Quiz IT certification exam

Question 1

You are training models in Vertex Al by using data that spans across multiple Google Cloud Projects You need to find track, and compare the performance of the different versions of your models Which Google Cloud services should you include in your ML workflow?

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Accepted Answer

Vertex Al Pipelines, Vertex Al Feature Store, and Vertex Al Experiments

Answer

Dataplex. Vertex Al Feature Store and Vertex Al TensorBoard

Answer

Dataplex. Vertex Al Experiments, and Vertex Al ML Metadata

Answer

Vertex Al Pipelines: Vertex Al Experiments and Vertex Al Metadata

Question 2

You need to use TensorFlow to train an image classification model. Your dataset is located in a Cloud Storage directory and contains millions of labeled images Before training the model, you need to prepare the data. You want the data preprocessing and model training workflow to be as efficient scalable, and low maintenance as possible. What should you do?

Accepted Answer

1 Create a Dataflow job that creates sharded TFRecord files in a Cloud Storage directory. 2 Reference tf .data.TFRecordDataset in the training script. 3. Train the model by using Vertex Al Training with a V100 GPU.

Answer

1 Create a Dataflow job that moves the images into multiple Cloud Storage directories, where each directory is named according to the corresponding label. 2 Reference tfds.fclder_da-asst.imageFclder in the training script. 3. Train the model by using Vertex AI Training with a V100 GPU.

Answer

1 Create a Jupyter notebook that uses an n1-standard-64, V100 GPU Vertex Al Workbench instance. 2 Write a Python script that creates sharded TFRecord files in a directory inside the instance 3. Reference tf. da-a.TFRecrrdDataset in the training script. 4. Train the model by using the Workbench instance.

Answer

1 Create a Jupyter notebook that uses an n1-standard-64, V100 GPU Vertex Al Workbench instance. 2 Write a Python scnpt that copies the images into multiple Cloud Storage directories, where each directory is named according to the corresponding label. 3 Reference tf ds. f older_dataset. imageFolder in the training script. 4. Train the model by using the Workbench instance.

Question 3

You are building a custom image classification model and plan to use Vertex Al Pipelines to implement the end-to-end training. Your dataset consists of images that need to be preprocessed before they can be used to train the model. The preprocessing steps include resizing the images, converting them to grayscale, and extracting features. You have already implemented some Python functions for the preprocessing tasks. Which components should you use in your pipeline'?

Accepted Answer

Question 4

You work for a retail company that is using a regression model built with BigQuery ML to predict product sales. This model is being used to serve online predictions Recently you developed a new version of the model that uses a different architecture (custom model) Initial analysis revealed that both models are performing as expected You want to deploy the new version of the model to production and monitor the performance over the next two months You need to minimize the impact to the existing and future model users How should you deploy the model?

Accepted Answer

Import the new model to the same Vertex Al Model Registry as a different version of the existing model. Deploy the new model to the same Vertex Al endpoint as the existing model, and use traffic splitting to route 95% of production traffic to the BigQuery ML model and 5% of production traffic to the new model.

Answer

Import the new model to the same Vertex Al Model Registry as the existing model Deploy the models to one Vertex Al endpoint Route 95% of production traffic to the BigQuery ML model and 5% of production traffic to the new model

Answer

Import the new model to the same Vertex Al Model Registry as the existing model Deploy each model to a separate Vertex Al endpoint.

Answer

Deploy the new model to a separate Vertex Al endpoint Create a Cloud Run service that routes the prediction requests to the corresponding endpoints based on the input feature values.

Question 5

You work for a large retailer and you need to build a model to predict customer churn. The company has a dataset of historical customer data, including customer demographics, purchase history, and website activity. You need to create the model in BigQuery ML and thoroughly evaluate its performance. What should you do?

Accepted Answer

Create a logistic regression model in BigQuery ML and register the model in Vertex Al Model Registry. Evaluate the model performance in Vertex Al.

Answer

Create a linear regression model in BigQuery ML and register the model in Vertex Al Model Registry Evaluate the model performance in Vertex Al.

Answer

Create a linear regression model in BigQuery ML Use the ml. evaluate function to evaluate the model performance.

Answer

Create a logistic regression model in BigQuery ML Use the ml.confusion_matrix function to evaluate the model performance.

Question 6

You are developing a model to identify traffic signs in images extracted from videos taken from the dashboard of a vehicle. You have a dataset of 100 000 images that were cropped to show one out of ten different traffic signs. The images have been labeled accordingly for model training and are stored in a Cloud Storage bucket You need to be able to tune the model during each training run. How should you train the model?

Accepted Answer

Develop the model training code for image classification and train a model by using Vertex Al custom training.

Answer

Train a model for object detection by using Vertex Al AutoML.

Answer

Train a model for image classification by using Vertex Al AutoML.

Answer

Develop the model training code for object detection and tram a model by using Vertex Al custom training.

Question 7

You have deployed a scikit-learn model to a Vertex Al endpoint using a custom model server. You enabled auto scaling; however, the deployed model fails to scale beyond one replica, which led to dropped requests. You notice that CPU utilization remains low even during periods of high load. What should you do?

Accepted Answer

Increase the number of workers in your model server.

Answer

Attach a GPU to the prediction nodes.

Answer

Schedule scaling of the nodes to match expected demand.

Answer

Increase the minReplicaCount in your DeployedModel configuration.

Question 8

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?

Accepted Answer

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.

Answer

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.

Answer

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.

Answer

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.

Question 9

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?

Accepted Answer

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

Answer

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

Answer

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.

Answer

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.

Question 10

You have created a Vertex Al pipeline that automates custom model training You want to add a pipeline component that enables your team to most easily collaborate when running different executions and comparing metrics both visually and programmatically. What should you do?

Accepted Answer

Add a component to the Vertex Al pipeline that logs metrics to Vertex ML Metadata Use Vertex Al Experiments to compare different executions of the pipeline Use Vertex Al TensorBoard to visualize metrics.

Answer

Add a component to the Vertex Al pipeline that logs metrics to a BigQuery table Query the table to compare different executions of the pipeline Connect BigQuery to Looker Studio to visualize metrics.

Answer

Add a component to the Vertex Al pipeline that logs metrics to a BigQuery table Load the table into a pandas DataFrame to compare different executions of the pipeline Use Matplotlib to visualize metrics.

Answer

Add a component to the Vertex Al pipeline that logs metrics to Vertex ML Metadata Load the Vertex ML Metadata into a pandas DataFrame to compare different executions of the pipeline. Use Matplotlib to visualize metrics.

Question 11

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?

Accepted Answer

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.

Answer

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

Answer

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

Answer

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

Question 12

You work on a team that builds state-of-the-art deep learning models by using the TensorFlow framework. Your team runs multiple ML experiments each week which makes it difficult to track the experiment runs. You want a simple approach to effectively track, visualize and debug ML experiment runs on Google Cloud while minimizing any overhead code. How should you proceed?

Accepted Answer

Set up Vertex Al Experiments to track metrics and parameters Configure Vertex Al TensorBoard for visualization.

Answer

Set up a Cloud Function to write and save metrics files to a Cloud Storage Bucket Configure a Google Cloud VM to host TensorBoard locally for visualization.

Answer

Set up a Vertex Al Workbench notebook instance Use the instance to save metrics data in a Cloud Storage bucket and to host TensorBoard locally for visualization.

Answer

Set up a Cloud Function to write and save metrics files to a BigQuery table. Configure a Google Cloud VM to host TensorBoard locally for visualization.

Question 13

Your work for a textile manufacturing company. Your company has hundreds of machines and each machine has many sensors. Your team used the sensory data to build hundreds of ML models that detect machine anomalies Models are retrained daily and you need to deploy these models in a cost-effective way. The models must operate 24/7 without downtime and make sub millisecond predictions. What should you do?

Accepted Answer

Deploy a Dataflow streaming pipeline with the Runlnference API and use automatic model refresh.

Answer

Deploy a Dataflow batch pipeline and a Vertex Al Prediction endpoint.

Answer

Deploy a Dataflow batch pipeline with the Runlnference API. and use model refresh.

Answer

Deploy a Dataflow streaming pipeline and a Vertex Al Prediction endpoint with autoscaling.

Question 14

You are developing an ML model that predicts the cost of used automobiles based on data such as location, condition model type color, and engine-'battery efficiency. The data is updated every night Car dealerships will use the model to determine appropriate car prices. You created a Vertex Al pipeline that reads the data splits the data into training/evaluation/test sets performs feature engineering trains the model by using the training dataset and validates the model by using the evaluation dataset. You need to configure a retraining workflow that minimizes cost What should you do?

Accepted Answer

Compare the training and evaluation losses of the current run If the losses are similar deploy the model to a Vertex Al endpoint with training/serving skew threshold model monitoring When the model monitoring threshold is tnggered redeploy the pipeline.

Answer

Compare the training and evaluation losses of the current run If the losses are similar, deploy the model to a Vertex AI endpoint Configure a cron job to redeploy the pipeline every night.

Answer

Compare the results to the evaluation results from a previous run If the performance improved deploy the model to a Vertex Al endpoint Configure a cron job to redeploy the pipeline every night.

Answer

Compare the results to the evaluation results from a previous run If the performance improved deploy the model to a Vertex Al endpoint with training/serving skew threshold model monitoring. When the model monitoring threshold is triggered, redeploy the pipeline.

Question 15

You recently used BigQuery ML to train an AutoML regression model. You shared results with your team and received positive feedback. You need to deploy your model for online prediction as quickly as possible. What should you do?

Accepted Answer

Retrain the model by using BigQuery ML. and specify Vertex Al as the model registry Deploy the model from Vertex Al Model Registry to a Vertex Al endpoint.

Answer

Retrain the model by using Vertex Al Deploy the model from Vertex Al Model Registry to a Vertex Al endpoint.

Answer

Alter the model by using BigQuery ML and specify Vertex Al as the model registry Deploy the model from Vertex Al Model Registry to a Vertex Al endpoint.

Answer

Export the model from BigQuery ML to Cloud Storage Import the model into Vertex Al Model Registry Deploy the model to a Vertex Al endpoint.

Question 16

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?

Accepted Answer

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

Answer

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

Answer

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

Answer

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

Question 17

You trained a model, packaged it with a custom Docker container for serving, and deployed it to Vertex Al Model Registry. When you submit a batch prediction job, it fails with this error 'Error model server never became ready Please validate that your model file or container configuration are valid. There are no additional errors in the logs What should you do?

Accepted Answer

Change the HTTP port in your model's configuration to the default value of 8080

Answer

Add a logging configuration to your application to emit logs to Cloud Logging.

Answer

Change the health Route value in your models configuration to /heal thcheck.

Answer

Pull the Docker image locally and use the decker run command to launch it locally. Use the docker logs command to explore the error logs.

Question 18

You are developing an ML model to identify your company s products in images. You have access to over one million images in a Cloud Storage bucket. You plan to experiment with different TensorFlow models by using Vertex Al Training You need to read images at scale during training while minimizing data I/O bottlenecks What should you do?

Accepted Answer

Convert the images to TFRecords and store them in a Cloud Storage bucket Read the TFRecords by using the tf. ciata.TFRecordDataset function.

Answer

Load the images directly into the Vertex Al compute nodes by using Cloud Storage FUSE Read the images by using the tf .data.Dataset.from_tensor_slices function.

Answer

Create a Vertex Al managed dataset from your image data Access the aip_training_data_uri environment variable to read the images by using the tf. data. Dataset. Iist_flies function.

Answer

Store the URLs of the images in a CSV file Read the file by using the tf.data.experomental.CsvDataset function.

Question 19

You work at an ecommerce startup. You need to create a customer churn prediction model Your company's recent sales records are stored in a BigQuery table You want to understand how your initial model is making predictions. You also want to iterate on the model as quickly as possible while minimizing cost How should you build your first model?

Accepted Answer

Prepare the data in BigQuery and associate the data with a Vertex Al dataset Create an AutoMLTabuiarTrainmgJob to train a classification model.

Answer

Export the data to a Cloud Storage Bucket Load the data into a pandas DataFrame on Vertex Al Workbench and train a logistic regression model with scikit-learn.

Answer

Create a tf.data.Dataset by using the TensorFlow BigQueryChent Implement a deep neural network in TensorFlow.

Answer

Export the data to a Cloud Storage Bucket Create tf. data. Dataset to read the data from Cloud Storage Implement a deep neural network in TensorFlow.

Question 20

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 steps1. Randomly split the data into training and evaluation datasets in a 65/35 ratio2. Conduct feature engineering3 Obtain metrics for the evaluation dataset.4 Compare models trained in different pipeline executionsHow should you execute these steps'?

Accepted Answer

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

Answer

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

Answer

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.

Answer

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.

Question 21

You work for a company that sells corporate electronic products to thousands of businesses worldwide. Your company stores historical customer data in BigQuery. You need to build a model that predicts customer lifetime value over the next three years. You want to use the simplest approach to build the model and you want to have access to visualization tools. What should you do?

Accepted Answer

Run the create model statement from the BigQuery console to create an AutoML model Validate the results by using the ml. evaluate and ml. predict statements.

Answer

Create a Vertex Al Workbench notebook to perform exploratory data analysis. Use IPython magics to create a new BigQuery table with input features Use the BigQuery console to run the create model statement Validate the results by using the ml. evaluate and ml. predict statements.

Answer

Create a Vertex Al Workbench notebook to perform exploratory data analysis and create input features Save the features as a CSV file in Cloud Storage Import the CSV file as a new BigQuery table Use the BigQuery console to run the create model statement Validate the results by using the ml. evaluate and ml. predict statements.

Answer

Create a Vertex Al Workbench notebook to perform exploratory data analysis Use IPython magics to create a new BigQuery table with input features, create the model and validate the results by using the create model, ml. evaluates, and ml. predict statements.

Question 22

You work at a large organization that recently decided to move their ML and data workloads to Google Cloud. The data engineering team has exported the structured data to a Cloud Storage bucket in Avro format. You need to propose a workflow that performs analytics, creates features, and hosts the features that your ML models use for online prediction How should you configure the pipeline?

Accepted Answer

Ingest the Avro files into BigQuery to perform analytics Use a Dataflow pipeline to create the features, and store them in Vertex Al Feature Store for online prediction.

Answer

Ingest the Avro files into Cloud Spanner to perform analytics Use a Dataflow pipeline to create the features and store them in BigQuery for online prediction.

Answer

Ingest the Avro files into BigQuery to perform analytics Use BigQuery SQL to create features and store them in a separate BigQuery table for online prediction.

Answer

Ingest the Avro files into Cloud Spanner to perform analytics. Use a Dataflow pipeline to create the features. and store them in Vertex Al Feature Store for online prediction.

Question 23

You work for a delivery company. You need to design a system that stores and manages features such as parcels delivered and truck locations over time. The system must retrieve the features with low latency and feed those features into a model for online prediction. The data science team will retrieve historical data at a specific point in time for model training. You want to store the features with minimal effort. What should you do?

Accepted Answer

Store features in Vertex Al Feature Store.

Answer

Store features in Bigtable as key/value data.

Answer

Store features as a Vertex Al dataset and use those features to tram the models hosted in Vertex Al endpoints.

Answer

Store features in BigQuery timestamp partitioned tables, and use the BigQuery Storage Read API to serve the features.

Question 24

You are working on a prototype of a text classification model in a managed Vertex AI Workbench notebook. You want to quickly experiment with tokenizing text by using a Natural Language Toolkit (NLTK) library. How should you add the library to your Jupyter kernel?

Accepted Answer

Install the NLTK library from a Jupyter cell by using the! pip install nltk ---user command.

Answer

Install the NLTK library from a terminal by using the pip install nltk command.

Answer

Write a custom Dataflow job that uses NLTK to tokenize your text and saves the output to Cloud Storage.

Answer

Create a new Vertex Al Workbench notebook with a custom image that includes the NLTK library.

Question 25

You have recently used TensorFlow to train a classification model on tabular data You have created a Dataflow pipeline that can transform several terabytes of data into training or prediction datasets consisting of TFRecords. You now need to productionize the model, and you want the predictions to be automatically uploaded to a BigQuery table on a weekly schedule. What should you do?

Accepted Answer

Import the model into Vertex Al On Vertex Al Pipelines, create a pipeline that uses the DatafIowPythonJobOp and the ModelBatchPredictOp components.

Answer

Import the model into Vertex Al and deploy it to a Vertex Al endpoint On Vertex Al Pipelines create a pipeline that uses the Dataf lowPythonJobop and the Mcdei3archPredictoc components.

Answer

Import the model into Vertex Al and deploy it to a Vertex Al endpoint Create a Dataflow pipeline that reuses the data processing logic sends requests to the endpoint and then uploads predictions to a BigQuery table.

Answer

Import the model into BigQuery Implement the data processing logic in a SQL query On Vertex Al Pipelines create a pipeline that uses the BigqueryQueryJobop and the EigqueryPredictModejobOp components.

Question 26

You work for an online grocery store. You recently developed a custom ML model that recommends a recipe when a user arrives at the website. You chose the machine type on the Vertex Al endpoint to optimize costs by using the queries per second (QPS) that the model can serve, and you deployed it on a single machine with 8 vCPUs and no accelerators.A holiday season is approaching and you anticipate four times more traffic during this time than the typical daily traffic You need to ensure that the model can scale efficiently to the increased demand. What should you do?

Accepted Answer

1 Maintain the same machine type on the endpoint Configure the endpoint to enable autoscalling based on vCPU usage. 2 Set up a monitoring job and an alert for CPU usage 3 If you receive an alert investigate the cause

Answer

1, Maintain the same machine type on the endpoint. 2 Set up a monitoring job and an alert for CPU usage 3 If you receive an alert add a compute node to the endpoint

Answer

1 Change the machine type on the endpoint to have 32 vCPUs 2. Set up a monitoring job and an alert for CPU usage 3 If you receive an alert, scale the vCPUs further as needed

Answer

1 Change the machine type on the endpoint to have a GPU_ Configure the endpoint to enable autoscaling based on the GPU usage. 2 Set up a monitoring job and an alert for GPU usage. 3 If you receive an alert investigate the cause.

Question 27

You recently trained an XGBoost model on tabular data You plan to expose the model for internal use as an HTTP microservice After deployment you expect a small number of incoming requests. You want to productionize the model with the least amount of effort and latency. What should you do?

Accepted Answer

Use a prebuilt XGBoost Vertex container to create a model and deploy it to Vertex Al Endpoints.

Answer

Deploy the model to BigQuery ML by using CREATE model with the BOOSTED-THREE-REGRESSOR statement and invoke the BigQuery API from the microservice.

Answer

Build a Flask-based app Package the app in a custom container on Vertex Al and deploy it to Vertex Al Endpoints.

Answer

Build a Flask-based app Package the app in a Docker image and deploy it to Google Kubernetes Engine in Autopilot mode.

Question 28

You work for an international manufacturing organization that ships scientific products all over the world Instruction manuals for these products need to be translated to 15 different languages Your organization's leadership team wants to start using machine learning to reduce the cost of manual human translations and increase translation speed. You need to implement a scalable solution that maximizes accuracy and minimizes operational overhead. You also want to include a process to evaluate and fix incorrect translations. What should you do?

Accepted Answer

Use AutoML Translation to tram a model Configure a Translation Hub project and use the trained model to translate the documents Use human reviewers to evaluate the incorrect translations

Answer

Create a workflow using Cloud Function Triggers Configure a Cloud Function that is triggered when documents are uploaded to an input Cloud Storage bucket Configure another Cloud Function that translates the documents using the Cloud Translation API and saves the translations to an output Cloud Storage bucket Use human reviewers to evaluate the incorrect translations.

Answer

Create a Vertex Al pipeline that processes the documents1 launches an AutoML Translation training job evaluates the translations, and deploys the model to a Vertex Al endpoint with autoscaling and model monitoring When there is a predetermined skew between training and live data re-trigger the pipeline with the latest data.

Answer

Use Vertex Al custom training jobs to fine-tune a state-of-the-art open source pretrained model with your data Deploy the model to a Vertex Al endpoint with autoscaling and model monitoring When there is a predetermined skew between the training and live data, configure a trigger to run another training job with the latest data.

Question 29

You have developed an application that uses a chain of multiple scikit-learn models to predict the optimal price for your company's products. The workflow logic is shown in the diagram Members of your team use the individual models in other solution workflows. You want to deploy this workflow while ensuring version control for each individual model and the overall workflow Your application needs to be able to scale down to zero. You want to minimize the compute resource utilization and the manual effort required to manage this solution. What should you do?<img alt="Google Professional Machine Learning Engineer image Question 229 30394 09182024191449000000"   width="868" height="248" src="https://examgecko.com/getfile/db5170b7-0041-4066-9a69-2fe57d229aba">

Accepted Answer

Expose each individual model as an endpoint in Vertex Al Endpoints. Use Cloud Run to orchestrate the workflow.

Answer

Expose each individual model as an endpoint in Vertex Al Endpoints. Create a custom container endpoint to orchestrate the workflow.

Answer

Create a custom container endpoint for the workflow that loads each models individual files Track the versions of each individual model in BigQuery.

Answer

Load each model's individual files into Cloud Run Use Cloud Run to orchestrate the workflow Track the versions of each individual model in BigQuery.

Question 30

You are developing a model to predict whether a failure will occur in a critical machine part. You have a dataset consisting of a multivariate time series and labels indicating whether the machine part failed You recently started experimenting with a few different preprocessing and modeling approaches in a Vertex Al Workbench notebook. You want to log data and track artifacts from each run. How should you set up your experiments?

Accepted Answer

Question 31

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?

Accepted Answer

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.

Answer

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

Answer

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

Answer

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

Question 32

You recently deployed a model lo a Vertex Al endpoint and set up online serving in Vertex Al Feature Store. You have configured a daily batch ingestion job to update your featurestore During the batch ingestion jobs you discover that CPU utilization is high in your featurestores online serving nodes and that feature retrieval latency is high. You need to improve online serving performance during the daily batch ingestion. What should you do?

Accepted Answer

Enable autoscaling of the online serving nodes in your featurestore

Answer

Schedule an increase in the number of online serving nodes in your featurestore prior to the batch ingestion jobs.

Answer

Enable autoscaling for the prediction nodes of your DeployedModel in the Vertex Al endpoint.

Answer

Increase the worker counts in the importFeaturevalues request of your batch ingestion job.

Question 33

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?

Accepted Answer

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.

Answer

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.

Answer

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.

Answer

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.

Question 34

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?

Accepted Answer

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

Answer

Store image files in Cloud Storage and access them directly.

Answer

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

Answer

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

Question 35

You are developing an image recognition model using PyTorch based on ResNet50 architecture Your code is working fine on your local laptop on a small subsample. Your full dataset has 200k labeled images You want to quickly scale your training workload while minimizing cost. You plan to use 4 V100 GPUs What should you do?

Accepted Answer

Package your code with Setuptools and use a pre-built container. Train your model with Vertex Al using a custom tier that contains the required GPUs.

Answer

Create a Google Kubernetes Engine cluster with a node pool that has 4 V100 GPUs Prepare and submit a TFJob operator to this node pool.

Answer

Configure a Compute Engine VM with all the dependencies that launches the training Tram your model with Vertex Al using a custom tier that contains the required GPUs.

Answer

Create a Vertex Al Workbench user-managed notebooks instance with 4 V100 GPUs, and use it to tram your model.

Question 36

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?

Accepted Answer

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.

Answer

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.

Answer

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

Answer

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

Question 37

You work for a company that is developing an application to help users with meal planning You want to use machine learning to scan a corpus of recipes and extract each ingredient (e g carrot, rice pasta) and each kitchen cookware (e.g. bowl, pot spoon) mentioned Each recipe is saved in an unstructured text file What should you do?

Accepted Answer

Create a text dataset on Vertex Al for entity extraction Create two entities called ingredient' and cookware' and label at least 200 examples of each entity Train an AutoML entity extraction model to extract occurrences of these entity types Evaluate performance on a holdout dataset.

Answer

Create a multi-label text classification dataset on Vertex Al Create a test dataset and label each recipe that corresponds to its ingredients and cookware Train a multi-class classification model Evaluate the model's performance on a holdout dataset.

Answer

Use the Entity Analysis method of the Natural Language API to extract the ingredients and cookware from each recipe Evaluate the model's performance on a prelabeled dataset.

Answer

Create a text dataset on Vertex Al for entity extraction Create as many entities as there are different ingredients and cookware Train an AutoML entity extraction model to extract those entities Evaluate the models performance on a holdout dataset.

Question 38

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?

Accepted Answer

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.

Answer

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.

Answer

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.

Answer

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.

Question 39

You created a model that uses BigQuery ML to perform linear regression. You need to retrain the model on the cumulative data collected every week. You want to minimize the development effort and the scheduling cost. What should you do?

Accepted Answer

Create a pipeline in Vertex Al Pipelines that executes the retraining query and use the Cloud Scheduler API to run the query weekly.

Answer

Use BigQuerys scheduling service to run the model retraining query periodically.

Answer

Use Cloud Scheduler to trigger a Cloud Function every week that runs the query for retraining the model.

Answer

Use the BigQuery API Connector and Cloud Scheduler to trigger. Workflows every week that retrains the model.

Question 40

You want to migrate a scikrt-learn classifier model to TensorFlow. You plan to train the TensorFlow classifier model using the same training set that was used to train the scikit-learn model and then compare the performances using a common test set. You want to use the Vertex Al Python SDK to manually log the evaluation metrics of each model and compare them based on their F1 scores and confusion matrices. How should you log the metrics?

Accepted Answer

Google Professional Machine Learning Engineer Practice Test 6

Google Professional Machine Learning Engineer Practice Tests