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You work for a pet food company that manages an online forum Customers upload photos of their pets on the forum to share with others About 20 photos are uploaded daily You want to automatically and in near real time detect whether each uploaded photo has an animal You want to prioritize time and minimize cost of your application development and deployment What should you do?

A.

Send user-submitted images to the Cloud Vision API Use object localization to identify all objects in the image and compare the results against a list of animals.

B.

Download an object detection model from TensorFlow Hub. Deploy the model to a Vertex Al endpoint. Send new user-submitted images to the model endpoint to classify whether each photo has an animal.

C.

Manually label previously submitted images with bounding boxes around any animals Build an AutoML object detection model by using Vertex Al Deploy the model to a Vertex Al endpoint Send new user-submitted images to your model endpoint to detect whether each photo has an animal.

D.

Manually label previously submitted images as having animals or not Create an image dataset on Vertex Al Train a classification model by using Vertex AutoML to distinguish the two classes Deploy the model to a Vertex Al endpoint Send new user-submitted images to your model endpoint to classify whether each photo has an animal.

You are an ML engineer at a travel company. You have been researching customers’ travel behavior for many years, and you have deployed models that predict customers’ vacation patterns. You have observed that customers’ vacation destinations vary based on seasonality and holidays; however, these seasonal variations are similar across years. You want to quickly and easily store and compare the model versions and performance statistics across years. What should you do?

A.

Store the performance statistics in Cloud SQL. Query that database to compare the performance statistics across the model versions.

B.

Create versions of your models for each season per year in Vertex AI. Compare the performance statistics across the models in the Evaluate tab of the Vertex AI UI.

C.

Store the performance statistics of each pipeline run in Kubeflow under an experiment for each season per year. Compare the results across the experiments in the Kubeflow UI.

D.

Store the performance statistics of each version of your models using seasons and years as events in Vertex ML Metadata. Compare the results across the slices.

You work at a subscription-based company. You have trained an ensemble of trees and neural networks to predict customer churn, which is the likelihood that customers will not renew their yearly subscription. The average prediction is a 15% churn rate, but for a particular customer the model predicts that they are 70% likely to churn. The customer has a product usage history of 30%, is located in New York City, and became a customer in 1997. You need to explain the difference between the actual prediction, a 70% churn rate, and the average prediction. You want to use Vertex Explainable AI. What should you do?

A.

Train local surrogate models to explain individual predictions.

B.

Configure sampled Shapley explanations on Vertex Explainable AI.

C.

Configure integrated gradients explanations on Vertex Explainable AI.

D.

Measure the effect of each feature as the weight of the feature multiplied by the feature value.

You work with a team of researchers to 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 SSH into the VM to tram and debug the model.

B.

Configure a v3-8 TPU node Use Cloud Shell to SSH into the Host VM to train and debug the model.

C.

Configure a M-standard-4 VM with 4 NVIDIA P100 GPUs SSH into the VM and use

Parameter Server Strategy to train the model.

D.

Configure a M-standard-4 VM with 4 NVIDIA P100 GPUs SSH into the VM and use

MultiWorkerMirroredStrategy to train the model.

You are training a custom language model for your company using a large dataset. You plan to use the ReductionServer strategy on Vertex Al. You need to configure the worker pools of the distributed training job. What should you do?

A.

Configure the machines of the first two worker pools to have GPUs and to use a container image where your training code runs Configure the third worker pool to have GPUs: and use the reduction server container image.

B.

Configure the machines of the first two worker pools to have GPUs and to use a container image where your training code runs. Configure the third worker pool to use the reductionserver container image without accelerators, and choose a machine type that prioritizes bandwidth.

C.

Configure the machines of the first two worker pools to have TPUs and to use a container image where your training code runs Configure the third worker pool without accelerators, and use the reductionserver container image without accelerators and choose a machine type that prioritizes bandwidth.

D.

Configure the machines of the first two pools to have TPUs. and to use a container image where your training code runs Configure the third pool to have TPUs: and use the reductionserver container image.

You are developing an ML pipeline using Vertex Al Pipelines. You want your pipeline to upload a new version of the XGBoost model to Vertex Al Model Registry and deploy it to Vertex Al End points for online inference. You want to use the simplest approach. What should you do?

A.

Use the Vertex Al REST API within a custom component based on a vertex-ai/prediction/xgboost-cpu image.

B.

Use the Vertex Al ModelEvaluationOp component to evaluate the model.

C.

Use the Vertex Al SDK for Python within a custom component based on a python: 3.10 Image.

D.

Chain the Vertex Al ModelUploadOp and ModelDeployop components together.

You work for a company that manages a ticketing platform for a large chain of cinemas. Customers use a mobile app to search for movies they’re interested in and purchase tickets in the app. Ticket purchase requests are sent to Pub/Sub and are processed with a Dataflow streaming pipeline configured to conduct the following steps:

1. Check for availability of the movie tickets at the selected cinema.

2. Assign the ticket price and accept payment.

3. Reserve the tickets at the selected cinema.

4. Send successful purchases to your database.

Each step in this process has low latency requirements (less than 50 milliseconds). You have developed a logistic regression model with BigQuery ML that predicts whether offering a promo code for free popcorn increases the chance of a ticket purchase, and this prediction should be added to the ticket purchase process. You want to identify the simplest way to deploy this model to production while adding minimal latency. What should you do?

A.

Run batch inference with BigQuery ML every five minutes on each new set of tickets issued.

B.

Export your model in TensorFlow format, and add a tfx_bsl.public.beam.RunInference step to the Dataflow pipeline.

C.

Export your model in TensorFlow format, deploy it on Vertex AI, and query the prediction endpoint from your streaming pipeline.

D.

Convert your model with TensorFlow Lite (TFLite), and add it to the mobile app so that the promo code and the incoming request arrive together in Pub/Sub.

You are developing a recommendation engine for an online clothing store. The historical customer transaction data is stored in BigQuery and Cloud Storage. You need to perform exploratory data analysis (EDA), preprocessing and model training. You plan to rerun these EDA, preprocessing, and training steps as you experiment with different types of algorithms. You want to minimize the cost and development effort of running these steps as you experiment. How should you configure the environment?

A.

Create a Vertex Al Workbench user-managed notebook using the default VM instance, and use the %%bigquery magic commands in Jupyter to query the tables.

B.

Create a Vertex Al Workbench managed notebook to browse and query the tables directly from the JupyterLab interface.

C.

Create a Vertex Al Workbench user-managed notebook on a Dataproc Hub. and use the %%bigquery magic commands in Jupyter to query the tables.

D.

Create a Vertex Al Workbench managed notebook on a Dataproc cluster, and use the spark-bigquery-connector to access the tables.

You work at a mobile gaming startup that creates online multiplayer games Recently, your company observed an increase in players cheating in the games, leading to a loss of revenue and a poor user experience. You built a binary classification model to determine whether a player cheated after a completed game session, and then send a message to other downstream systems to ban the player that cheated Your model has performed well during testing, and you now need to deploy the model to production You want your serving solution to provide immediate classifications after a completed game session to avoid further loss of revenue. What should you do?

A.

Import the model into Vertex Al Model Registry. Use the Vertex Batch Prediction service to run batch inference jobs.

B.

Save the model files in a Cloud Storage Bucket Create a Cloud Function to read the model files and make online inference requests on the Cloud Function.

C.

Save the model files in a VM Load the model files each time there is a prediction request and run an inference job on the VM.

D.

Import the model into Vertex Al Model Registry Create a Vertex Al endpoint that hosts the model and make online inference requests.

You work at a bank. You need to develop a credit risk model to support loan application decisions You decide to implement the model by using a neural network in TensorFlow Due to regulatory requirements, you need to be able to explain the models predictions based on its features When the model is deployed, you also want to monitor the model's performance overtime You decided to use Vertex Al for both model development and deployment What should you do?

A.

Use Vertex Explainable Al with the sampled Shapley method, and enable Vertex Al Model Monitoring to

check for feature distribution drift.

B.

Use Vertex Explainable Al with the sampled Shapley method, and enable Vertex Al Model Monitoring to

check for feature distribution skew.

C.

Use Vertex Explainable Al with the XRAI method, and enable Vertex Al Model Monitoring to check for feature distribution drift.

D.

Use Vertex Explainable Al with the XRAI method and enable Vertex Al Model Monitoring to check for feature distribution skew.