Question 1

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?

Options :

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.

Answer: B

Question 2

You are developing a training pipeline for a new XGBoost classification model based on tabular data The data

is stored in a BigQuery table You need to complete the following steps

1. Randomly split the data into training and evaluation datasets in a 65/35 ratio

2. Conduct feature engineering

3 Obtain metrics for the evaluation dataset.

4 Compare models trained in different pipeline executions

How should you execute these steps'?

Options :

A : 1 Using Vertex Al Pipelines, add a component to divide the data into training and evaluation sets, and

add another component for feature engineering

2. Enable auto logging of metrics in the training component.

3 Compare pipeline runs in Vertex Al Experiments

B : 1 Using Vertex Al Pipelines, add a component to divide the data into training and evaluation sets, and

add another component for feature engineering

2 Enable autologging of metrics in the training component

3 Compare models using the artifacts lineage in Vertex ML Metadata

C : 1 In BigQuery ML. use the create model statement with bocstzd_tree_classifier as the model

type and use BigQuery to handle the data splits.

2 Use a SQL view to apply feature engineering and train the model using the data in that view

3. Compare the evaluation metrics of the models by using a SQL query with the ml. training_infc

statement.

D : 1 In BigQuery ML use the create model statement with boosted_tree_classifier as the model

type, and use BigQuery to handle the data splits.

2 Use ml transform to specify the feature engineering transformations, and train the model using the

data in the table

' 3. Compare the evaluation metrics of the models by using a SQL query with the ml. training_info

statement.

Answer: B

Question 3

You work for a retail company. You have a managed tabular dataset in Vertex Al that contains sales data from three different stores. The dataset includes several features such as store name and sale timestamp. You want to use the data to train a model that makes sales predictions for a new store that will open soon You need to split the data between the training, validation, and test sets What approach should you use to split the data?

Options :

A : Use Vertex Al manual split, using the store name feature to assign one store for each set.

B : Use Vertex Al default data split.

C : Use Vertex Al chronological split and specify the sales timestamp feature as the time vanable.

D :

Use Vertex Al random split assigning 70% of the rows to the training set, 10% to the validation set, and 20% to the test set.

Answer: B

Question 4

You are training an object detection machine learning model on a dataset that consists of three million X-ray images, each roughly 2 GB in size. You are using Vertex AI Training to run a custom training application on a Compute Engine instance with 32-cores, 128 GB of RAM, and 1 NVIDIA P100 GPU. You notice that model training is taking a very long time. You want to decrease training time without sacrificing model performance. What should you do?

Options :

A : Increase the instance memory to 512 GB, and increase the batch size.

B : Replace the NVIDIA P100 GPU with a K80 GPU in the training job.

C : Enable early stopping in your Vertex AI Training job.

D : Use the tf.distribute.Strategy API and run a distributed training job.

Answer: D

Question 5

You work for an organization that operates a streaming music service. You have a custom production model that is serving a "next song" recommendation based on a user’s recent listening history. Your model is deployed on a Vertex Al endpoint. You recently retrained the same model by using fresh data. The model received positive test results offline. You now want to test the new model in production while minimizing complexity. What should you do?

Options :

A :

Create a new Vertex Al endpoint for the new model and deploy the new model to that new endpoint Build a service to randomly send 5% of production traffic to the new endpoint Monitor end-user metrics such as listening time If end-user metrics improve between models over time gradually increase the percentage of production traffic sent to the new endpoint.

B :

Capture incoming prediction requests in BigQuery Create an experiment in Vertex Al Experiments Run batch predictions for both models using the captured data Use the user-s selected song to compare the models performance side by side If the new models performance metrics are better than the previous model deploy the new model to production

C :

Deploy the new model to the existing Vertex Al endpoint Use traffic splitting to send 5% of production traffic to the new model Monitor end-user metrics, such as listening time If end-user metrics improve between models over time, gradually increase the percentage of production traffic sent to the new model.

D :

Configure a model monitoring job for the existing Vertex Al endpoint. Configure the monitoring job to detect prediction drift, and set a threshold for alerts Update the model on the endpoint from the previous model to the new model If you receive an alert of prediction drift, revert to the previous model

Answer: C

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