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WorksheetsPMLE 51-100
Total questions: 50
Worksheet time: 2hrs 40mins
Name
Class
Date
1.
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?
Your model performs well, but just before deploying it to production, you discover that your current serving latency is 10ms @ 90 percentile and you currently serve on CPUs. Your production requirements expect a model latency of 8ms @ 90 percentile. You're willing to accept a small decrease in performance in order to reach the latency requirement.
Therefore your plan is to improve latency while evaluating how much the model's prediction decreases. What should you first try to quickly lower the serving latency?
a)
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.
b)
Create a Vertex AI Workbench user-managed notebooks instance with 4 V100 GPUs, and use it to train your model.
c)
Package your code with Setuptools, and use a pre-built container. Train your model with Vertex AI using a custom tier that contains the required GPUs.
d)
Configure a Compute Engine VM with all the dependencies that launches the training. Train your model with Vertex AI using a custom tier that contains the required GPUs.
2.
You have trained a DNN regressor with TensorFlow to predict housing prices using a set of predictive features. Your default precision is tf.float64, and you use a standard TensorFlow estimator:
a)
Switch from CPU to GPU serving.
b)
Apply quantization to your SavedModel by reducing the floating point precision to tf.float16.
c)
Increase the dropout rate to 0.8 and retrain your model.
d)
Increase the dropout rate to 0.8 in _PREDICT mode by adjusting the TensorFlow Serving parameters
3.
You work on the data science team at a manufacturing company. You are reviewing the company’s historical sales data, which has hundreds of millions of records. For your exploratory data analysis, you need to calculate descriptive statistics such as mean, median, and mode; conduct complex statistical tests for hypothesis testing; and plot variations of the features over time. You want to use as much of the sales data as possible in your analyses while minimizing computational resources. What should you do?
a)
Visualize the time plots in Google Data Studio. Import the dataset into Vertex Al Workbench user-managed notebooks. Use this data to calculate the descriptive statistics and run the statistical analyses.
b)
Spin up a Vertex Al Workbench user-managed notebooks instance and import the dataset. Use this data to create statistical and visual analyses.
c)
Use BigQuery to calculate the descriptive statistics. Use Vertex Al Workbench user-managed notebooks to visualize the time plots and run the statistical analyses.
d)
Use BigQuery to calculate the descriptive statistics, and use Google Data Studio to visualize the time plots. Use Vertex Al Workbench user-managed notebooks to run the statistical analyses.
4.
Your data science team needs to rapidly experiment with various features, model architectures, and hyperparameters. They need to track the accuracy metrics for various experiments and use an API to query the metrics over time. What should they use to track and report their experiments while minimizing manual effort?
a)
Use Vertex Al Pipelines to execute the experiments. Query the results stored in MetadataStore using the Vertex Al API.
b)
Use Vertex Al Training to execute the experiments. Write the accuracy metrics to BigQuery, and query the results using the BigQuery API.
c)
Use Vertex Al Training to execute the experiments. Write the accuracy metrics to Cloud Monitoring, and query the results using the Monitoring API.
d)
Use Vertex Al Workbench user-managed notebooks to execute the experiments. Collect the results in a shared Google Sheets file, and query the results using the Google Sheets API.
5.
You are training an ML model using data stored in BigQuery that contains several values that are considered Personally Identifiable Information (PII). You need to reduce the sensitivity of the dataset before training your model. Every column is critical to your model. How should you proceed?
a)
Using Dataflow, ingest the columns with sensitive data from BigQuery, and then randomize the values in each sensitive column.
b)
Using Dataflow, ingest the columns with sensitive data from BigQuery, and then randomize the values in each sensitive column.
c)
Using Dataflow, ingest the columns with sensitive data from BigQuery, and then randomize the values in each sensitive column.
d)
Using Dataflow, ingest the columns with sensitive data from BigQuery, and then randomize the values in each sensitive column.
6.
You recently deployed an ML model. Three months after deployment, you notice that your model is underperforming on certain subgroups, thus potentially leading to biased results. You suspect that the inequitable performance is due to class imbalances in the training data, but you cannot collect more data. What should you do? (Choose two.)
a)
Remove training examples of high-performing subgroups, and retrain the model.
b)
Add an additional objective to penalize the model more for errors made on the minority class, and retrain the model
c)
Remove the features that have the highest correlations with the majority class.
d)
Upsample or reweight your existing training data, and retrain the model
e)
Redeploy the model, and provide a label explaining the model's behavior to users.
7.
You have been asked to develop an input pipeline for an ML training model that processes images from disparate sources at a low latency. You discover that your input data does not fit in memory. How should you create a dataset following Google-recommended best practices?
a)
Create a tf.data.Dataset.prefetch transformation.
b)
Convert the images to tf.Tensor objects, and then run Dataset.from_tensor_slices().
c)
Convert the images to tf.Tensor objects, and then run tf.data.Dataset.from_tensors().
d)
Convert the images into TFRecords, store the images in Cloud Storage, and then use the tf.data API to read the images for training.
8.
You are working on a binary classification ML algorithm that detects whether an image of a classified scanned document contains a company’s logo. In the dataset, 96% of examples don’t have the logo, so the dataset is very skewed. Which metric would give you the most confidence in your model?
a)
Precision
b)
Recall
c)
RMSE
d)
F1 Score
9.
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?
a)
Include the flag -runner=DataflowRunner in beam_pipeline_args to run the evaluation step on Dataflow.
b)
Move the evaluation step out of your pipeline and run it on custom Compute Engine VMs with sufficient memory.
c)
Migrate your pipeline to Kubeflow hosted on Google Kubernetes Engine, and specify the appropriate node parameters for the evaluation step.
d)
Add tfma.MetricsSpec () to limit the number of metrics in the evaluation step.
10.
You are developing an ML model using a dataset with categorical input variables. You have randomly split half of the data into training and test sets. After applying one-hot encoding on the categorical variables in the training set, you discover that one categorical variable is missing from the test set. What should you do?
a)
Use sparse representation in the test set.
b)
Randomly redistribute the data, with 70% for the training set and 30% for the test set
c)
Apply one-hot encoding on the categorical variables in the test data
d)
Collect more data representing all categories
11.
You work for a bank and are building a random forest model for fraud detection. You have a dataset that includes transactions, of which 1% are identified as fraudulent. Which data transformation strategy would likely improve the performance of your classifier?
a)
Modify the target variable using the Box-Cox transformation.
b)
Z-normalize all the numeric features.
c)
Oversample the fraudulent transaction 10 times.
d)
Log transform all numeric features.
12.
You are developing a classification model to support predictions for your company’s various products. The dataset you were given for model development has class imbalance You need to minimize false positives and false negatives What evaluation metric should you use to properly train the model?
a)
F1 Score
b)
Recall
c)
Accuracy
d)
Precision
13.
You are training an object detection machine learning model on a dataset that consists of three million X-ray images, each roughly 2 GB in size. You are using Vertex AI Training to run a custom training application on a Compute Engine instance with 32-cores, 128 GB of RAM, and 1 NVIDIA P100 GPU. You notice that model training is taking a very long time. You want to decrease training time without sacrificing model performance. What should you do?
a)
Increase the instance memory to 512 GB, and increase the batch size.
b)
Replace the NVIDIA P100 GPU with a K80 GPU in the training job.
c)
Enable early stopping in your Vertex AI Training job.
d)
Use the tf.distribute.Strategy API and run a distributed training job.
14.
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?
a)
Train a TensorFlow model on Vertex AI.
b)
Train a classification Vertex AutoML model.
c)
Run a logistic regression job on BigQuery ML.
d)
Use scikit-learn in Vertex AI Workbench user-managed notebooks with pandas library.
15.
You recently developed a deep learning model. To test your new model, you trained it for a few epochs on a large dataset. You observe that the training and validation losses barely changed during the training run. You want to quickly debug your model. What should you do first?
a)
Verify that your model can obtain a low loss on a small subset of the dataset
b)
Add handcrafted features to inject your domain knowledge into the model
c)
Use the Vertex AI hyperparameter tuning service to identify a better learning rate
d)
Use hardware accelerators and train your model for more epochs
16.
You are a data scientist at an industrial equipment manufacturing company. You are developing a regression model to estimate the power consumption in the company’s manufacturing plants based on sensor data collected from all of the plants. The sensors collect tens of millions of records every day. You need to schedule daily training runs for your model that use all the data collected up to the current date. You want your model to scale smoothly and require minimal development work. What should you do?
a)
Develop a custom TensorFlow regression model, and optimize it using Vertex AI Training.
b)
Develop a regression model using BigQuery ML.
c)
Develop a custom scikit-learn regression model, and optimize it using Vertex AI Training.
d)
Develop a custom PyTorch regression model, and optimize it using Vertex AI Training.
17.
Your organization manages an online message board. A few months ago, you discovered an increase in toxic language and bullying on the message board. You deployed an automated text classifier that flags certain comments as toxic or harmful. Now some users are reporting that benign comments referencing their religion are being misclassified as abusive. Upon further inspection, you find that your classifier's false positive rate is higher for comments that reference certain underrepresented religious groups. Your team has a limited budget and is already overextended. What should you do?
a)
Add synthetic training data where those phrases are used in non-toxic ways.
b)
Remove the model and replace it with human moderation.
c)
Replace your model with a different text classifier.
d)
Raise the threshold for comments to be considered toxic or harmful.
18.
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 model's 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?
a)
Classification
b)
Reinforcement Learning
c)
Recurrent Neural Networks (RNN)
d)
Convolutional Neural Networks (CNN)
19.
You work for a magazine distributor and need to build a model that predicts which customers will renew their subscriptions for the upcoming year. Using your company’s historical data as your training set, you created a TensorFlow model and deployed it to Vertex AI. You need to determine which customer attribute has the most predictive power for each prediction served by the model. What should you do?
a)
Stream prediction results to BigQuery. Use BigQuery’s CORR(X1, X2) function to calculate the Pearson correlation coefficient between each feature and the target variable.
b)
Use Vertex Explainable AI. Submit each prediction request with the explain' keyword to retrieve feature attributions using the sampled Shapley method.
c)
Use Vertex AI Workbench user-managed notebooks to perform a Lasso regression analysis on your model, which will eliminate features that do not provide a strong signal.
d)
Use the What-If tool in Google Cloud to determine how your model will perform when individual features are excluded. Rank the feature importance in order of those that caused the most significant performance drop when removed from the model.
20.
You are an ML engineer at a manufacturing company. You are creating a classification model for a predictive maintenance use case. You need to predict whether a crucial machine will fail in the next three days so that the repair crew has enough time to fix the machine before it breaks. Regular maintenance of the machine is relatively inexpensive, but a failure would be very costly. You have trained several binary classifiers to predict whether the machine will fail, where a prediction of 1 means that the ML model predicts a failure.
You are now evaluating each model on an evaluation dataset. You want to choose a model that prioritizes detection while ensuring that more than 50% of the maintenance jobs triggered by your model address an imminent machine failure. Which model should you choose?
a)
The model with the highest area under the receiver operating characteristic curve (AUC ROC) and precision greater than 0.5
b)
The model with the lowest root mean squared error (RMSE) and recall greater than 0.5.
c)
The model with the highest recall where precision is greater than 0.5.
d)
The model with the highest precision where recall is greater than 0.5.
21.
You built a custom ML model using scikit-learn. Training time is taking longer than expected. You decide to migrate your model to Vertex AI Training, and you want to improve the model’s training time. What should you try out first?
a)
Train your model in a distributed mode using multiple Compute Engine VMs.
b)
Train your model using Vertex AI Training with CPUs.
c)
Migrate your model to TensorFlow, and train it using Vertex AI Training.
d)
Train your model using Vertex AI Training with GPUs.
22.
You are an ML engineer at a retail company. You have built a model that predicts a coupon to offer an ecommerce customer at checkout based on the items in their cart. When a customer goes to checkout, your serving pipeline, which is hosted on Google Cloud, joins the customer's existing cart with a row in a BigQuery table that contains the customers' historic purchase behavior and uses that as the model's input. The web team is reporting that your model is returning predictions too slowly to load the coupon offer with the rest of the web page. How should you speed up your model's predictions?
a)
Attach an NVIDIA P100 GPU to your deployed model’s instance.
b)
Use a low latency database for the customers’ historic purchase behavior.
c)
Deploy your model to more instances behind a load balancer to distribute traffic.
d)
Create a materialized view in BigQuery with the necessary data for predictions.
23.
You work for a small company that has deployed an ML model with autoscaling on Vertex AI to serve online predictions in a production environment. The current model receives about 20 prediction requests per hour with an average response time of one second. You have retrained the same model on a new batch of data, and now you are canary testing it, sending ~10% of production traffic to the new model. During this canary test, you notice that prediction requests for your new model are taking between 30 and 180 seconds to complete. What should you do?
a)
Submit a request to raise your project quota to ensure that multiple prediction services can run concurrently.
b)
Turn off auto-scaling for the online prediction service of your new model. Use manual scaling with one node always available.
c)
Remove your new model from the production environment. Compare the new model and existing model codes to identify the cause of the performance bottleneck.
d)
Remove your new model from the production environment. For a short trial period, send all incoming prediction requests to BigQuery. Request batch predictions from your new model, and then use the Data Labeling Service to validate your model’s performance before promoting it to production.
24.
You want to train an AutoML model to predict house prices by using a small public dataset stored in BigQuery. You need to prepare the data and want to use the simplest, most efficient approach. What should you do?
a)
Write a query that preprocesses the data by using BigQuery and creates a new table. Create a Vertex AI managed dataset with the new table as the data source.
b)
Use Dataflow to preprocess the data. Write the output in TFRecord format to a Cloud Storage bucket.
c)
Write a query that preprocesses the data by using BigQuery. Export the query results as CSV files, and use those files to create a Vertex AI managed dataset.
d)
Use a Vertex AI Workbench notebook instance to preprocess the data by using the pandas library. Export the data as CSV files, and use those files to create a Vertex AI managed dataset.
25.
You developed a Vertex AI ML pipeline that consists of preprocessing and training steps and each set of steps runs on a separate custom Docker image. Your organization uses GitHub and GitHub Actions as CI/CD to run unit and integration tests. You need to automate the model retraining workflow so that it can be initiated both manually and when a new version of the code is merged in the main branch. You want to minimize the steps required to build the workflow while also allowing for maximum flexibility. How should you configure the CI/CD workflow?
a)
Trigger a Cloud Build workflow to run tests, build custom Docker images, push the images to Artifact Registry, and launch the pipeline in Vertex AI Pipelines.
b)
Trigger GitHub Actions to run the tests, launch a job on Cloud Run to build custom Docker images, push the images to Artifact Registry, and launch the pipeline in Vertex AI Pipelines.
c)
Trigger GitHub Actions to run the tests, build custom Docker images, push the images to Artifact Registry, and launch the pipeline in Vertex AI Pipelines.
d)
Trigger GitHub Actions to run the tests, launch a Cloud Build workflow to build custom Docker images, push the images to Artifact Registry, and launch the pipeline in Vertex AI Pipelines.
26.
You are working with a dataset that contains customer transactions. You need to build an ML model to predict customer purchase behavior. You plan to develop the model in BigQuery ML, and export it to Cloud Storage for online prediction. You notice that the input data contains a few categorical features, including product category and payment method. You want to deploy the model as quickly as possible. What should you do?
a)
Use the TRANSFORM clause with the ML.ONE_HOT_ENCODER function on the categorical features at model creation and select the categorical and non-categorical features.
b)
Use the ML.ONE_HOT_ENCODER function on the categorical features and select the encoded categorical features and non-categorical features as inputs to create your model.
c)
Use the CREATE MODEL statement and select the categorical and non-categorical features.
d)
Use the ML.MULTI_HOT_ENCODER function on the categorical features, and select the encoded categorical features and non-categorical features as inputs to create your model.
27.
You need to develop an image classification model by using a large dataset that contains labeled images in a Cloud Storage bucket. What should you do?
a)
Use Vertex AI Pipelines with the Kubeflow Pipelines SDK to create a pipeline that reads the images from Cloud Storage and trains the model.
b)
Use Vertex AI Pipelines with TensorFlow Extended (TFX) to create a pipeline that reads the images from Cloud Storage and trains the model.
c)
Import the labeled images as a managed dataset in Vertex AI and use AutoML to train the model.
d)
Convert the image dataset to a tabular format using Dataflow Load the data into BigQuery and use BigQuery ML to train the model.
28.
You are developing a model to detect fraudulent credit card transactions. You need to prioritize detection, because missing even one fraudulent transaction could severely impact the credit card holder. You used AutoML to tram a model on users' profile information and credit card transaction data After training the initial model, you notice that the model is failing to detect many fraudulent transactions. How should you adjust the training parameters in AutoML to improve model performance? (Choose two.)
a)
Increase the score threshold
b)
Decrease the score threshold.
c)
Add more positive examples to the training set
d)
Add more negative examples to the training set
e)
Reduce the maximum number of node hours for training
29.
You are building a real-time prediction engine that streams files which may contain Personally Identifiable Information (PII) to Google Cloud. You want to use the
Cloud Data Loss Prevention (DLP) API to scan the files. How should you ensure that the PII is not accessible by unauthorized individuals?
a)
Stream all files to Google Cloud, and then write the data to BigQuery. Periodically conduct a bulk scan of the table using the DLP API.
b)
Stream all files to Google Cloud, and write batches of the data to BigQuery. While the data is being written to BigQuery, conduct a bulk scan of the data using the DLP API.
c)
Create two buckets of data: Sensitive and Non-sensitive. Write all data to the Non-sensitive bucket. Periodically conduct a bulk scan of that bucket using the DLP API, and move the sensitive data to the Sensitive bucket.
d)
Create three buckets of data: Quarantine, Sensitive, and Non-sensitive. Write all data to the Quarantine bucket. Periodically conduct a bulk scan of that bucket using the DLP API, and move the data to either the Sensitive or Non-Sensitive bucket.
30.
You need to deploy a scikit-leam classification model to production. The model must be able to serve requests 24/7, and you expect millions of requests per second to the production application from 8 am to 7 pm. You need to minimize the cost of deployment. What should you do?
a)
Deploy an online Vertex AI prediction endpoint. Set the max replica count to 1
b)
Deploy an online Vertex AI prediction endpoint. Set the max replica count to 100
c)
Deploy an online Vertex AI prediction endpoint with one GPU per replica. Set the max replica count to 1
d)
Deploy an online Vertex AI prediction endpoint with one GPU per replica. Set the max replica count to 100
31.
You work with a team of researchers to develop state-of-the-art algorithms for financial analysis. Your team develops and debugs complex models in TensorFlow. You want to maintain the ease of debugging while also reducing the model training time. How should you set up your training environment?
a)
Configure a v3-8 TPU VM. SSH into the VM to train and debug the model.
b)
Configure a v3-8 TPU node. Use Cloud Shell to SSH into the Host VM to train and debug the model.
c)
Configure a n1 -standard-4 VM with 4 NVIDIA P100 GPUs. SSH into the VM and use ParameterServerStraregv to train the model.
d)
Configure a n1-standard-4 VM with 4 NVIDIA P100 GPUs. SSH into the VM and use MultiWorkerMirroredStrategy to train the model.
32.
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 rate
You 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?
a)
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 learning rates. Choose the grid search option.
b)
1. Create a Vertex AI 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.
c)
1. Create a Vertex AI 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.
d)
1. Create an experiment in Vertex AI Experiments. 2. Create a Vertex AI pipeline with a custom model training job as part of the pipeline. Configure the pipeline’s parameters to include those you are investigating. 3. Submit multiple runs to the same experiment, using different values for the parameters.
33.
You received a training-serving skew alert from a Vertex AI Model Monitoring job running in production. You retrained the model with more recent training data, and deployed it back to the Vertex AI endpoint, but you are still receiving the same alert. What should you do?
a)
Update the model monitoring job to use a lower sampling rate.
b)
Update the model monitoring job to use the more recent training data that was used to retrain the model.
c)
Temporarily disable the alert. Enable the alert again after a sufficient amount of new production traffic has passed through the Vertex AI endpoint.
d)
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 AI endpoint.
34.
You developed a custom model by using Vertex AI 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 AI Model Monitoring for drift detection and you want to minimize the cost. What should you do?
a)
Use the features for monitoring. Set a monitoring-frequency value that is higher than the default.
b)
Use the features for monitoring. Set a prediction-sampling-rate value that is closer to 1 than 0.
c)
Use the features and the feature attributions for monitoring. Set a monitoring-frequency value that is lower than the default.
d)
Use the features and the feature attributions for monitoring. Set a prediction-sampling-rate value that is closer to 0 than 1.
35.
You have recently trained a scikit-learn model that you plan to deploy on Vertex AI. 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?
a)
1. Upload your model to the Vertex AI Model Registry by using a prebuilt scikit-ieam prediction container. 2. Deploy your model to Vertex AI Endpoints, and create a Vertex AI batch prediction job that uses the instanceConfig.instanceType setting to transform your input data.
b)
1. Wrap your model in a custom prediction routine (CPR). and build a container image from the CPR local model. 2. Upload your scikit learn model container to Vertex AI Model Registry. 3. Deploy your model to Vertex AI Endpoints, and create a Vertex AI batch prediction job
c)
1. Create a custom container for your scikit learn model. 2. Define a custom serving function for your model. 3. Upload your model and custom container to Vertex AI Model Registry. 4. Deploy your model to Vertex AI Endpoints, and create a Vertex AI batch prediction job.
d)
1. Create a custom container for your scikit learn model. 2. Upload your model and custom container to Vertex AI Model Registry. 3. Deploy your model to Vertex AI Endpoints, and create a Vertex AI batch prediction job that uses the instanceConfig.instanceType setting to transform your input data.
36.
You work for a food product company. Your company’s historical sales data is stored in BigQuery.You need to use Vertex AI’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 mm-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?
a)
Write the transformations into Spark that uses the spark-bigquery-connector, and use Dataproc to preprocess the data.
b)
Write SQL queries to transform the data in-place in BigQuery.
c)
Add the transformations as a preprocessing layer in the TensorFlow models.
d)
Create a Dataflow pipeline that uses the BigQuerylO connector to ingest the data, process it, and write it back to BigQuery.
37.
You have created a Vertex AI 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?
a)
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.
b)
Create another pipeline without the preprocessing step, and hardcode the preprocessed Cloud Storage file location for model training.
c)
Configure a machine with more CPU and RAM from the compute-optimized machine family for the data preprocessing step.
d)
Enable caching for the pipeline job, and disable caching for the model training step.
38.
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?
a)
Create an AutoML tabular model by using the BigQuery data with integrated Vertex Explainable AI.
b)
Create a BigQuery ML deep neural network model and use the ML.EXPLAIN_PREDICT method with the num_integral_steps parameter.
c)
Upload the custom model to Vertex AI Model Registry and configure feature-based attribution by using sampled Shapley with input baselines.
d)
Update the custom serving container to include sampled Shapley-based explanations in the prediction outputs.
39.
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 Kubernetes 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?
a)
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.
b)
Use FastAPI to implement an HTTP server. Create a Docker image that runs your HTTP server, Upload the image to Vertex AI Model Registry and deploy it to a Vertex AI endpoint.
c)
Use the Predictor interface to implement a custom prediction routine. Build the custom container, upload the container to Vertex AI Model Registry and deploy it to a Vertex AI endpoint.
d)
Use the XGBoost prebuilt serving container when importing the trained model into Vertex AI. Deploy the model to a Vertex AI endpoint. Work with the backend engineers to implement the pre- and postprocessing steps in the Golang backend service.
40.
You work for a large hotel chain and have been asked to assist the marketing team in gathering predictions for a targeted marketing strategy. You need to make predictions about user lifetime value (LTV) over the next 20 days so that marketing can be adjusted accordingly. The customer dataset is in BigQuery, and you are preparing the tabular data for training with AutoML Tables. This data has a time signal that is spread across multiple columns. How should you ensure that
AutoML fits the best model to your data?
a)
Manually combine all columns that contain a time signal into an array. AIlow AutoML to interpret this array appropriately. Choose an automatic data split across the training, validation, and testing sets.
b)
Submit the data for training without performing any manual transformations. AIlow AutoML to handle the appropriate transformations. Choose an automatic data split across the training, validation, and testing sets.
c)
Submit the data for training without performing any manual transformations, and indicate an appropriate column as the Time column. AIlow AutoML to split your data based on the time signal provided, and reserve the more recent data for the validation and testing sets.
d)
Submit the data for training without performing any manual transformations. Use the columns that have a time signal to manually split your data. Ensure that the data in your validation set is from 30 days after the data in your training set and that the data in your testing sets from 30 days after your validation set.
41.
You recently deployed a pipeline in Vertex AI Pipelines that trains and pushes a model to a Vertex AI 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?
a)
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 AI Pipelines.
b)
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.
c)
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.
d)
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, rebuild the source code and deploy the artifacts to production.
42.
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 project’s network. You need the data to be accessed in the most secure way, while mitigating the risk of data exfiltration. What should you do?
a)
Enable VPC Service Controls for peerings, and add Vertex AI to a service perimeter.
b)
Create a Cloud Run endpoint as a proxy to the data. Use Identity and Access Management (IAM) authentication to secure access to the endpoint from the training job.
c)
Configure VPC Peering with Vertex AI, and specify the network of the training job.
d)
Download the data to a Cloud Storage bucket before calling the training job.
43.
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?
a)
1. Create a new endpoint 2. Create a new model. Set it as the default version. Upload the model to Vertex AI Model Registry 3. Deploy the new model to the new endpoint 4. Update Cloud DNS to point to the new endpoint
b)
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 AI Model Registry 3. Deploy the new model to the new endpoint, and set the new model to 100% of the traffic.
c)
1. Create a new model. Set the parentModel parameter to the model ID of the currently deployed model. Upload the model to Vertex AI Model Registry. 2. Deploy the new model to the existing endpoint, and set the new model to 100% of the traffic
d)
1. Create a new model. Set it as the default version. Upload the model to Vertex AI Model Registry 2. Deploy the new model to the existing endpoint
44.
You are training an ML model on a large dataset. You are using a TPU to accelerate the training process. You notice that the training process is taking longer than expected. You discover that the TPU is not reaching its full capacity. What should you do?
a)
Increase the learning rate
b)
Increase the number of epochs
c)
Decrease the learning rate
d)
Increase the batch size.
45.
You work for a retail company. You have a managed tabular dataset in Vertex AI that contains sales data from three different stores. The dataset includes several features, such as store name and sale timestamp. You want to use the data to train a model that makes sales predictions for a new store that will open soon. You need to split the data between the training, validation, and test sets. What approach should you use to split the data?
a)
Use Vertex AI manual split, using the store name feature to assign one store for each set
b)
Use Vertex AI default data split
c)
Use Vertex AI chronological split, and specify the sales timestamp feature as the time variable
d)
Use Vertex AI random split, assigning 70% of the rows to the training set, 10% to the validation set, and 20% to the test set
46.
You have developed a BigQuery ML model that predicts customer chum, and deployed the model to Vertex AI Endpoints. You want to automate the retraining of your model by using minimal additional code when model feature values change. You also want to minimize the number of times that your model is retrained to reduce training costs. What should you do?
a)
1 Enable request-response logging on Vertex AI Endpoints 2. Schedule a TensorFlow Data Validation job to monitor prediction drift 3. Execute model retraining if there is significant distance between the distributions
b)
1. Enable request-response logging on Vertex AI Endpoints 2. Schedule a TensorFlow Data Validation job to monitor training/serving skew 3. Execute model retraining if there is significant distance between the distributions
c)
1. Create a Vertex AI Model Monitoring job configured to monitor prediction drift 2. Configure alert monitoring to publish a message to a Pub/Sub queue when a monitoring alert is detected 3. Use a Cloud Function to monitor the Pub/Sub queue, and trigger retraining in BigQuery
d)
1. Create a Vertex AI Model Monitoring job configured to monitor training/serving skew 2. Configure alert monitoring to publish a message to a Pub/Sub queue when a monitoring alert is detected 3. Use a Cloud Function to monitor the Pub/Sub queue, and trigger retraining in BigQuery
47.
You have been tasked with deploying prototype code to production. The feature engineering code is in PySpark and runs on Dataproc Serverless. The model training is executed by using a Vertex AI custom training job. The two steps are not connected, and the model training must currently be run manually after the feature engineering step finishes. You need to create a scalable and maintainable production process that runs end-to-end and tracks the connections between steps. What should you do?
a)
Create a Vertex AI Workbench notebook. Use the notebook to submit the Dataproc Serverless feature engineering job. Use the same notebook to submit the custom model training job. Run the notebook cells sequentially to tie the steps together end-to-end.
b)
Create a Vertex AI Workbench notebook. Initiate an Apache Spark context in the notebook and run the PySpark feature engineering code. Use the same notebook to run the custom model training job in TensorFlow. Run the notebook cells sequentially to tie the steps together end-to-end.
c)
Use the Kubeflow pipelines SDK to write code that specifies two components: - The first is a Dataproc Serverless component that launches the feature engineering job - The second is a custom component wrapped in the create_custom_training_job_from_component utility that launches the custom model training job Create a Vertex AI Pipelines job to link and run both components
d)
Use the Kubeflow pipelines SDK to write code that specifies two components - The first component initiates an Apache Spark context that runs the PySpark feature engineering code - The second component runs the TensorFlow custom model training code Create a Vertex AI Pipelines job to link and run both components.
48.
You recently deployed a scikit-learn model to a Vertex AI endpoint. You are now testing the model on live production traffic. While monitoring the endpoint, you discover twice as many requests per hour than expected throughout the day. You want the endpoint to efficiently scale when the demand increases in the future to prevent users from experiencing high latency. What should you do?
a)
Deploy two models to the same endpoint, and distribute requests among them evenly
b)
Configure an appropriate minReplicaCount value based on expected baseline traffic
c)
Set the target utilization percentage in the autoscailngMetricSpecs configuration to a higher value
d)
Change the model’s machine type to one that utilizes GPUs
49.
You work at a bank. You have a custom tabular ML model that was provided by the bank’s vendor. The training data is not available due to its sensitivity. The model is packaged as a Vertex AI Model serving container, which accepts a string as input for each prediction instance. In each string, the feature values are separated by commas. You want to deploy this model to production for online predictions and monitor the feature distribution over time with minimal effort. What should you do?
a)
1. Upload the model to Vertex AI Model Registry, and deploy the model to a Vertex AI endpoint 2. Create a Vertex AI Model Monitoring job with feature drift detection as the monitoring objective, and provide an instance schema
b)
1. Upload the model to Vertex AI Model Registry, and deploy the model to a Vertex AI endpoint 2. Create a Vertex AI Model Monitoring job with feature skew detection as the monitoring objective, and provide an instance schema
c)
1. Refactor the serving container to accept key-value pairs as input format 2. Upload the model to Vertex AI Model Registry, and deploy the model to a Vertex AI endpoint 3. Create a Vertex AI Model Monitoring job with feature drift detection as the monitoring objective.
d)
1. Refactor the serving container to accept key-value pairs as input format 2. Upload the model to Vertex AI Model Registry, and deploy the model to a Vertex AI endpoint 3. Create a Vertex AI Model Monitoring job with feature skew detection as the monitoring objective
50.
You are implementing a batch inference ML pipeline in Google Cloud. The model was developed using TensorFlow and is stored in SavedModel format in Cloud Storage. You need to apply the model to a historical dataset containing 10 TB of data that is stored in a BigQuery table. How should you perform the inference?
a)
Export the historical data to Cloud Storage in Avro format. Configure a Vertex AI batch prediction job to generate predictions for the exported data
b)
Import the TensorFlow model by using the CREATE MODEL statement in BigQuery ML. Apply the historical data to the TensorFlow model
c)
Export the historical data to Cloud Storage in CSV format. Configure a Vertex AI batch prediction job to generate predictions for the exported data
d)
Configure a Vertex AI batch prediction job to apply the model to the historical data in BigQuery
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