Quiz IT certification exam

Question 1

You work at a gaming startup that has several terabytes of structured data in Cloud Storage. This data includes gameplay time data user metadata and game metadata. You want to build a model that recommends new games to users that requires the least amount of coding. What should you do?

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Accepted Answer

Load the data in BigQuery Use BigQuery ML to train a matrix factorization model.

Answer

Load the data in BigQuery Use BigQuery ML to tram an Autoencoder model.

Answer

Read data to a Vertex Al Workbench notebook Use TensorFlow to train a two-tower model.

Answer

Read data to a Vertex AI Workbench notebook Use TensorFlow to train a matrix factorization model.

Question 2

You are developing a model to help your company create more targeted online advertising campaigns. You need to create a dataset that you will use to train the model. You want to avoid creating or reinforcing unfair bias in the model. What should you do?Choose 2 answers

Accepted Answer

Collect a random sample of production traffic to build the training dataset.

Accepted Answer

Conduct fairness tests across sensitive categories and demographics on the trained model.

Answer

Include a comprehensive set of demographic features.

Answer

include only the demographic groups that most frequently interact with advertisements.

Answer

Collect a stratified sample of production traffic to build the training dataset.

Question 3

You are developing an ML model in a Vertex Al Workbench notebook. You want to track artifacts and compare models during experimentation using different approaches. You need to rapidly and easily transition successful experiments to production as you iterate on your model implementation. What should you do?

Accepted Answer

1 Initialize the Vertex SDK with the name of your experiment Log parameters and metrics for each experiment, and attach dataset and model artifacts as inputs and outputs to each execution. 2 After a successful experiment create a Vertex Al pipeline.

Answer

1. Initialize the Vertex SDK with the name of your experiment Log parameters and metrics for each experiment, save your dataset to a Cloud Storage bucket and upload the models to Vertex Al Model Registry. 2 After a successful experiment create a Vertex Al pipeline.

Answer

1 Create a Vertex Al pipeline with parameters you want to track as arguments to your Pipeline Job Use the Metrics. Model, and Dataset artifact types from the Kubeflow Pipelines DSL as the inputs and outputs of the components in your pipeline. 2. Associate the pipeline with your experiment when you submit the job.

Answer

1 Create a Vertex Al pipeline Use the Dataset and Model artifact types from the Kubeflow Pipelines. DSL as the inputs and outputs of the components in your pipeline. 2. In your training component use the Vertex Al SDK to create an experiment run Configure the log_params and log_metrics functions to track parameters and metrics of your experiment.

Question 4

You recently created a new Google Cloud Project After testing that you can submit a Vertex Al Pipeline job from the Cloud Shell, you want to use a Vertex Al Workbench user-managed notebook instance to run your code from that instance You created the instance and ran the code but this time the job fails with an insufficient permissions error. What should you do?

Accepted Answer

Ensure that the Vertex Al Workbench instance is assigned the Identity and Access Management (1AM) Vertex Al User rote.

Answer

Ensure that the Workbench instance that you created is in the same region of the Vertex Al Pipelines resources you will use.

Answer

Ensure that the Vertex Al Workbench instance is on the same subnetwork of the Vertex Al Pipeline resources that you will use.

Answer

Ensure that the Vertex Al Workbench instance is assigned the Identity and Access Management (1AM) Notebooks Runner role.

Question 5

You work for a semiconductor manufacturing company. You need to create a real-time application that automates the quality control process High-definition images of each semiconductor are taken at the end of the assembly line in real time. The photos are uploaded to a Cloud Storage bucket along with tabular data that includes each semiconductor's batch number serial number dimensions, and weight You need to configure model training and serving while maximizing model accuracy. What should you do?

Accepted Answer

Use Vertex Al Data Labeling Service to label the images and train an AutoML image classification model. Deploy the model and configure Pub/Sub to publish a message when an image is categorized into the failing class.

Answer

Use Vertex Al Data Labeling Service to label the images and train an AutoML image classification model. Schedule a daily batch prediction job that publishes a Pub/Sub message when the job completes.

Answer

Convert the images into an embedding representation Import this data into BigQuery, and train a BigQuery. ML K-means clustenng model with two clusters Deploy the model and configure Pub/Sub to publish a message when a semiconductor's data is categorized into the failing cluster.

Answer

Import the tabular data into BigQuery use Vertex Al Data Labeling Service to label the data and train an AutoML tabular classification model Deploy the model and configure Pub/Sub to publish a message when a semiconductor's data is categorized into the failing class.

Question 6

You work at a gaming startup that has several terabytes of structured data in Cloud Storage. This data includes gameplay time data user metadata and game metadata. You want to build a model that recommends new games to users that requires the least amount of coding. What should you do?

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Accepted Answer

Load the data in BigQuery. Use BigQuery ML to train a matrix factorization model.

Answer

Load the data in BigQuery. Use BigQuery ML to train an Autoencoder model.

Answer

Read data to a Vertex Al Workbench notebook. Use TensorFlow to train a two-tower model.

Answer

Read data to a Vertex Al Workbench notebook. Use TensorFlow to train a matrix factorization model.

Question 7

While running a model training pipeline on Vertex Al, you discover that the evaluation step is failing because of an out-of-memory error. You are currently using TensorFlow Model Analysis (TFMA) with a standard Evaluator TensorFlow Extended (TFX) pipeline component for the evaluation step. You want to stabilize the pipeline without downgrading the evaluation quality while minimizing infrastructure overhead. What should you do?

Accepted Answer

Include the flag -runner=DataflowRunner in beam_pipeline_args to run the evaluation step on Dataflow.

Answer

Add tfma.MetricsSpec () to limit the number of metrics in the evaluation step.

Answer

Migrate your pipeline to Kubeflow hosted on Google Kubernetes Engine, and specify the appropriate node parameters for the evaluation step.

Answer

Move the evaluation step out of your pipeline and run it on custom Compute Engine VMs with sufficient memory.

Question 8

You developed a BigQuery ML linear regressor model by using a training dataset stored in a BigQuery table. New data is added to the table every minute. You are using Cloud Scheduler and Vertex Al Pipelines to automate hourly model training, and use the model for direct inference. The feature preprocessing logic includes quantile bucketization and MinMax scaling on data received in the last hour. You want to minimize storage and computational overhead. What should you do?

Accepted Answer

Use the TRANSFORM clause in the CREATE MODEL statement in the SQL query to calculate the required statistics.

Answer

Create a component in the Vertex Al Pipelines directed acyclic graph (DAG) to calculate the required statistics, and pass the statistics on to subsequent components.

Answer

Preprocess and stage the data in BigQuery prior to feeding it to the model during training and inference.

Answer

Create SQL queries to calculate and store the required statistics in separate BigQuery tables that are referenced in the CREATE MODEL statement.

Question 9

You are creating a social media app where pet owners can post images of their pets. You have one million user uploaded images with hashtags. You want to build a comprehensive system that recommends images to users that are similar in appearance to their own uploaded images.What should you do?

Accepted Answer

Download a pretrained convolutional neural network, and use the model to generate embeddings of the input images. Measure similarity between embeddings to make recommendations.

Answer

Download a pretrained convolutional neural network, and fine-tune the model to predict hashtags based on the input images. Use the predicted hashtags to make recommendations.

Answer

Retrieve image labels and dominant colors from the input images using the Vision API. Use these properties and the hashtags to make recommendations.

Answer

Use the provided hashtags to create a collaborative filtering algorithm to make recommendations.

Question 10

You are training a deep learning model for semantic image segmentation with reduced training time. While using a Deep Learning VM Image, you receive the following error: The resource 'projects/deeplearning-platforn/zones/europe-west4-c/acceleratorTypes/nvidia-tesla-k80' was not found. What should you do?

Accepted Answer

Ensure that the required GPU is available in the selected region.

Answer

Ensure that you have GPU quota in the selected region.

Answer

Ensure that you have preemptible GPU quota in the selected region.

Answer

Ensure that the selected GPU has enough GPU memory for the workload.

Question 11

You need to build classification workflows over several structured datasets currently stored in BigQuery. Because you will be performing the classification several times, you want to complete the following steps without writing code: exploratory data analysis, feature selection, model building, training, and hyperparameter tuning and serving. What should you do?

Accepted Answer

Configure AutoML Tables to perform the classification task

Answer

Run a BigQuery ML task to perform logistic regression for the classification

Answer

Use Al Platform Notebooks to run the classification model with pandas library

Answer

Use Al Platform to run the classification model job configured for hyperparameter tuning

Question 12

You work for a rapidly growing social media company. Your team builds TensorFlow recommender models in an on-premises CPU cluster. The data contains billions of historical user events and 100 000 categorical features. You notice that as the data increases the model training time increases. You plan to move the models to Google Cloud You want to use the most scalable approach that also minimizes training time. What should you do?

Accepted Answer

Deploy the training jobs by using TPU VMs with TPUv3 Pod slices, and use the TPUEmbedding API.

Answer

Deploy the training jobs in an autoscaling Google Kubernetes Engine cluster with CPUs

Answer

Deploy a matrix factorization model training job by using BigQuery ML.

Answer

Deploy the training jobs by using Compute Engine instances with A100 GPUs and use the t f. nn. embedding_lookup API.

Question 13

You created an ML pipeline with multiple input parameters. You want to investigate the tradeoffs between different parameter combinations. The parameter options are* input dataset* Max tree depth of the boosted tree regressor* Optimizer learning rateYou need to compare the pipeline performance of the different parameter combinations measured in F1 score, time to train and model complexity. You want your approach to be reproducible and track all pipeline runs on the same platform. What should you do?

Accepted Answer

1 Create an experiment in Vertex Al Experiments 2. Create a Vertex Al pipeline with a custom model training job as part of the pipeline. Configure the pipelines parameters to include those you are investigating 3. Submit multiple runs to the same experiment using different values for the parameters

Answer

1 Use BigQueryML to create a boosted tree regressor and use the hyperparameter tuning capability 2 Configure the hyperparameter syntax to select different input datasets. max tree depths, and optimizer teaming rates Choose the grid search option

Answer

1 Create a Vertex Al pipeline with a custom model training job as part of the pipeline Configure the pipeline's parameters to include those you are investigating 2 In the custom training step, use the Bayesian optimization method with F1 score as the target to maximize

Answer

1 Create a Vertex Al Workbench notebook for each of the different input datasets 2 In each notebook, run different local training jobs with different combinations of the max tree depth and optimizer learning rate parameters 3 After each notebook finishes, append the results to a BigQuery table

Question 14

You received a training-serving skew alert from a Vertex Al Model Monitoring job running in production. You retrained the model with more recent training data, and deployed it back to the Vertex Al endpoint but you are still receiving the same alert. What should you do?

Accepted Answer

Update the model monitoring job to use the more recent training data that was used to retrain the model.

Answer

Update the model monitoring job to use a lower sampling rate.

Answer

Temporarily disable the alert Enable the alert again after a sufficient amount of new production traffic has passed through the Vertex Al endpoint.

Answer

Temporarily disable the alert until the model can be retrained again on newer training data Retrain the model again after a sufficient amount of new production traffic has passed through the Vertex Al endpoint

Question 15

You developed a custom model by using Vertex Al to forecast the sales of your company s products based on historical transactional data You anticipate changes in the feature distributions and the correlations between the features in the near future You also expect to receive a large volume of prediction requests You plan to use Vertex Al Model Monitoring for drift detection and you want to minimize the cost. What should you do?

Accepted Answer

Use the features and the feature attributions for monitoring Set a prediction-sampling-rate value that is closer to 0 than 1.

Answer

Use the features for monitoring Set a monitoring- frequency value that is higher than the default.

Answer

Use the features for monitoring Set a prediction-sampling-rare value that is closer to 1 than 0.

Answer

Use the features and the feature attributions for monitoring. Set a monitoring-frequency value that is lower than the default.

Question 16

You have recently trained a scikit-learn model that you plan to deploy on Vertex Al. This model will support both online and batch prediction. You need to preprocess input data for model inference. You want to package the model for deployment while minimizing additional code What should you do?

Accepted Answer

1 Wrap your model in a custom prediction routine (CPR). and build a container image from the CPR local model 2 Upload your sci-kit learn model container to Vertex Al Model Registry 3 Deploy your model to Vertex Al Endpoints, and create a Vertex Al batch prediction job

Answer

1 Upload your model to the Vertex Al Model Registry by using a prebuilt scikit-learn prediction container 2 Deploy your model to Vertex Al Endpoints, and create a Vertex Al batch prediction job that uses the instanceConfig.inscanceType setting to transform your input data

Answer

1. Create a custom container for your sci-kit learn model, 2 Define a custom serving function for your model 3 Upload your model and custom container to Vertex Al Model Registry 4 Deploy your model to Vertex Al Endpoints, and create a Vertex Al batch prediction job

Answer

1 Create a custom container for your sci-kit learn model. 2 Upload your model and custom container to Vertex Al Model Registry 3 Deploy your model to Vertex Al Endpoints, and create a Vertex Al batch prediction job that uses the instanceConfig. instanceType setting to transform your input data

Question 17

You work for a food product company. Your company's historical sales data is stored in BigQuery You need to use Vertex Al's custom training service to train multiple TensorFlow models that read the data from BigQuery and predict future sales You plan to implement a data preprocessing algorithm that performs min-max scaling and bucketing on a large number of features before you start experimenting with the models. You want to minimize preprocessing time, cost and development effort How should you configure this workflow?

Accepted Answer

Add the transformations as a preprocessing layer in the TensorFlow models.

Answer

Write the transformations into Spark that uses the spark-bigquery-connector and use Dataproc to preprocess the data.

Answer

Write SQL queries to transform the data in-place in BigQuery.

Answer

Create a Dataflow pipeline that uses the BigQuerylO connector to ingest the data process it and write it back to BigQuery.

Question 18

You have created a Vertex Al pipeline that includes two steps. The first step preprocesses 10 TB data completes in about 1 hour, and saves the result in a Cloud Storage bucket The second step uses the processed data to train a model You need to update the model's code to allow you to test different algorithms You want to reduce pipeline execution time and cost, while also minimizing pipeline changes What should you do?

Accepted Answer

Enable caching for the pipeline job. and disable caching for the model training step.

Answer

Add a pipeline parameter and an additional pipeline step Depending on the parameter value the pipeline step conducts or skips data preprocessing and starts model training.

Answer

Create another pipeline without the preprocessing step, and hardcode the preprocessed Cloud Storage file location for model training.

Answer

Configure a machine with more CPU and RAM from the compute-optimized machine family for the data preprocessing step.

Question 19

You work for a bank. You have created a custom model to predict whether a loan application should be flagged for human review. The input features are stored in a BigQuery table. The model is performing well and you plan to deploy it to production. Due to compliance requirements the model must provide explanations for each prediction. You want to add this functionality to your model code with minimal effort and provide explanations that are as accurate as possible What should you do?

Accepted Answer

Upload the custom model to Vertex Al Model Registry and configure feature-based attribution by using sampled Shapley with input baselines.

Answer

Create an AutoML tabular model by using the BigQuery data with integrated Vertex Explainable Al.

Answer

Create a BigQuery ML deep neural network model, and use the ML. EXPLAIN_PREDICT method with the num_integral_steps parameter.

Answer

Update the custom serving container to include sampled Shapley-based explanations in the prediction outputs.

Question 20

You recently used XGBoost to train a model in Python that will be used for online serving Your model prediction service will be called by a backend service implemented in Golang running on a Google Kubemetes Engine (GKE) cluster Your model requires pre and postprocessing steps You need to implement the processing steps so that they run at serving time You want to minimize code changes and infrastructure maintenance and deploy your model into production as quickly as possible. What should you do?

Accepted Answer

Use the Predictor interface to implement a custom prediction routine Build the custom contain upload the container to Vertex Al Model Registry, and deploy it to a Vertex Al endpoint.

Answer

Use FastAPI to implement an HTTP server Create a Docker image that runs your HTTP server and deploy it on your organization's GKE cluster.

Answer

Use FastAPI to implement an HTTP server Create a Docker image that runs your HTTP server Upload the image to Vertex Al Model Registry and deploy it to a Vertex Al endpoint.

Answer

Use the XGBoost prebuilt serving container when importing the trained model into Vertex Al Deploy the model to a Vertex Al endpoint Work with the backend engineers to implement the pre- and postprocessing steps in the Golang backend service.

Question 21

You recently deployed a pipeline in Vertex Al Pipelines that trains and pushes a model to a Vertex Al endpoint to serve real-time traffic. You need to continue experimenting and iterating on your pipeline to improve model performance. You plan to use Cloud Build for CI/CD You want to quickly and easily deploy new pipelines into production and you want to minimize the chance that the new pipeline implementations will break in production. What should you do?

Accepted Answer

Set up a CI/CD pipeline that builds and tests your source code and then deploys built artifacts into a pre-production environment. After a successful pipeline run in the pre-production environment deploy the pipeline to production

Answer

Set up a CI/CD pipeline that builds and tests your source code If the tests are successful use the Google Cloud console to upload the built container to Artifact Registry and upload the compiled pipeline to Vertex Al Pipelines.

Answer

Set up a CI/CD pipeline that builds your source code and then deploys built artifacts into a pre-production environment Run unit tests in the pre-production environment If the tests are successful deploy the pipeline to production.

Answer

Set up a CI/CD pipeline that builds and tests your source code and then deploys built arrets into a pre-production environment After a successful pipeline run in the pre-production environment, rebuild the source code, and deploy the artifacts to production

Question 22

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?

Accepted Answer

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

Answer

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.

Answer

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

Answer

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

Question 23

You are deploying a new version of a model to a production Vertex Al endpoint that is serving traffic You plan to direct all user traffic to the new model You need to deploy the model with minimal disruption to your application What should you do?

Accepted Answer

1 Create a new model Set the parentModel parameter to the model ID of the currently deployed model Upload the model to Vertex Al Model Registry. 2 Deploy the new model to the existing endpoint and set the new model to 100% of the traffic.

Answer

1 Create a new endpoint. 2 Create a new model Set it as the default version Upload the model to Vertex Al Model Registry. 3. Deploy the new model to the new endpoint. 4 Update Cloud DNS to point to the new endpoint

Answer

1. Create a new endpoint. 2. Create a new model Set the parentModel parameter to the model ID of the currently deployed model and set it as the default version Upload the model to Vertex Al Model Registry 3. Deploy the new model to the new endpoint and set the new model to 100% of the traffic

Answer

1, Create a new model Set it as the default version Upload the model to Vertex Al Model Registry 2 Deploy the new model to the existing endpoint

Question 24

You are training and deploying updated versions of a regression model with tabular data by using Vertex Al Pipelines. Vertex Al Training Vertex Al Experiments and Vertex Al Endpoints. The model is deployed in a Vertex Al endpoint and your users call the model by using the Vertex Al endpoint. You want to receive an email when the feature data distribution changes significantly, so you can retrigger the training pipeline and deploy an updated version of your model What should you do?

Accepted Answer

Use Vertex Al Model Monitoring Enable prediction drift monitoring on the endpoint. and specify a notification email.

Answer

In Cloud Logging, create a logs-based alert using the logs in the Vertex Al endpoint. Configure Cloud Logging to send an email when the alert is triggered.

Answer

In Cloud Monitoring create a logs-based metric and a threshold alert for the metric. Configure Cloud Monitoring to send an email when the alert is triggered.

Answer

Export the container logs of the endpoint to BigQuery Create a Cloud Function to run a SQL query over the exported logs and send an email. Use Cloud Scheduler to trigger the Cloud Function.

Question 25

You have trained an XGBoost model that you plan to deploy on Vertex Al for online prediction. You are now uploading your model to Vertex Al Model Registry, and you need to configure the explanation method that will serve online prediction requests to be returned with minimal latency. You also want to be alerted when feature attributions of the model meaningfully change over time. What should you do?

Accepted Answer

1 Specify sampled Shapley as the explanation method with a path count of 5. 2 Deploy the model to Vertex Al Endpoints. 3. Create a Model Monitoring job that uses prediction drift as the monitoring objective.

Answer

1 Specify Integrated Gradients as the explanation method with a path count of 5. 2 Deploy the model to Vertex Al Endpoints. 3. Create a Model Monitoring job that uses prediction drift as the monitoring objective.

Answer

1. Specify sampled Shapley as the explanation method with a path count of 50. 2. Deploy the model to Vertex Al Endpoints. 3. Create a Model Monitoring job that uses training-serving skew as the monitoring objective.

Answer

1 Specify Integrated Gradients as the explanation method with a path count of 50. 2. Deploy the model to Vertex Al Endpoints. 3 Create a Model Monitoring job that uses training-serving skew as the monitoring objective.

Question 26

You have recently developed a new ML model in a Jupyter notebook. You want to establish a reliable and repeatable model training process that tracks the versions and lineage of your model artifacts. You plan to retrain your model weekly. How should you operationalize your training process?

Accepted Answer

1. Create a managed pipeline in Vertex Al Pipelines to train your model by using a Vertex Al CustomTrainingJoOp component. 2. Use the ModelUploadOp component to upload your model to Vertex Al Model Registry. 3. Use Cloud Scheduler and Cloud Functions to run the Vertex Al pipeline weekly.

Answer

1. Create an instance of the CustomTrainingJob class with the Vertex AI SDK to train your model. 2. Using the Notebooks API, create a scheduled execution to run the training code weekly.

Answer

1. Create an instance of the CustomJob class with the Vertex AI SDK to train your model. 2. Use the Metadata API to register your model as a model artifact. 3. Using the Notebooks API, create a scheduled execution to run the training code weekly.

Answer

1. Create a managed pipeline in Vertex Al Pipelines to train your model using a Vertex Al HyperParameterTuningJobRunOp component. 2. Use the ModelUploadOp component to upload your model to Vertex Al Model Registry. 3. Use Cloud Scheduler and Cloud Functions to run the Vertex Al pipeline weekly.

Question 27

You have recently developed a custom model for image classification by using a neural network. You need to automatically identify the values for learning rate, number of layers, and kernel size. To do this, you plan to run multiple jobs in parallel to identify the parameters that optimize performance. You want to minimize custom code development and infrastructure management. What should you do?

Accepted Answer

Create a Vertex Al hyperparameter tuning job.

Answer

Create a Vertex Al pipeline that runs different model training jobs in parallel.

Answer

Train an AutoML image classification model.

Answer

Create a custom training job that uses the Vertex Al Vizier SDK for parameter optimization.

Question 28

You are training a deep learning model for semantic image segmentation with reduced training time. While using a Deep Learning VM Image, you receive the following error: The resource 'projects/deeplearning-platforn/zones/europe-west4-c/acceleratorTypes/nvidia-tesla-k80' was not found. What should you do?

Accepted Answer

Ensure that the required GPU is available in the selected region.

Answer

Ensure that you have GPU quota in the selected region.

Answer

Ensure that you have preemptible GPU quota in the selected region.

Answer

Ensure that the selected GPU has enough GPU memory for the workload.

Question 29

You are using Kubeflow Pipelines to develop an end-to-end PyTorch-based MLOps pipeline. The pipeline reads data from BigQuery, processes the data, conducts feature engineering, model training, model evaluation, and deploys the model as a binary file to Cloud Storage. You are writing code for several different versions of the feature engineering and model training steps, and running each new version in Vertex Al Pipelines.Each pipeline run is taking over an hour to complete. You want to speed up the pipeline execution to reduce your development time, and you want to avoid additional costs. What should you do?

Accepted Answer

Enable caching in all the steps of the Kubeflow pipeline.

Answer

Delegate feature engineering to BigQuery and remove it from the pipeline.

Answer

Add a GPU to the model training step.

Answer

Comment out the part of the pipeline that you are not currently updating.

Question 30

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?

Accepted Answer

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.

Answer

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

Answer

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.

Answer

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

Question 31

You work at an organization that maintains a cloud-based communication platform that integrates conventional chat, voice, and video conferencing into one platform. The audio recordings are stored in Cloud Storage. All recordings have an 8 kHz sample rate and are more than one minute long. You need to implement a new feature in the platform that will automatically transcribe voice call recordings into a text for future applications, such as call summarization and sentiment analysis. How should you implement the voice call transcription feature following Google-recommended best practices?

Accepted Answer

Upsample the audio recordings to 16 kHz. and transcribe the audio by using the Speech-to-Text API with asynchronous recognition.

Answer

Use the original audio sampling rate, and transcribe the audio by using the Speech-to-Text API with synchronous recognition.

Answer

Use the original audio sampling rate, and transcribe the audio by using the Speech-to-Text API with asynchronous recognition.

Answer

Upsample the audio recordings to 16 kHz. and transcribe the audio by using the Speech-to-Text API with synchronous recognition.

Question 32

You developed a Python module by using Keras to train a regression model. You developed two model architectures, linear regression and deep neural network (DNN). within the same module. You are using the -- raining_method argument to select one of the two methods, and you are using the Learning_rate-and num_hidden_layers arguments in the DNN. You plan to use Vertex Al's hypertuning service with a Budget to perform 100 trials. You want to identify the model architecture and hyperparameter values that minimize training loss and maximize model performance What should you do?

Accepted Answer

Run one hypertuning job for 100 trials Set num_hidden_layers and learning_rate as conditional hyperparameters based on their parent hyperparameter training method.

Answer

Run one hypertuning job for 100 trials. Set num hidden_layers as a conditional hypetparameter based on its parent hyperparameter training_mothod. and set learning rate as a non-conditional hyperparameter

Answer

Run two separate hypertuning jobs. a linear regression job for 50 trials, and a DNN job for 50 trials Compare their final performance on a common validation set. and select the set of hyperparameters with the least training loss

Answer

Run one hypertuning job with training_method as the hyperparameter for 50 trials Select the architecture with the lowest training loss. and further hypertune It and its corresponding hyperparameters for 50 trials

Question 33

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?

Accepted Answer

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

Answer

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

Answer

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

Answer

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.

Question 34

You have developed a fraud detection model for a large financial institution using Vertex AI. The model achieves high accuracy, but stakeholders are concerned about potential bias based on customer demographics. You have been asked to provide insights into the model's decision-making process and identify any fairness issues. What should you do?

Accepted Answer

Use feature attribution in Vertex AI to analyze model predictions and the impact of each feature on the model's predictions.

Answer

Enable Vertex AI Model Monitoring to detect training-serving skew. Configure an alert to send an email when the skew or drift for a model's feature exceeds a predefined threshold. Retrain the model by appending new data to existing training data.

Answer

Compile a dataset of unfair predictions. Use Vertex AI Vector Search to identify similar data points in the model's predictions. Report these data points to the stakeholders.

Answer

Create feature groups using Vertex AI Feature Store to segregate customer demographic features and non-demographic features. Retrain the model using only non-demographic features.

Question 35

You have created multiple versions of an ML model and have imported them to Vertex AI Model Registry. You want to perform A/B testing to identify the best-performing model using the simplest approach. What should you do?

Accepted Answer

Split incoming traffic to distribute prediction requests among the versions. Monitor the performance of each version using Vertex AI's built-in monitoring tools.

Answer

Split incoming traffic among separate Cloud Run instances of deployed models. Monitor the performance of each version using Cloud Monitoring.

Answer

Split incoming traffic to distribute prediction requests among the versions. Monitor the performance of each version using Looker Studio dashboards that compare logged data for each version.

Answer

Split incoming traffic among Google Kubernetes Engine (GKE) clusters and use Traffic Director to distribute prediction requests to different versions. Monitor the performance of each version using Cloud Monitoring.

Question 36

You are the lead ML engineer on a mission-critical project that involves analyzing massive datasets using Apache Spark. You need to establish a robust environment that allows your team to rapidly prototype Spark models using Jupyter notebooks. What is the fastest way to achieve this?

Accepted Answer

Set up a Dataproc cluster with Spark and use Jupyter notebooks.

Answer

Configure a Compute Engine instance with Spark and use Jupyter notebooks.

Answer

Set up a Vertex AI Workbench instance with a Spark kernel.

Answer

Use Colab Enterprise with a Spark kernel.

Question 37

You need to train a ControlNet model with Stable Diffusion XL for an image editing use case. You want to train this model as quickly as possible. Which hardware configuration should you choose to train your model?

Accepted Answer

Configure one a2-highgpu-1g instance with an NVIDIA A100 GPU with 80 GB of RAM. Use float32 precision during model training.

Answer

Configure one a2-highgpu-1g instance with an NVIDIA A100 GPU with 80 GB of RAM. Use bfloat16 quantization during model training.

Answer

Configure four n1-standard-16 instances, each with one NVIDIA Tesla T4 GPU with 16 GB of RAM. Use float32 precision during model training.

Answer

Configure four n1-standard-16 instances, each with one NVIDIA Tesla T4 GPU with 16 GB of RAM. Use float16 quantization during model training.

Question 38

You trained a model on data stored in a Cloud Storage bucket. The model needs to be retrained frequently in Vertex AI Training using the latest data in the bucket. Data preprocessing is required prior to retraining. You want to build a simple and efficient near-real-time ML pipeline in Vertex AI that will preprocess the data when new data arrives in the bucket. What should you do?

Accepted Answer

Create a Cloud Run function that is triggered when new data arrives in the bucket. The function initiates a Vertex AI Pipeline to preprocess the new data and store the processed features in Vertex AI Feature Store.

Answer

Create a pipeline using the Vertex AI SDK. Schedule the pipeline with Cloud Scheduler to preprocess the new data in the bucket. Store the processed features in Vertex AI Feature Store.

Answer

Build a Dataflow pipeline to preprocess the new data in the bucket and store the processed features in BigQuery. Configure a cron job to trigger the pipeline execution.

Answer

Use the Vertex AI SDK to preprocess the new data in the bucket prior to each model retraining. Store the processed features in BigQuery.

Question 39

You work as an ML researcher at an investment bank and are experimenting with the Gemini large language model (LLM). You plan to deploy the model for an internal use case and need full control of the model's underlying infrastructure while minimizing inference time. Which serving configuration should you use for this task?

Accepted Answer

Deploy the model on a Google Kubernetes Engine (GKE) cluster manually by creating a custom YAML manifest.

Answer

Deploy the model on a Vertex AI endpoint using one-click deployment in Model Garden.

Answer

Deploy the model on a Vertex AI endpoint manually by creating a custom inference container.

Answer

Deploy the model on a Google Kubernetes Engine (GKE) cluster using the deployment options in Model Garden.

Question 40

Your company needs to generate product summaries for vendors. You evaluated a foundation model from Model Garden for text summarization but found that the summaries do not align with your company's brand voice. How should you improve this LLM-based summarization model to better meet your business objectives?

Accepted Answer

Fine-tune the model using a company-specific dataset.

Answer

Increase the model's temperature parameter.

Answer

Tune the token output limit in the response.

Answer

Replace the pre-trained model with another model in Model Garden.

Google Professional Machine Learning Engineer Practice Test 7

Google Professional Machine Learning Engineer Practice Tests

Google Professional Machine Learning Engineer Practice Test 7

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Google Professional Machine Learning Engineer Practice Tests

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