Professional Machine Learning Engineer Free Practice Questions — Page 17
Question 162
You have created a Vertex AI pipeline that automates custom model training. You want to add a pipeline component that enables your team to most easily collaborate when running different executions and comparing metrics both visually and programmatically. What should you do?
A. Add a component to the Vertex AI pipeline that logs metrics to a BigQuery table. Query the table to compare different executions of the pipeline. Connect BigQuery to Looker Studio to visualize metrics.
B. Add a component to the Vertex AI pipeline that logs metrics to a BigQuery table. Load the table into a pandas DataFrame to compare different executions of the pipeline. Use Matplotlib to visualize metrics.
C. Add a component to the Vertex AI pipeline that logs metrics to Vertex ML Metadata. Use Vertex AI Experiments to compare different executions of the pipeline. Use Vertex AI TensorBoard to visualize metrics.
D. Add a component to the Vertex AI pipeline that logs metrics to Vertex ML Metadata. Load the Vertex ML Metadata into a pandas DataFrame to compare different executions of the pipeline. Use Matplotlib to visualize metrics.
Show Answer
Correct Answer: C
Explanation: Vertex AI Experiments is purpose-built for comparing pipeline runs and their metrics across executions, backed by Vertex ML Metadata. It supports both programmatic access via SDKs/APIs and visual comparison in the console. TensorBoard integrates naturally to visualize training and evaluation metrics. This combination is the most native, collaborative, and low-effort approach on Vertex AI for tracking, comparing, and visualizing metrics across pipeline runs, without building and maintaining custom logging or dashboards.
Question 163
You work for a delivery company. You need to design a system that stores and manages features such as parcels delivered and truck locations over time. The system must retrieve the features with low latency and feed those features into a model for online prediction. The data science team will retrieve historical data at a specific point in time for model training. You want to store the features with minimal effort. What should you do?
A. Store features in Bigtable as key/value data.
B. Store features in Vertex AI Feature Store.
C. Store features as a Vertex AI dataset, and use those features to train the models hosted in Vertex AI endpoints.
D. Store features in BigQuery timestamp partitioned tables, and use the BigQuery Storage Read API to serve the features.
Show Answer
Correct Answer: B
Explanation: The requirements include low-latency online feature retrieval, point-in-time access to historical features for training, and minimal operational effort. Vertex AI Feature Store is purpose-built for this: it supports online serving with low latency, offline historical retrieval with point-in-time correctness for training, and integrates directly with Vertex AI workflows, reducing engineering overhead. Other options require more custom work or do not natively support both online and offline feature serving with time travel.
Question 164
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.
Show Answer
Correct Answer: B
Explanation: Vertex AI training-serving skew is measured by comparing live prediction data against the baseline training dataset configured in the Model Monitoring job. Retraining and redeploying the model alone does not update this baseline. If the monitoring job still references the old training data, skew alerts will persist even if the model was retrained. Updating the monitoring job to use the new training dataset recalculates the baseline and aligns skew detection with the retrained model.
Question 165
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.
Show Answer
Correct Answer: D
Explanation: You need a reproducible way to run the same pipeline with different parameter combinations and systematically compare metrics like F1 score, training time, and model complexity. Vertex AI Pipelines ensures the workflow is consistent and reproducible, while Vertex AI Experiments is specifically designed to track, group, and compare multiple runs with different parameters on the same platform. Submitting multiple pipeline runs to the same experiment enables direct comparison of metrics across configurations.
Question 166
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.
Show Answer
Correct Answer: A
Explanation: The goal is to reduce training time while maintaining ease of debugging in TensorFlow. TPU VMs provide direct SSH access and support standard TensorFlow debugging workflows (eager execution, familiar tools), unlike TPU nodes which add indirection via a host VM. TPUs also offer significantly faster training for state-of-the-art models compared to GPUs. Option D is flawed because MultiWorkerMirroredStrategy is intended for multiple VMs, not a single VM with multiple GPUs (which would use MirroredStrategy). Therefore, configuring a v3-8 TPU VM is the best choice.
Question 167
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
Show Answer
Correct Answer: B
Explanation: The workload requires very high, sustained online throughput (millions of requests per second) during long peak hours while minimizing cost. An online Vertex AI endpoint with autoscaling and a high max replica count allows the service to scale out during peak demand and scale in during off-peak hours, optimizing cost. scikit-learn models do not benefit from GPUs, so GPU-based options add unnecessary cost. A max replica count of 1 cannot handle the required traffic. Therefore, deploying an online endpoint with a high max replica count is the correct choice.
Question 168
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
Show Answer
Correct Answer: B, C
Explanation: The priority is to minimize false negatives (missed fraud). Decreasing the score threshold makes the model more sensitive, flagging more transactions as fraudulent. Adding more positive examples (fraudulent transactions) helps the model better learn fraud patterns, which is especially important because fraud is typically a rare class.
Question 169
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.
Show Answer
Correct Answer: C
Explanation: The requirement is simply to train an image classification model from a large set of labeled images stored in Cloud Storage. Vertex AI AutoML for image classification is specifically designed for this use case: you import images from a Cloud Storage bucket as a managed dataset and train a model with minimal setup. Using Vertex AI Pipelines with Kubeflow or TFX is unnecessary unless you need a custom, end-to-end ML pipeline or complex preprocessing. BigQuery ML is not suitable for image data.
Question 170
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.
Show Answer
Correct Answer: C
Explanation: BigQuery ML automatically handles categorical features through built-in auto-preprocessing (such as one-hot encoding for most models). To deploy as quickly as possible, you should simply use CREATE MODEL and select both categorical and non-categorical features, avoiding manual encoding with TRANSFORM or encoder functions, which adds complexity and development time.
Question 171
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.
Show Answer
Correct Answer: D
Explanation: Use GitHub Actions for what it does best: triggering on merges or manual dispatch and running unit/integration tests. Delegate Docker image builds and pushes to Cloud Build, which is a managed, scalable service optimized for container builds and integrates natively with Artifact Registry and GCP IAM. From there, launch the Vertex AI Pipeline. This minimizes custom build logic in GitHub Actions while maximizing flexibility, scalability, and alignment with GCP best practices.
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