Free GCP-PMLE Sample Questions — Google Cloud Platform - Professional Machine Learning Engineer

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

• A. Use the Healthcare Natural Language API to extract medical entities
• B. Use a BERT-based model to fine-tune a medical entity extraction model
• C. Use AutoML Entity Extraction to train a medical entity extraction model
• D. Use TensorFlow to build a custom medical entity extraction model

You work for a large retailer, and you need to build a model to predict customer chum. The company has a dataset of historical customer data, including customer demographics purchase history, and website activity. You need to create the model in BigQuery ML and thoroughly evaluate its performance. What should you do?

• A. Create a linear regression model in BigQuery ML, and register the model in Vertex AI Model Registry. Use Vertex AI to evaluate the model performance
• B. Create a logistic regression model in BigQuery ML, and register the model in Vertex AI Model Registry. Use ML.ARIMA_EVALUATE function to evaluate the model performance
• C. Create a linear regression model in BigQuery ML. Use the ML.EVALUATE function to evaluate the model performance
• D. Create a logistic regression model in BigQuery ML. Use the ML.CONFUSION_MATRIX function to evaluate the model performance

You are developing an AI text generator that will be able to dynamically adapt its generated responses to mirror the writing style of the user and mimic famous authors if their style is detected. You have a large dataset of various authors' works, and you plan to host the model on a custom VM. You want to use the most effective model. What should you do?

• A. Deploy Llama 3 from Model Garden, and use prompt engineering techniques
• B. Fine-tune a BERT-based model from TensorFlow Hub
• C. Fine-tune Llama 3 from Model Garden on Vertex AI Pipelines
• D. Use the Gemini 1.5 Flash foundational model to build the text generator

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

• A. Use the Vision API to parse the text from each PDF file. Use the Natural Language API analyzeSentiment feature to infer overall satisfaction scores
• B. Use the Vision API to parse the text from each PDF file. Use the Natural Language API analyzeEntitySentiment feature to infer overall satisfaction scores
• C. Uptrain a Document AI custom extractor to parse the text in the comments section of each PDF file. Use the Natural Language API analyzeSentiment feature to infer overall satisfaction scores
• D. Uptrain a Document AI custom extractor to parse the text in the comments section of each PDF file. Use the Natural Language API analyzeEntitySentiment feature to infer overall satisfaction scores

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

You recently used BigQuery ML to train an AutoML regression model. You shared results with your team and received positive feedback. You need to deploy your model for online prediction as quickly as possible. What should you do?

• A. Retrain the model by using BigQuery ML, and specify Vertex AI as the model registry. Deploy the model from Vertex AI Model Registry to a Vertex AI endpoint,
• B. Retrain the model by using Vertex Al Deploy the model from Vertex AI Model. Registry to a Vertex AI endpoint
• C. Alter the model by using BigQuery ML, and specify Vertex AI as the model registry. Deploy the model from Vertex AI Model Registry to a Vertex AI endpoint
• D. Export the model from BigQuery ML to Cloud Storage. Import the model into Vertex AI Model Registry. Deploy the model to a Vertex AI endpoint

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 AI Platform's continuous evaluation service to ensure that the models have high accuracy on your test dataset. What should you do?

• A. Keep the original test dataset unchanged even if newer products are incorporated into retraining
• B. Extend your test dataset with images of the newer products when they are introduced to retraining
• C. Replace your test dataset with images of the newer products when they are introduced to retraining
• D. Update your test dataset with images of the newer products when your evaluation metrics drop below a pre-decided threshold

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

While performing exploratory data analysis on a dataset, you find that an important categorical feature has 5% null values. You want to minimize the bias that could result from the missing values. How should you handle the missing values?

• A. Remove the rows with missing values, and upsample your dataset by 5%
• B. Replace the missing values with the feature’s mean
• C. Replace the missing values with a placeholder category indicating a missing value
• D. Move the rows with missing values to your validation dataset

You need to train an XGBoost model on a small dataset. Your training code requires custom dependencies. You need to set up a Vertex AI custom training job. You want to minimize the startup time of the training job while following Google-recommended practices. What should you do?

• A. Create a custom container that includes the data and the custom dependencies. In your training application, load the data into a pandas DataFrame and train the model
• B. Store the data in a Cloud Storage bucket, and use the XGBoost prebuilt custom container to run your training application. Create a Python source distribution that installs the custom dependencies at runtime. In your training application, read the data from Cloud Storage and train the model
• C. Use the XGBoost prebuilt custom container. Create a Python source distribution that includes the data and installs the custom dependencies at runtime. In your training application, load the data into a pandas DataFrame and train the model
• D. Store the data in a Cloud Storage bucket, and create a custom container with your training application and its custom dependencies. In your training application, read the data from Cloud Storage and train the model

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

You need to train a regression model based on a dataset containing 50,000 records that is stored in BigQuery. The data includes a total of 20 categorical and numerical features with a target variable that can include negative values. You need to minimize effort and training time while maximizing model performance. What approach should you take to train this regression model?

• A. Create a custom TensorFlow DNN model
• B. Use BQML XGBoost regression to train the model
• C. Use AutoML Tables to train the model without early stopping
• D. Use AutoML Tables to train the model with RMSLE as the optimization objective

You are developing an ML model to identify your company’s products in images. You have access to over one million images in a Cloud Storage bucket. You plan to experiment with different TensorFlow models by using Vertex AI Training. You need to read images at scale during training while minimizing data I/O bottlenecks. What should you do?

• A. Load the images directly into the Vertex AI compute nodes by using Cloud Storage FUSE. Read the images by using the tf.data.Dataset.from_tensor_slices function
• B. Create a Vertex AI managed dataset from your image data. Access the AIP_TRAINING_DATA_URI environment variable to read the images by using the tf.data.Dataset.list_files function
• C. Convert the images to TFRecords and store them in a Cloud Storage bucket. Read the TFRecords by using the tf.data.TFRecordDataset function
• D. Store the URLs of the images in a CSV file. Read the file by using the tf.data.experimental.CsvDataset function

You are developing an ML model in a Vertex AI Workbench notebook. You want to track artifacts and compare models during experimentation using different approaches. You need to rapidly and easily transition successful experiments to production as you iterate on your model implementation. What should you do?

• A. 1. Initialize the Vertex SDK with the name of your experiment. Log parameters and metrics for each experiment, and attach dataset and model artifacts as inputs and outputs to each execution. 2. After a successful experiment create a Vertex AI pipeline
• B. 1. Initialize the Vertex SDK with the name of your experiment. Log parameters and metrics for each experiment, save your dataset to a Cloud Storage bucket, and upload the models to Vertex AI Model Registry. 2. After a successful experiment, create a Vertex AI pipeline
• C. 1. Create a Vertex AI pipeline with parameters you want to track as arguments to your PipelineJob. Use the Metrics, Model, and Dataset artifact types from the Kubeflow Pipelines DSL as the inputs and outputs of the components in your pipeline. 2. Associate the pipeline with your experiment when you submit the job
• D. 1. Create a Vertex AI pipeline. Use the Dataset and Model artifact types from the Kubeflow Pipelines DSL as the inputs and outputs of the components in your pipeline. 2. In your training component, use the Vertex AI SDK to create an experiment run. Configure the log_params and log_metrics functions to track parameters and metrics of your experiment

You work for an online travel agency that also sells advertising placements on its website to other companies. You have been asked to predict the most relevant web banner that a user should see next. Security is important to your company. The model latency requirements are 300ms@p99, the inventory is thousands of web banners, and your exploratory analysis has shown that navigation context is a good predictor. You want to Implement the simplest solution. How should you configure the prediction pipeline?

• A. Embed the client on the website, and then deploy the model on AI Platform Prediction
• B. Embed the client on the website, deploy the gateway on App Engine, deploy the database on Firestore for writing and for reading the user’s navigation context, and then deploy the model on AI Platform Prediction
• C. Embed the client on the website, deploy the gateway on App Engine, deploy the database on Cloud Bigtable for writing and for reading the user’s navigation context, and then deploy the model on AI Platform Prediction
• D. Embed the client on the website, deploy the gateway on App Engine, deploy the database on Memorystore for writing and for reading the user’s navigation context, and then deploy the model on Google Kubernetes Engine

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

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. What should you do?

• A. Create a Vertex AI Workbench notebook. Use IPython magic to run the CREATE MODEL statement to create an ARIMA model
• B. Access BigQuery Studio in the Google Cloud console. Run the CREATE MODEL statement in the SQL editor to create an AutoML regression model
• C. Create a Vertex AI Workbench notebook. Use IPython magic to run the CREATE MODEL statement to create an AutoML regression model
• D. Access BigQuery Studio in the Google Cloud console. Run the CREATE MODEL statement in the SQL editor to create an ARIMA model

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

Your team has been tasked with creating an ML solution in Google Cloud to classify support requests for one of your platforms. You analyzed the requirements and decided to use TensorFlow to build the classifier so that you have full control of the model's code, serving, and deployment. You will use Kubeflow pipelines for the ML platform. To save time, you want to build on existing resources and use managed services instead of building a completely new model. How should you build the classifier?

• A. Use the Natural Language API to classify support requests
• B. Use AutoML Natural Language to build the support requests classifier
• C. Use an established text classification model on AI Platform to perform transfer learning
• D. Use an established text classification model on AI Platform as-is to classify support requests

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

• A. 1. Upload the audio files to Cloud Storage 2. Call the speech:longrunningrecognize API endpoint to generate transcriptions 3. Call the predict method of an AutoML sentiment analysis model to analyze the transcriptions
• B. 1. Upload the audio files to Cloud Storage. 2. Call the speech:longrunningrecognize API endpoint to generate transcriptions 3. Create a Cloud Function that calls the Natural Language API by using the analyzeSentiment method
• C. 1. Iterate over your local files in Python 2. Use the Speech-to-Text Python library to create a speech.RecognitionAudio object, and set the content to the audio file data 3. Call the speech:recognize API endpoint to generate transcriptions 4. Call the predict method of an AutoML sentiment analysis model to analyze the transcriptions
• D. 1. Iterate over your local files in Python 2. Use the Speech-to-Text Python Library to create a speech.RecognitionAudio object and set the content to the audio file data 3. Call the speech:longrunningrecognize API endpoint to generate transcriptions. 4. Call the Natural Language API by using the analyzeSentiment method