, while mitigating the risk of data exfiltration. What should you do?

A.

Enable VPC Service Controls for peering’s, and add Vertex Al to a service perimeter

B.

Create a Cloud Run endpoint as a proxy to the data Use Identity and Access Management (1AM)

authentication to secure access to the endpoint from the training job.

C.

Configure VPC Peering with Vertex Al and specify the network of the training job

D.

Download the data to a Cloud Storage bucket before calling the training job

You are creating a deep neural network classification model using a dataset with categorical input values. Certain columns have a cardinality greater than 10,000 unique values. How should you encode these categorical values as input into the model?

A.

Convert each categorical value into an integer value.

B.

Convert the categorical string data to one-hot hash buckets.

C.

Map the categorical variables into a vector of boolean values.

D.

Convert each categorical value into a run-length encoded string.

You work with a learn of researchers lo develop state-of-the-art algorithms for financial analysis. Your team develops and debugs complex models in TensorFlow. You want to maintain the ease of debugging while also reducing the model training time. How should you set up your training environment?

A.

Configure a v3-8 TPU VM.

B.

Configure a v3-8 TPU node.

C.

Configure a c2-standard-60 VM without GPUs.

D, Configure a n1-standard-4 VM with 1 NVIDIA P100 GPU.

You have a custom job that runs on Vertex Al on a weekly basis The job is Implemented using a proprietary ML workflow that produces the datasets. models, and custom artifacts, and sends them to a Cloud Storage bucket Many different versions of the datasets and models were created Due to compliance requirements, your company needs to track which model was used for making a particular prediction, and needs access to the artifacts for each model. How should you configure your workflows to meet these requirement?

A.

Configure a TensorFlow Extended (TFX) ML Metadata database, and use the ML Metadata API.

B.

Create a Vertex Al experiment, and enable autologging inside the custom job

C.

Use the Vertex Al Metadata API inside the custom Job to create context, execution, and artifacts for each model, and use events to link them together.

D.

Register each model in Vertex Al Model Registry, and use model labels to store the related dataset and model information.

Your team has been tasked with creating an ML solution in Google Cloud to classify support requests for one of your platforms. You analyzed the requirements and decided to use TensorFlow to build the classifier so that you have full control of the model's code, serving, and deployment. You will use Kubeflow pipelines for the ML platform. To save time, you want to build on existing resources and use managed services instead of building a completely new model. How should you build the classifier?

A.

Use the Natural Language API to classify support requests

B.

Use AutoML Natural Language to build the support requests classifier

C.

Use an established text classification model on Al Platform to perform transfer learning

D.

Use an established text classification model on Al Platform as-is to classify support requests

You work for a retail company. You have been tasked with building a model to determine the probability of churn for each customer. You need the predictions to be interpretable so the results can be used to develop marketing campaigns that target at-risk customers. What should you do?

A.

Build a random forest regression model in a Vertex Al Workbench notebook instance Configure the model to generate feature importance’s after the model is trained.

B.

Build an AutoML tabular regression model Configure the model to generate explanations when it makes predictions.

C.

Build a custom TensorFlow neural network by using Vertex Al custom training Configure the model to generate explanations when it makes predictions.

D.

Build a random forest classification model in a Vertex Al Workbench notebook instance Configure the model to generate feature importance’s after the model is trained.

You are creating a model training pipeline to predict sentiment scores from text-based product reviews. You want to have control over how the model parameters are tuned, and you will deploy the model to an endpoint after it has been trained You will use Vertex Al Pipelines to run the pipeline You need to decide which Google Cloud pipeline components to use What components should you choose?

A.

B.

C.

D.

You are developing a custom TensorFlow classification model based on tabular data. Your raw data is stored in BigQuery contains hundreds of millions of rows, and includes both categorical and numerical features. You need to use a MaxMin scaler on some numerical features, and apply a one-hot encoding to some categorical features such as SKU names. Your model will be trained over multiple epochs. You want to minimize the effort and cost of your solution. What should you do?

A.

1 Write a SQL query to create a separate lookup table to scale the numerical features.

2. Deploy a TensorFlow-based model from Hugging Face to BigQuery to encode the text features.

3. Feed the resulting BigQuery view into Vertex Al Training.

B.

1 Use BigQuery to scale the numerical features.

2. Feed the features into Vertex Al Training.

3 Allow TensorFlow to perform the one-hot text encoding.

C.

1 Use TFX components with Dataflow to encode the text features and scale the numerical features.

2 Export results to Cloud Storage as TFRecords.

3 Feed the data into Vertex Al Training.

D.

1 Write a SQL query to create a separate lookup table to scale the numerical features.

2 Perform the one-hot text encoding in BigQuery.

3. Feed the resulting BigQuery view into Vertex Al Training.