Quiz IT certification exam

Question 1

You work for a retail company. You have created a Vertex Al forecast model that produces monthly item sales predictions. You want to quickly create a report that will help to explain how the model calculates the predictions. You have one month of recent actual sales data that was not included in the training dataset. How should you generate data for your report?

Accepted Answer

Create a batch prediction job by using the actual sates data and configure the job settings to generate feature attributions. Compare the results in the report.

Answer

Create a batch prediction job by using the actual sales data Compare the predictions to the actuals in the report.

Answer

Generate counterfactual examples by using the actual sales data Create a batch prediction job using the actual sales data and the counterfactual examples Compare the results in the report.

Answer

Train another model by using the same training dataset as the original and exclude some columns. Using the actual sales data create one batch prediction job by using the new model and another one with the original model Compare the two sets of predictions in the report.

Question 2

Your team has a model deployed to a Vertex Al endpoint You have created a Vertex Al pipeline that automates the model training process and is triggered by a Cloud Function. You need to prioritize keeping the model up-to-date, but also minimize retraining costs. How should you configure retraining'?

Accepted Answer

Enable model monitoring on the Vertex Al endpoint Configure Pub/Sub to call the Cloud Function when feature drift is detected.

Answer

Configure Pub/Sub to call the Cloud Function when a sufficient amount of new data becomes available.

Answer

Configure a Cloud Scheduler job that calls the Cloud Function at a predetermined frequency that fits your team's budget.

Answer

Enable model monitoring on the Vertex Al endpoint Configure Pub/Sub to call the Cloud Function when anomalies are detected.

Question 3

Your company stores a large number of audio files of phone calls made to your customer call center in an on-premises database. Each audio file is in wav format and is approximately 5 minutes long. You need to analyze these audio files for customer sentiment. You plan to use the Speech-to-Text API. You want to use the most efficient approach. What should you do?

Accepted Answer

1 Upload the audio files to Cloud Storage 2 Call the speech: Iongrunningrecognize API endpoint to generate transcriptions. 3 Create a Cloud Function that calls the Natural Language API by using the analyzesentiment method

Answer

1 Upload the audio files to Cloud Storage 2. Call the speech: Iongrunningrecognize API endpoint to generate transcriptions 3. Call the predict method of an AutoML sentiment analysis model to analyze the transcriptions

Answer

1 Iterate over your local Tiles in Python 2. Use the Speech-to-Text Python library to create a speech.RecognitionAudio object and set the content to the audio file data 3. Call the speech: recognize API endpoint to generate transcriptions 4. Call the predict method of an AutoML sentiment analysis model to analyze the transcriptions

Answer

1 Iterate over your local files in Python 2 Use the Speech-to-Text Python Library to create a speech.RecognitionAudio object, and set the content to the audio file data 3. Call the speech: lengrunningrecognize API endpoint to generate transcriptions 4 Call the Natural Language API by using the analyzesenriment method

Question 4

You work for a social media company. You want to create a no-code image classification model for an iOS mobile application to identify fashion accessories You have a labeled dataset in Cloud Storage You need to configure a training workflow that minimizes cost and serves predictions with the lowest possible latency What should you do?

Accepted Answer

Train the model by using AutoML Edge and export it as a Core ML model Configure your mobile application to use the mlmodel file directly.

Answer

Train the model by using AutoML, and register the model in Vertex Al Model Registry Configure your mobile application to send batch requests during prediction.

Answer

Train the model by using AutoML Edge and export the model as a TFLite model Configure your mobile application to use the tflite file directly

Answer

Train the model by using AutoML, and expose the model as a Vertex Al endpoint Configure your mobile application to invoke the endpoint during prediction.

Question 5

You work for a retail company. You have been asked to develop a model to predict whether a customer will purchase a product on a given day. Your team has processed the company's sales data, and created a table with the following rows:* Customer_id* Product_id* Date* Days_since_last_purchase (measured in days)* Average_purchase_frequency (measured in 1/days)* Purchase (binary class, if customer purchased product on the Date)You need to interpret your models results for each individual prediction. What should you do?

Accepted Answer

Create a Vertex Al tabular dataset Train an AutoML model to predict customer purchases Deploy the model to a Vertex Al endpoint and enable feature attributions Use the 'explain' method to get feature attribution values for each individual prediction.

Answer

Create a BigQuery table Use BigQuery ML to build a boosted tree classifier Inspect the partition rules of the trees to understand how each prediction flows through the trees.

Answer

Create a BigQuery table Use BigQuery ML to build a logistic regression classification model Use the values of the coefficients of the model to interpret the feature importance with higher values corresponding to more importance.

Answer

Create a Vertex Al tabular dataset Train an AutoML model to predict customer purchases Deploy the model to a Vertex Al endpoint. At each prediction enable L1 regularization to detect non-informative features.

Question 6

You work for a company that captures live video footage of checkout areas in their retail stores You need to use the live video footage to build a mode! to detect the number of customers waiting for service in near real time You want to implement a solution quickly and with minimal effort How should you build the model?

Accepted Answer

Use the Vertex Al Vision Occupancy Analytics model.

Answer

Use the Vertex Al Vision Person/vehicle detector model

Answer

Train an AutoML object detection model on an annotated dataset by using Vertex AutoML

Answer

Train a Seq2Seq+ object detection model on an annotated dataset by using Vertex AutoML

Question 7

You work as an analyst at a large banking firm. You are developing a robust, scalable ML pipeline to train several regression and classification models. Your primary focus for the pipeline is model interpretability. You want to productionize the pipeline as quickly as possible What should you do?

Accepted Answer

Use Cloud Composer to build the training pipelines for custom deep learning-based models.

Answer

Use Tabular Workflow for Wide & Deep through Vertex Al Pipelines to jointly train wide linear models and deep neural networks.

Answer

Use Google Kubernetes Engine to build a custom training pipeline for XGBoost-based models.

Answer

Use Tabular Workflow forTabel through Vertex Al Pipelines to train attention-based models.

Question 8

You developed a Transformer model in TensorFlow to translate text Your training data includes millions of documents in a Cloud Storage bucket. You plan to use distributed training to reduce training time. You need to configure the training job while minimizing the effort required to modify code and to manage the clusters configuration. What should you do?

Accepted Answer

Create a training job that uses Cloud TPU VMs Use tf.distribute.TPUStrategy for distribution.

Answer

Create a Vertex Al custom training job with GPU accelerators for the second worker pool Use tf .distribute.MultiWorkerMirroredStrategy for distribution.

Answer

Create a Vertex Al custom distributed training job with Reduction Server Use N1 high-memory machine type instances for the first and second pools, and use N1 high-CPU machine type instances for the third worker pool.

Answer

Create a Vertex Al custom training job with a single worker pool of A2 GPU machine type instances Use tf .distribute.MirroredStraregy for distribution.

Question 9

You are developing a process for training and running your custom model in production. You need to be able to show lineage for your model and predictions. What should you do?

Accepted Answer

1 Use Vertex Al Experiments to train your model. 2 Register your model in Vertex Al Model Registry 3. Generate batch predictions in Vertex Al

Answer

1 Create a Vertex Al managed dataset 2 Use a Vertex Ai training pipeline to train your model 3 Generate batch predictions in Vertex Al

Answer

1 Use a Vertex Al Pipelines custom training job component to train your model 2. Generate predictions by using a Vertex Al Pipelines model batch predict component

Answer

1 Upload your dataset to BigQuery 2. Use a Vertex Al custom training job to train your model 3 Generate predictions by using Vertex Al SDK custom prediction routines

Question 10

You work for a hotel and have a dataset that contains customers' written comments scanned from paper-based customer feedback forms which are stored as PDF files Every form has the same layout. You need to quickly predict an overall satisfaction score from the customer comments on each form. How should you accomplish this task'?

Accepted Answer

Uptrain a Document Al custom extractor to parse the text in the comments section of each PDF file. Use the Natural Language API analyze sentiment feature to infer overall satisfaction scores.

Answer

Use the Vision API to parse the text from each PDF file Use the Natural Language API analyzesentiment feature to infer overall satisfaction scores.

Answer

Use the Vision API to parse the text from each PDF file Use the Natural Language API analyzeEntitysentiment feature to infer overall satisfaction scores.

Answer

Uptrain a Document Al custom extractor to parse the text in the comments section of each PDF file. Use the Natural Language API analyzeEntitySentiment feature to infer overall satisfaction scores.

Question 11

You developed a Vertex Al pipeline that trains a classification model on data stored in a large BigQuery table. The pipeline has four steps, where each step is created by a Python function that uses the KubeFlow v2 API The components have the following names: Google Professional Machine Learning Engineer image Question 171 30336 09182024191449000000

Google Professional Machine Learning Engineer image Question 171 30336 09182024191449000000

You launch your Vertex Al pipeline as the following: Google Professional Machine Learning Engineer image Question 171 30336 09182024191449000000

You perform many model iterations by adjusting the code and parameters of the training step. You observe high costs associated with the development, particularly the data export and preprocessing steps. You need to reduce model development costs. What should you do?

Accepted Answer

Question 12

You work for a startup that has multiple data science workloads. Your compute infrastructure is currently on-premises. and the data science workloads are native to PySpark Your team plans to migrate their data science workloads to Google Cloud You need to build a proof of concept to migrate one data science job to Google Cloud You want to propose a migration process that requires minimal cost and effort. What should you do first?

Accepted Answer

Create a Standard (1 master. 3 workers) Dataproc cluster, and run a Vertex Al Workbench notebook instance on it.

Answer

Create a n2-standard-4 VM instance and install Java, Scala and Apache Spark dependencies on it.

Answer

Create a Google Kubemetes Engine cluster with a basic node pool configuration install Java Scala, and Apache Spark dependencies on it.

Answer

Create a Vertex Al Workbench notebook with instance type n2-standard-4.

Question 13

You work for a bank You have been asked to develop an ML model that will support loan application decisions. You need to determine which Vertex Al services to include in the workflow You want to track the model's training parameters and the metrics per training epoch. You plan to compare the performance of each version of the model to determine the best model based on your chosen metrics. Which Vertex Al services should you use?

Accepted Answer

Vertex ML Metadata Vertex Al Experiments, and Vertex Al TensorBoard

Answer

Vertex ML Metadata Vertex Al Feature Store, and Vertex Al Vizier

Answer

Vertex Al Pipelines. Vertex Al Experiments, and Vertex Al Vizier

Answer

Vertex Al Pipelines. Vertex Al Feature Store, and Vertex Al TensorBoard

Question 14

You work for an auto insurance company. You are preparing a proof-of-concept ML application that uses images of damaged vehicles to infer damaged parts Your team has assembled a set of annotated images from damage claim documents in the company's database The annotations associated with each image consist of a bounding box for each identified damaged part and the part name. You have been given a sufficient budget to tram models on Google Cloud You need to quickly create an initial model What should you do?

Accepted Answer

Train an object detection model in AutoML by using the annotated image data.

Answer

Download a pre-trained object detection mode! from TensorFlow Hub Fine-tune the model in Vertex Al Workbench by using the annotated image data.

Answer

Create a pipeline in Vertex Al Pipelines and configure the AutoMLTrainingJobRunOp compon it to train a custom object detection model by using the annotated image data.

Answer

Train an object detection model in Vertex Al custom training by using the annotated image data.

Question 15

You are analyzing customer data for a healthcare organization that is stored in Cloud Storage. The data contains personally identifiable information (PII) You need to perform data exploration and preprocessing while ensuring the security and privacy of sensitive fields What should you do?

Accepted Answer

Use the Cloud Data Loss Prevention (DLP) API to de-identify the PI! before performing data exploration and preprocessing.

Answer

Use customer-managed encryption keys (CMEK) to encrypt the Pll data at rest and decrypt the Pll data during data exploration and preprocessing.

Answer

Use a VM inside a VPC Service Controls security perimeter to perform data exploration and preprocessing.

Answer

Use Google-managed encryption keys to encrypt the Pll data at rest, and decrypt the Pll data during data exploration and preprocessing.

Question 16

You are building a predictive maintenance model to preemptively detect part defects in bridges. You plan to use high definition images of the bridges as model inputs. You need to explain the output of the model to the relevant stakeholders so they can take appropriate action. How should you build the model?

Accepted Answer

Use TensorFlow to create a deep learning-based model and use Integrated Gradients to explain the model output.

Answer

Use scikit-learn to build a tree-based model, and use SHAP values to explain the model output.

Answer

Use scikit-lean to build a tree-based model, and use partial dependence plots (PDP) to explain the model output.

Answer

Use TensorFlow to create a deep learning-based model and use the sampled Shapley method to explain the model output.

Question 17

You work for a hospital that wants to optimize how it schedules operations. You need to create a model that uses the relationship between the number of surgeries scheduled and beds used You want to predict how many beds will be needed for patients each day in advance based on the scheduled surgeries You have one year of data for the hospital organized in 365 rowsThe data includes the following variables for each day* Number of scheduled surgeries* Number of beds occupied* DateYou want to maximize the speed of model development and testing What should you do?

Accepted Answer

Create a Vertex Al tabular dataset Train a Vertex Al AutoML Forecasting model with number of beds as the target variable, number of scheduled surgeries as a covariate, and date as the time variable.

Answer

Create a BigQuery table Use BigQuery ML to build a regression model, with number of beds as the target variable and number of scheduled surgeries and date features (such as day of week) as the predictors

Answer

Create a BigQuery table Use BigQuery ML to build an ARIMA model, with number of beds as the target variable and date as the time variable.

Answer

Create a Vertex Al tabular dataset Tram an AutoML regression model, with number of beds as the target variable and number of scheduled minor surgeries and date features (such as day of the week) as the predictors

Question 18

You recently developed a wide and deep model in TensorFlow. You generated training datasets using a SQL script that preprocessed raw data in BigQuery by performing instance-level transformations of the data. You need to create a training pipeline to retrain the model on a weekly basis. The trained model will be used to generate daily recommendations. You want to minimize model development and training time. How should you develop the training pipeline?

Accepted Answer

Use the TensorFlow Extended SDK to implement the pipeline Use the Examplegen component with the BigQuery executor to ingest the data the Transform component to preprocess the data, and the Trainer component to launch a Vertex Al training job.

Answer

Use the Kubeflow Pipelines SDK to implement the pipeline Use the BigQueryJobop component to run the preprocessing script and the customTrainingJobop component to launch a Vertex Al training job.

Answer

Use the Kubeflow Pipelines SDK to implement the pipeline. Use the dataflowpythonjobopcomponent to preprocess the data and the customTraining JobOp component to launch a Vertex Al training job.

Answer

Use the TensorFlow Extended SDK to implement the pipeline Implement the preprocessing steps as part of the input_fn of the model Use the ExampleGen component with the BigQuery executor to ingest the data and the Trainer component to launch a Vertex Al training job.

Question 19

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?

Accepted Answer

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.

Answer

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.

Answer

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.

Answer

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.

Question 20

You have trained a model by using data that was preprocessed in a batch Dataflow pipeline Your use case requires real-time inference. You want to ensure that the data preprocessing logic is applied consistently between training and serving. What should you do?

Accepted Answer

Refactor the transformation code in the batch data pipeline so that it can be used outside of the pipeline Use the same code in the endpoint.

Answer

Perform data validation to ensure that the input data to the pipeline is the same format as the input data to the endpoint.

Answer

Refactor the transformation code in the batch data pipeline so that it can be used outside of the pipeline Share this code with the end users of the endpoint.

Answer

Batch the real-time requests by using a time window and then use the Dataflow pipeline to preprocess the batched requests. Send the preprocessed requests to the endpoint.

Question 21

You need to develop a custom TensorRow model that will be used for online predictions. The training data is stored in BigQuery. You need to apply instance-level data transformations to the data for model training and serving. You want to use the same preprocessing routine during model training and serving. How should you configure the preprocessing routine?

Accepted Answer

Create an Apache Beam pipeline to read the data from BigQuery and preprocess it by using TensorFlow Transform and Dataflow.

Answer

Create a BigQuery script to preprocess the data, and write the result to another BigQuery table.

Answer

Create a pipeline in Vertex Al Pipelines to read the data from BigQuery and preprocess it using a custom preprocessing component.

Answer

Create a preprocessing function that reads and transforms the data from BigQuery Create a Vertex Al custom prediction routine that calls the preprocessing function at serving time.

Question 22

You are pre-training a large language model on Google Cloud. This model includes custom TensorFlow operations in the training loop Model training will use a large batch size, and you expect training to take several weeks You need to configure a training architecture that minimizes both training time and compute costs What should you do?

Accepted Answer

Question 23

You are building a TensorFlow text-to-image generative model by using a dataset that contains billions of images with their respective captions. You want to create a low maintenance, automated workflow that reads the data from a Cloud Storage bucket collects statistics, splits the dataset into training/validation/test datasets performs data transformations, trains the model using the training/validation datasets. and validates the model by using the test dataset. What should you do?

Accepted Answer

Use the TensorFlow Extended (TFX) SDK to create multiple components that use Dataflow and Vertex Al services Deploy the workflow on Vertex Al Pipelines.

Answer

Use the Apache Airflow SDK to create multiple operators that use Dataflow and Vertex Al services Deploy the workflow on Cloud Composer.

Answer

Use the MLFlow SDK and deploy it on a Google Kubernetes Engine Cluster Create multiple components that use Dataflow and Vertex Al services.

Answer

Use the Kubeflow Pipelines (KFP) SDK to create multiple components that use Dataflow and Vertex Al services Deploy the workflow on Vertex Al Pipelines.

Question 24

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?

Accepted Answer

Chain the Vertex Al ModelUploadOp and ModelDeployop components together.

Answer

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

Answer

Use the Vertex Al ModelEvaluationOp component to evaluate the model.

Answer

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

Question 25

You work for an online retailer. Your company has a few thousand short lifecycle products. Your company has five years of sales data stored in BigQuery. You have been asked to build a model that will make monthly sales predictions for each product. You want to use a solution that can be implemented quickly with minimal effort. What should you do?

Accepted Answer

Use BigQuery ML to build a statistical AR1MA_PLUS model.

Answer

Use Prophet on Vertex Al Training to build a custom model.

Answer

Use Vertex Al Forecast to build a NN-based model.

Answer

Use TensorFlow on Vertex Al Training to build a custom model.

Question 26

You are creating a model training pipeline to predict sentiment scores from text-based product reviews. You want to have control over how the model parameters are tuned, and you will deploy the model to an endpoint after it has been trained You will use Vertex Al Pipelines to run the pipeline You need to decide which Google Cloud pipeline components to use What components should you choose?

Accepted Answer

Question 27

Your team frequently creates new ML models and runs experiments. Your team pushes code to a single repository hosted on Cloud Source Repositories. You want to create a continuous integration pipeline that automatically retrains the models whenever there is any modification of the code. What should be your first step to set up the CI pipeline?

Accepted Answer

Configure a Cloud Build trigger with the event set as 'Push to a branch'

Answer

Configure a Cloud Build trigger with the event set as 'Pull Request'

Answer

Configure a Cloud Function that builds the repository each time there is a code change.

Answer

Configure a Cloud Function that builds the repository each time a new branch is created.

Question 28

You have built a custom model that performs several memory-intensive preprocessing tasks before it makes a prediction. You deployed the model to a Vertex Al endpoint. and validated that results were received in a reasonable amount of time After routing user traffic to the endpoint, you discover that the endpoint does not autoscale as expected when receiving multiple requests What should you do?

Accepted Answer

Decrease the CPU utilization target in the autoscaling configurations

Answer

Use a machine type with more memory

Answer

Decrease the number of workers per machine

Answer

Increase the CPU utilization target in the autoscaling configurations

Question 29

Your company manages an ecommerce website. You developed an ML model that recommends additional products to users in near real time based on items currently in the user's cart. The workflow will include the following processes.1 The website will send a Pub/Sub message with the relevant data and then receive a message with the prediction from Pub/Sub.2 Predictions will be stored in BigQuery3. The model will be stored in a Cloud Storage bucket and will be updated frequentlyYou want to minimize prediction latency and the effort required to update the model How should you reconfigure the architecture?

Accepted Answer

Use the Runlnference API with watchFilePatterr. in a Dataflow job that wraps around the model and serves predictions.

Answer

Write a Cloud Function that loads the model into memory for prediction Configure the function to be triggered when messages are sent to Pub/Sub.

Answer

Create a pipeline in Vertex Al Pipelines that performs preprocessing, prediction and postprocessing Configure the pipeline to be triggered by a Cloud Function when messages are sent to Pub/Sub.

Answer

Expose the model as a Vertex Al endpoint Write a custom DoFn in a Dataflow job that calls the endpoint for prediction.

Question 30

You are collaborating on a model prototype with your team. You need to create a Vertex Al Workbench environment for the members of your team and also limit access to other employees in your project. What should you do?

Accepted Answer

1 Create a new service account and grant it the Vertex Al User role. 2 Grant the Service Account User role to each team member on the service account. 3. Grant the Notebook Viewer role to each team member. 4 Provision a Vertex Al Workbench user-managed notebook instance that uses the new service account.

Answer

1. Create a new service account and grant it the Notebook Viewer role. 2 Grant the Service Account User role to each team member on the service account. 3 Grant the Vertex Al User role to each team member. 4. Provision a Vertex Al Workbench user-managed notebook instance that uses the new service account.

Answer

1. Grant the Vertex Al User role to the default Compute Engine service account. 2. Grant the Service Account User role to each team member on the default Compute Engine service account. 3. Provision a Vertex Al Workbench user-managed notebook instance that uses the default Compute Engine service account.

Answer

1 Grant the Vertex Al User role to the primary team member. 2. Grant the Notebook Viewer role to the other team members. 3. Provision a Vertex Al Workbench user-managed notebook instance that uses the primary user's account.

Question 31

You work at a leading healthcare firm developing state-of-the-art algorithms for various use cases You have unstructured textual data with custom labels You need to extract and classify various medical phrases with these labels What should you do?

Accepted Answer

Use a BERT-based model to fine-tune a medical entity extraction model.

Answer

Use the Healthcare Natural Language API to extract medical entities.

Answer

Use AutoML Entity Extraction to train a medical entity extraction model.

Answer

Use TensorFlow to build a custom medical entity extraction model.

Question 32

You developed a custom model by using Vertex Al to predict your application's user churn rate You are using Vertex Al Model Monitoring for skew detection The training data stored in BigQuery contains two sets of features - demographic and behavioral You later discover that two separate models trained on each set perform better than the original modelYou need to configure a new model mentioning pipeline that splits traffic among the two models You want to use the same prediction-sampling-rate and monitoring-frequency for each model You also want to minimize management effort What should you do?

Accepted Answer

Separate the training dataset into two tables based on demographic and behavioral features. Deploy both models to the same endpoint and submit a Vertex Al Model Monitoring job with a monitoring-config-from parameter that accounts for the model IDs and training datasets

Answer

Keep the training dataset as is Deploy the models to two separate endpoints and submit two Vertex Al Model Monitoring jobs with appropriately selected feature-thresholds parameters

Answer

Keep the training dataset as is Deploy both models to the same endpoint and submit a Vertex Al Model Monitoring job with a monitoring-config-from parameter that accounts for the model IDs and feature selections

Answer

Separate the training dataset into two tables based on demographic and behavioral features Deploy the models to two separate endpoints, and submit two Vertex Al Model Monitoring jobs

Question 33

You work for a pharmaceutical company based in Canada. Your team developed a BigQuery ML model to predict the number of flu infections for the next month in Canada Weather data is published weekly and flu infection statistics are published monthly. You need to configure a model retraining policy that minimizes cost What should you do?

Accepted Answer

Download the weather data each week, and download the flu data each month Deploy the model to a Vertex Al endpoint with feature drift monitoring. and retrain the model if a monitoring alert is detected.

Answer

Download the weather and flu data each week Configure Cloud Scheduler to execute a Vertex Al pipeline to retrain the model weekly.

Answer

Download the weather and flu data each month Configure Cloud Scheduler to execute a Vertex Al pipeline to retrain the model monthly.

Answer

Download the weather and flu data each week Configure Cloud Scheduler to execute a Vertex Al pipeline to retrain the model every month.

Question 34

You are building a MLOps platform to automate your company's ML experiments and model retraining. You need to organize the artifacts for dozens of pipelines How should you store the pipelines' artifacts'?

Accepted Answer

Store parameters in Vertex ML Metadata store the models' source code in GitHub and store the models' binaries in Cloud Storage.

Answer

Store parameters in Cloud SQL and store the models' source code and binaries in GitHub

Answer

Store parameters in Cloud SQL store the models' source code in GitHub, and store the models' binaries in Cloud Storage.

Answer

Store parameters in Vertex ML Metadata and store the models source code and binaries in GitHub.

Question 35

You work for a telecommunications company You're building a model to predict which customers may fail to pay their next phone bill. The purpose of this model is to proactively offer at-risk customers assistance such as service discounts and bill deadline extensions. The data is stored in BigQuery, and the predictive features that are available for model training include- Customer_id -Age- Salary (measured in local currency) -Sex-Average bill value (measured in local currency)- Number of phone calls in the last month (integer) -Average duration of phone calls (measured in minutes)You need to investigate and mitigate potential bias against disadvantaged groups while preserving model accuracy What should you do?

Accepted Answer

Define a fairness metric that is represented by accuracy across the sensitive features Train a BigQuery ML boosted trees classification model with all features Use the trained model to make predictions on a test set Join the data back with the sensitive features, and calculate a fairness metric to investigate whether it meets your requirements.

Answer

Determine whether there is a meaningful correlation between the sensitive features and the other features Train a BigQuery ML boosted trees classification model and exclude the sensitive features and any meaningfully correlated features

Answer

Train a BigQuery ML boosted trees classification model with all features Use the ml. global explain method to calculate the global attribution values for each feature of the model If the feature importance value for any of the sensitive features exceeds a threshold, discard the model and tram without this feature

Answer

Train a BigQuery ML boosted trees classification model with all features Use the ml. exflain_predict method to calculate the attribution values for each feature for each customer in a test set If for any individual customer the importance value for any feature exceeds a predefined threshold, discard the model and train the model again without this feature.

Question 36

You recently trained a XGBoost model that you plan to deploy to production for online inference Before sending a predict request to your model's binary you need to perform a simple data preprocessing step This step exposes a REST API that accepts requests in your internal VPC Service Controls and returns predictions You want to configure this preprocessing step while minimizing cost and effort What should you do?

Accepted Answer

Build a custom predictor class based on XGBoost Predictor from the Vertex Al SDK and package the handler in a custom container image based on a Vertex built-in container image Store a pickled model in Cloud Storage and deploy the model to Vertex Al Endpoints.

Answer

Store a pickled model in Cloud Storage Build a Flask-based app packages the app in a custom container image, and deploy the model to Vertex Al Endpoints.

Answer

Build a Flask-based app. package the app and a pickled model in a custom container image, and deploy the model to Vertex Al Endpoints.

Answer

Build a custom predictor class based on XGBoost Predictor from the Vertex Al SDK. package it and a pickled model in a custom container image based on a Vertex built-in image, and deploy the model to Vertex Al Endpoints.

Question 37

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?

Accepted Answer

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

Answer

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

Answer

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

Answer

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

Question 38

You are investigating the root cause of a misclassification error made by one of your models. You used Vertex Al Pipelines to tram and deploy the model. The pipeline reads data from BigQuery. creates a copy of the data in Cloud Storage in TFRecord format trains the model in Vertex Al Training on that copy, and deploys the model to a Vertex Al endpoint. You have identified the specific version of that model that misclassified: and you need to recover the data this model was trained on. How should you find that copy of the data'?

Accepted Answer

Use the lineage feature of Vertex Al Metadata to find the model artifact Determine the version of the model and identify the step that creates the data copy, and search in the metadata for its location.

Answer

Use Vertex Al Feature Store Modify the pipeline to use the feature store; and ensure that all training data is stored in it Search the feature store for the data used for the training.

Answer

Use the logging features in the Vertex Al endpoint to determine the timestamp of the models deployment Find the pipeline run at that timestamp Identify the step that creates the data copy; and search in the logs for its location.

Answer

Find the job ID in Vertex Al Training corresponding to the training for the model Search in the logs of that job for the data used for the training.

Question 39

You work for a manufacturing company. You need to train a custom image classification model to detect product defects at the end of an assembly line Although your model is performing well some images in your holdout set are consistently mislabeled with high confidence You want to use Vertex Al to understand your model's results What should you do?

Accepted Answer

Question 40

You are using Keras and TensorFlow to develop a fraud detection model Records of customer transactions are stored in a large table in BigQuery. You need to preprocess these records in a cost-effective and efficient way before you use them to train the model. The trained model will be used to perform batch inference in BigQuery. How should you implement the preprocessing workflow?

Accepted Answer

Perform preprocessing in BigQuery by using SQL Use the BigQueryClient in TensorFlow to read the data directly from BigQuery.

Answer

Implement a preprocessing pipeline by using Apache Spark, and run the pipeline on Dataproc Save the preprocessed data as CSV files in a Cloud Storage bucket.

Answer

Load the data into a pandas DataFrame Implement the preprocessing steps using panda's transformations. and train the model directly on the DataFrame.

Answer

Implement a preprocessing pipeline by using Apache Beam, and run the pipeline on Dataflow Save the preprocessed data as CSV files in a Cloud Storage bucket.

Google Professional Machine Learning Engineer Practice Test 5

Google Professional Machine Learning Engineer Practice Tests