Home / Google / Professional Machine Learning Engineer

Google Professional Machine Learning Engineer Practice Test - Questions Answers, Page 24

Question list

List of questions

Question 231

(0)

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 analys

Question 232

(0)

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 bat

Question 233

(0)

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 numeri

Question 234

(0)

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

Question 235

(0)

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 ima

Question 236

(0)

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

Question 237

(0)

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 p

Question 238

(0)

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

Question 239

(0)

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

Question 240

(0)

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 the

Related questions

You are implementing a batch inference ML pipeline in Google Cloud. The model was developed by using TensorFlow and is stored in SavedModel format in Cloud Storage. You need to apply the model to a historical dataset that is stored in a BigQuery table. You want to perform inference with minimal effort. What should you do?

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?

You are an ML engineer at a large grocery retailer with stores in multiple regions. You have been asked to create an inventory prediction model. Your models features include region, location, historical demand, and seasonal popularity. You want the algorithm to learn from new inventory data on a daily basis. Which algorithms should you use to build the model?

Your team trained and tested a DNN regression model with good results. Six months after deployment, the model is performing poorly due to a change in the distribution of the input data. How should you address the input differences in production?

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?

You are an AI architect at a popular photo-sharing social media platform. Your organization's content moderation team currently scans images uploaded by users and removes explicit images manually. You want to implement an AI service to automatically prevent users from uploading explicit images. What should you do?

You work for an online retail company that is creating a visual search engine. You have set up an end-to-end ML pipeline on Google Cloud to classify whether an image contains your company's product. Expecting the release of new products in the near future, you configured a retraining functionality in the pipeline so that new data can be fed into your ML models. You also want to use Al Platform's continuous evaluation service to ensure that the models have high accuracy on your test data set. What should you do?

You are an AI engineer working for a popular video streaming platform. You built a classification model using PyTorch to predict customer churn. Each week, the customer retention team plans to contact customers identified as at-risk for churning with personalized offers. You want to deploy the model while minimizing maintenance effort. What should you do?

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

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?

Question 231

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?

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.

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

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

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

Show Answer Comment (0)

Question 232

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?

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

Enable autoscaling of the online serving nodes in your featurestore

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

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

Show Answer Comment (0)

Question 233

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?

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.

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.

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.

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.

Show Answer Comment (0)

Question 234

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?

Store image files in Cloud Storage and access them directly.

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

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

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

Show Answer Comment (0)

Question 235

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?

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.

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.

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

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.

Show Answer Comment (0)

Question 236

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?

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.

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

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

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.

Show Answer Comment (0)

Question 237

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?

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.

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.

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.

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.

Show Answer Comment (0)

Question 238

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?

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.

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.

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.

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.

Show Answer Comment (0)

Question 239

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?

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

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

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

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

Show Answer Comment (0)

Question 240

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?

Show Answer Comment (0)

Total 285 questions

22 23 24 25 26

Go to page: of 29