Summer Special Limited Time 65% Discount Offer - Ends in 0d 00h 00m 00s - Coupon code: exc65

You work for a large hotel chain and have been asked to assist the marketing team in gathering predictions for a targeted marketing strategy. You need to make predictions about user lifetime value (LTV) over the next 30 days so that marketing can be adjusted accordingly. The customer dataset is in BigQuery, and you are preparing the tabular data for training with AutoML Tables. This data has a time signal that is spread across multiple columns. How should you ensure that AutoML fits the best model to your data?

A.

Manually combine all columns that contain a time signal into an array Allow AutoML to interpret this array appropriately

Choose an automatic data split across the training, validation, and testing sets

B.

Submit the data for training without performing any manual transformations Allow AutoML to handle the appropriate

transformations Choose an automatic data split across the training, validation, and testing sets

C.

Submit the data for training without performing any manual transformations, and indicate an appropriate column as the Time column Allow AutoML to split your data based on the time signal provided, and reserve the more recent data for the validation and testing sets

D.

Submit the data for training without performing any manual transformations Use the columns that have a time signal to manually split your data Ensure that the data in your validation set is from 30 days after the data in your training set and that the data in your testing set is from 30 days after your validation set

You are developing a custom image classification model in Python. You plan to run your training application on Vertex Al Your input dataset contains several hundred thousand small images You need to determine how to store and access the images for training. You want to maximize data throughput and minimize training time while reducing the amount of additional code. What should you do?

A.

Store image files in Cloud Storage and access them directly.

B.

Store image files in Cloud Storage and access them by using serialized records.

C.

Store image files in Cloud Filestore, and access them by using serialized records.

D.

Store image files in Cloud Filestore and access them directly by using an NFS mount point.

Your team is training a large number of ML models that use different algorithms, parameters and datasets. Some models are trained in Vertex Ai Pipelines, and some are trained on Vertex Al Workbench notebook instances. Your team wants to compare the performance of the models across both services. You want to minimize the effort required to store the parameters and metrics What should you do?

A.

Implement an additional step for all the models running in pipelines and notebooks to export parameters and metrics to BigQuery.

B.

Create a Vertex Al experiment Submit all the pipelines as experiment runs. For models trained on notebooks log parameters and metrics by using the Vertex Al SDK.

C.

Implement all models in Vertex Al Pipelines Create a Vertex Al experiment, and associate all pipeline runs with that experiment.

D.

Store all model parameters and metrics as mode! metadata by using the Vertex Al Metadata API.

You need to build an ML model for a social media application to predict whether a user’s submitted profile photo meets the requirements. The application will inform the user if the picture meets the requirements. How should you build a model to ensure that the application does not falsely accept a non-compliant picture?

A.

Use AutoML to optimize the model’s recall in order to minimize false negatives.

B.

Use AutoML to optimize the model’s F1 score in order to balance the accuracy of false positives and false negatives.

C.

Use Vertex AI Workbench user-managed notebooks to build a custom model that has three times as many examples of pictures that meet the profile photo requirements.

D.

Use Vertex AI Workbench user-managed notebooks to build a custom model that has three times as many examples of pictures that do not meet the profile photo requirements.

Your data science team is training a PyTorch model for image classification based on a pre-trained RestNet model. You need to perform hyperparameter tuning to optimize for several parameters. What should you do?

A.

Convert the model to a Keras model, and run a Keras Tuner job.

B.

Run a hyperparameter tuning job on AI Platform using custom containers.

C.

Create a Kuberflow Pipelines instance, and run a hyperparameter tuning job on Katib.

D.

Convert the model to a TensorFlow model, and run a hyperparameter tuning job on AI Platform.

You have developed an AutoML tabular classification model that identifies high-value customers who interact with your organization's website.

You plan to deploy the model to a new Vertex Al endpoint that will integrate with your website application. You expect higher traffic to the website during

nights and weekends. You need to configure the model endpoint's deployment settings to minimize latency and cost. What should you do?

A.

Configure the model deployment settings to use an n1-standard-32 machine type.

B.

Configure the model deployment settings to use an n1-standard-4 machine type. Set the minReplicaCount value to 1 and the maxReplicaCount value to 8.

C.

Configure the model deployment settings to use an n1-standard-4 machine type and a GPU accelerator. Set the minReplicaCount value to 1 and the maxReplicaCount value to 4.

D.

Configure the model deployment settings to use an n1-standard-8 machine type and a GPU accelerator.

As the lead ML Engineer for your company, you are responsible for building ML models to digitize scanned customer forms. You have developed a TensorFlow model that converts the scanned images into text and stores them in Cloud Storage. You need to use your ML model on the aggregated data collected at the end of each day with minimal manual intervention. What should you do?

A.

Use the batch prediction functionality of Al Platform

B.

Create a serving pipeline in Compute Engine for prediction

C.

Use Cloud Functions for prediction each time a new data point is ingested

D.

Deploy the model on Al Platform and create a version of it for online inference.

You need to design an architecture that serves asynchronous predictions to determine whether a particular mission-critical machine part will fail. Your system collects data from multiple sensors from the machine. You want to build a model that will predict a failure in the next N minutes, given the average of each sensor’s data from the past 12 hours. How should you design the architecture?

A.

1. HTTP requests are sent by the sensors to your ML model, which is deployed as a microservice and exposes a REST API for prediction

2. Your application queries a Vertex AI endpoint where you deployed your model.

3. Responses are received by the caller application as soon as the model produces the prediction.

B.

1. Events are sent by the sensors to Pub/Sub, consumed in real time, and processed by a Dataflow stream processing pipeline.

2. The pipeline invokes the model for prediction and sends the predictions to another Pub/Sub topic.

3. Pub/Sub messages containing predictions are then consumed by a downstream system for monitoring.

C.

1. Export your data to Cloud Storage using Dataflow.

2. Submit a Vertex AI batch prediction job that uses your trained model in Cloud Storage to perform scoring on the preprocessed data.

3. Export the batch prediction job outputs from Cloud Storage and import them into Cloud SQL.

D.

1. Export the data to Cloud Storage using the BigQuery command-line tool

2. Submit a Vertex AI batch prediction job that uses your trained model in Cloud Storage to perform scoring on the preprocessed data.

3. Export the batch prediction job outputs from Cloud Storage and import them into BigQuery.

You have been tasked with deploying prototype code to production. The feature engineering code is in PySpark and runs on Dataproc Serverless. The model training is executed by using a Vertex Al custom training job. The two steps are not connected, and the model training must currently be run manually after the feature engineering step finishes. You need to create a scalable and maintainable production process that runs end-to-end and tracks the connections between steps. What should you do?

A.

Create a Vertex Al Workbench notebook Use the notebook to submit the Dataproc Serverless feature engineering job Use the same notebook to submit the custom model training job Run the notebook cells sequentially to tie the steps together end-to-end

B.

Create a Vertex Al Workbench notebook Initiate an Apache Spark context in the notebook, and run the PySpark feature engineering code Use the same notebook to run the custom model training job in TensorFlow Run the notebook cells sequentially to tie the steps together end-to-end

C.

Use the Kubeflow pipelines SDK to write code that specifies two components

- The first is a Dataproc Serverless component that launches the feature engineering job

- The second is a custom component wrapped in the

creare_cusrora_rraining_job_from_ccraponent Utility that launches the custom model training

job.

D.

Create a Vertex Al Pipelines job to link and run both components Use the Kubeflow pipelines SDK to write code that specifies two components

- The first component initiates an Apache Spark context that runs the PySpark feature engineering code

- The second component runs the TensorFlow custom model training code Create a Vertex Al Pipelines job to link and run both components

You are training an object detection model using a Cloud TPU v2. Training time is taking longer than expected. Based on this simplified trace obtained with a Cloud TPU profile, what action should you take to decrease training time in a cost-efficient way?

A.

Move from Cloud TPU v2 to Cloud TPU v3 and increase batch size.

B.

Move from Cloud TPU v2 to 8 NVIDIA V100 GPUs and increase batch size.

C.

Rewrite your input function to resize and reshape the input images.

D.

Rewrite your input function using parallel reads, parallel processing, and prefetch.