Professional Machine Learning Engineer Free Practice Questions — Page 9
Question 82
You recently deployed a model to a Vertex AI endpoint and set up online serving in Vertex AI 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 featurestore’s 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?
A. Schedule an increase in the number of online serving nodes in your featurestore prior to the batch ingestion jobs
B. Enable autoscaling of the online serving nodes in your featurestore
C. Enable autoscaling for the prediction nodes of your DeployedModel in the Vertex AI endpoint
D. Increase the worker_count in the ImportFeatureValues request of your batch ingestion job
Show Answer
Correct Answer: A
Explanation: The latency spike occurs during a known, predictable daily batch ingestion window that increases load on Feature Store online serving nodes. Autoscaling is reactive and may not scale fast enough to handle sudden bursts, causing high CPU and latency before new nodes are ready. Pre-scheduling an increase (or raising min node count ahead of time) ensures sufficient capacity during ingestion. Autoscaling prediction nodes doesn’t affect feature retrieval, and increasing ingestion workers can worsen contention.
Question 83
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?
A. Create a Vertex AI Workbench user-managed notebook using the default VM instance, and use the %%bigquerv magic commands in Jupyter to query the tables.
B. Create a Vertex AI Workbench managed notebook to browse and query the tables directly from the JupyterLab interface.
C. Create a Vertex AI Workbench user-managed notebook on a Dataproc Hub, and use the %%bigquery magic commands in Jupyter to query the tables.
D. Create a Vertex AI Workbench managed notebook on a Dataproc cluster, and use the spark-bigquery-connector to access the tables.
Show Answer
Correct Answer: B
Explanation: Vertex AI Workbench managed notebooks are optimized for iterative data science workflows like EDA, preprocessing, and model training. They integrate natively with BigQuery and Cloud Storage through the JupyterLab interface, reducing setup and maintenance overhead. Because Google manages the environment, you minimize development effort, and you avoid the additional cost and complexity of managing VMs or Dataproc/Spark infrastructure, which would be unnecessary for repeated experimentation.
Question 84
You have developed an application that uses a chain of multiple scikit-learn models to predict the optimal price for your company’s products. The workflow logic is shown in the diagram. Members of your team use the individual models in other solution workflows. You want to deploy this workflow while ensuring version control for each individual model and the overall workflow. Your application needs to be able to scale down to zero. You want to minimize the compute resource utilization and the manual effort required to manage this solution. What should you do?
A. Expose each individual model as an endpoint in Vertex AI Endpoints. Create a custom container endpoint to orchestrate the workflow.
B. Create a custom container endpoint for the workflow that loads each model’s individual files Track the versions of each individual model in BigQuery.
C. Expose each individual model as an endpoint in Vertex AI Endpoints. Use Cloud Run to orchestrate the workflow.
D. Load each model’s individual files into Cloud Run. Use Cloud Run to orchestrate the workflow. Track the versions of each individual model in BigQuery.
Show Answer
Correct Answer: C
Explanation: Option C best meets all requirements. Exposing each individual scikit-learn model as a Vertex AI Endpoint provides built-in model versioning, reuse across teams, and independent lifecycle management. Using Cloud Run to orchestrate the workflow allows the application to scale down to zero, minimizing compute cost, and reduces operational overhead compared to custom endpoint orchestration. This combination cleanly separates model serving from workflow logic while ensuring version control at both the model and workflow levels.
Question 85
You work for an international manufacturing organization that ships scientific products all over the world. Instruction manuals for these products need to be translated to 15 different languages. Your organization’s leadership team wants to start using machine learning to reduce the cost of manual human translations and increase translation speed. You need to implement a scalable solution that maximizes accuracy and minimizes operational overhead. You also want to include a process to evaluate and fix incorrect translations. What should you do?
A. Create a workflow using Cloud Function triggers. Configure a Cloud Function that is triggered when documents are uploaded to an input Cloud Storage bucket. Configure another Cloud Function that translates the documents using the Cloud Translation API, and saves the translations to an output Cloud Storage bucket. Use human reviewers to evaluate the incorrect translations.
B. Create a Vertex AI pipeline that processes the documents launches, an AutoML Translation training job, evaluates the translations and deploys the model to a Vertex AI endpoint with autoscaling and model monitoring. When there is a predetermined skew between training and live data, re-trigger the pipeline with the latest data.
C. Use AutoML Translation to train a model. Configure a Translation Hub project, and use the trained model to translate the documents. Use human reviewers to evaluate the incorrect translations.
D. Use Vertex AI custom training jobs to fine-tune a state-of-the-art open source pretrained model with your data. Deploy the model to a Vertex AI endpoint with autoscaling and model monitoring. When there is a predetermined skew between the training and live data, configure a trigger to run another training job with the latest data.
Show Answer
Correct Answer: C
Explanation: The requirement is a scalable, managed translation solution that maximizes accuracy while minimizing operational overhead, and includes a way to review and correct errors. AutoML Translation is designed for domain‑specific translations (such as scientific manuals) and can significantly improve accuracy over generic translation models. Translation Hub provides a managed, scalable workflow for document translation across many languages and supports human‑in‑the‑loop review to evaluate and fix incorrect translations. This combination avoids the complexity of custom pipelines or custom model training while still using machine learning tailored to the domain.
Question 86
You recently trained an XGBoost model on tabular data. You plan to expose the model for internal use as an HTTP microservice. After deployment, you expect a small number of incoming requests. You want to productionize the model with the least amount of effort and latency. What should you do?
A. Deploy the model to BigQuery ML by using CREATE MODEL with the BOOSTED_TREE_REGRESSOR statement, and invoke the BigQuery API from the microservice.
B. Build a Flask-based app. Package the app in a custom container on Vertex AI, and deploy it to Vertex AI Endpoints.
C. Build a Flask-based app. Package the app in a Docker image, and deploy it to Google Kubernetes Engine in Autopilot mode.
D. Use a prebuilt XGBoost Vertex container to create a model, and deploy it to Vertex AI Endpoints.
Show Answer
Correct Answer: D
Explanation: Using the prebuilt XGBoost Vertex AI container requires the least effort because you avoid building and maintaining a custom serving application or container. Vertex AI Endpoints provide a fully managed, low-latency HTTP serving layer suitable for a small request volume, making this the fastest path to production with minimal operational overhead.
Question 87
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?
A. Import the model into Vertex AI and deploy it to a Vertex AI endpoint. On Vertex AI Pipelines, create a pipeline that uses the DataflowPythonJobOp and the ModelBacthPredictOp components.
B. Import the model into Vertex AI and deploy it to a Vertex AI endpoint. Create a Dataflow pipeline that reuses the data processing logic sends requests to the endpoint, and then uploads predictions to a BigQuery table.
C. Import the model into Vertex AI. On Vertex AI Pipelines, create a pipeline that uses the DataflowPvthonJobOp and the ModelBatchPredictOp components.
D. Import the model into BigQuery. Implement the data processing logic in a SQL query. On Vertex AI Pipelines create a pipeline that uses the BigquervQueryJobOp and the BigqueryPredictModelJobOp components.
Show Answer
Correct Answer: C
Explanation: This use case requires large-scale batch prediction on a schedule, not online serving. Vertex AI batch prediction can directly read from and write to BigQuery without deploying an endpoint. The correct approach is to import the TensorFlow model into Vertex AI, use DataflowPythonJobOp in a Vertex AI Pipeline to run the existing Beam/Dataflow logic that produces TFRecords, and then use ModelBatchPredictOp to run batch inference and automatically write predictions to a BigQuery table. Deploying an endpoint (options A and B) is unnecessary and inefficient for batch jobs, and BigQuery ML components (option D) are not appropriate for a TensorFlow model trained outside BigQuery.
Question 88
You are working on a prototype of a text classification model in a managed Vertex AI Workbench notebook. You want to quickly experiment with tokenizing text by using a Natural Language Toolkit (NLTK) library. How should you add the library to your Jupyter kernel?
A. Install the NLTK library from a terminal by using the pip install nltk command.
B. Write a custom Dataflow job that uses NLTK to tokenize your text and saves the output to Cloud Storage.
C. Create a new Vertex AI Workbench notebook with a custom image that includes the NLTK library.
D. Install the NLTK library from a Jupyter cell by using the !pip install nltk --user command.
Show Answer
Correct Answer: D
Explanation: For quick experimentation in a managed Vertex AI Workbench notebook, the simplest and fastest approach is to install the library directly from a Jupyter cell. Using `!pip install nltk --user` makes NLTK immediately available to the active kernel without creating custom images, running external jobs, or switching to a terminal. This fits the goal of rapid prototyping.
Question 89
You work for a company that sells corporate electronic products to thousands of businesses worldwide. Your company stores historical customer data in BigQuery. You need to build a model that predicts customer lifetime value over the next three years. You want to use the simplest approach to build the model and you want to have access to visualization tools. What should you do?
A. Create a Vertex AI Workbench notebook to perform exploratory data analysis. Use IPython magics to create a new BigQuery table with input features. Use the BigQuery console to run the CREATE MODEL statement. Validate the results by using the ML.EVALUATE and ML.PREDICT statements.
B. Run the CREATE MODEL statement from the BigQuery console to create an AutoML model. Validate the results by using the ML.EVALUATE and ML.PREDICT statements.
C. Create a Vertex AI Workbench notebook to perform exploratory data analysis and create input features. Save the features as a CSV file in Cloud Storage. Import the CSV file as a new BigQuery table. Use the BigQuery console to run the CREATE MODEL statement. Validate the results by using the ML.EVALUATE and ML.PREDICT statements.
D. Create a Vertex AI Workbench notebook to perform exploratory data analysis. Use IPython magics to create a new BigQuery table with input features, create the model, and validate the results by using the CREATE MODEL, ML.EVALUATE, and ML.PREDICT statements.
Show Answer
Correct Answer: D
Explanation: The requirements are a simple modeling approach and access to visualization tools while using data already in BigQuery. Using Vertex AI Workbench provides rich visualization and exploratory data analysis capabilities. Running BigQuery ML SQL (CREATE MODEL, ML.EVALUATE, ML.PREDICT) directly from the notebook via IPython magics keeps the workflow simple and centralized without unnecessary data movement. This satisfies simplicity, leverages BigQuery ML, and provides visualization tools in one environment.
Question 90
You are developing a training pipeline for a new XGBoost classification model based on tabular data. The data is stored in a BigQuery table. You need to complete the following steps:
1. Randomly split the data into training and evaluation datasets in a 65/35 ratio
2. Conduct feature engineering
3. Obtain metrics for the evaluation dataset
4. Compare models trained in different pipeline executions
How should you execute these steps?
A. 1. Using Vertex AI Pipelines, add a component to divide the data into training and evaluation sets, and add another component for feature engineering. 2. Enable autologging of metrics in the training component. 3. Compare pipeline runs in Vertex AI Experiments.
B. 1. Using Vertex AI Pipelines, add a component to divide the data into training and evaluation sets, and add another component for feature engineering. 2. Enable autologging of metrics in the training component. 3. Compare models using the artifacts’ lineage in Vertex ML Metadata.
C. 1. In BigQuery ML, use the CREATE MODEL statement with BOOSTED_TREE_CLASSIFIER as the model type and use BigQuery to handle the data splits. 2. Use a SQL view to apply feature engineering and train the model using the data in that view. 3. Compare the evaluation metrics of the models by using a SQL query with the ML.TRAINING_INFO statement.
D. 1. In BigQuery ML, use the CREATE MODEL statement with BOOSTED_TREE_CLASSIFIER as the model type and use BigQuery to handle the data splits. 2. Use ML TRANSFORM to specify the feature engineering transformations and tram the model using the data in the table. 3. Compare the evaluation metrics of the models by using a SQL query with the ML.TRAINING_INFO statement.
Show Answer
Correct Answer: A
Explanation: The requirements call for an end-to-end training pipeline with data splitting, feature engineering, metric tracking, and comparison across different pipeline executions. Vertex AI Pipelines support modular components for splitting data and feature engineering. Enabling autologging in the training step captures evaluation metrics automatically. Vertex AI Experiments is specifically designed to compare metrics and parameters across multiple pipeline runs, making it the correct tool for model comparison. BigQuery ML options are less suitable for orchestrated pipelines and cross-run experiment comparison, and ML Metadata lineage is not intended for direct model performance comparison.
Question 91
You work at an ecommerce startup. You need to create a customer churn prediction model. Your company’s recent sales records are stored in a BigQuery table. You want to understand how your initial model is making predictions. You also want to iterate on the model as quickly as possible while minimizing cost. How should you build your first model?
A. Export the data to a Cloud Storage bucket. Load the data into a pandas DataFrame on Vertex AI Workbench and train a logistic regression model with scikit-learn.
B. Create a tf.data.Dataset by using the TensorFlow BigQueryClient. Implement a deep neural network in TensorFlow.
C. Prepare the data in BigQuery and associate the data with a Vertex AI dataset. Create an AutoMLTabularTrainingJob to tram a classification model.
D. Export the data to a Cloud Storage bucket. Create a tf.data.Dataset to read the data from Cloud Storage. Implement a deep neural network in TensorFlow.
Show Answer
Correct Answer: C
Explanation: The goal is to quickly build an initial churn model with minimal cost while still understanding predictions. AutoML Tabular on Vertex AI integrates directly with BigQuery, avoiding data export and reducing data movement costs. It enables rapid iteration through automated feature engineering and model selection. Importantly, Vertex AI AutoML Tabular supports built-in Explainable AI, providing feature attributions to understand how the model makes predictions without custom code. The other options require more manual development, higher complexity, or use less interpretable deep learning approaches, making them less suitable for a fast, cost-effective first model.
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