Google PROFESSIONAL-MACHINE-LEARNING-ENGINEER Online Practice Questions and Exam Preparation

Google PROFESSIONAL-MACHINE-LEARNING-ENGINEER Online Questions & Answers

• Question 121:

  You recently joined a machine learning team that will soon release a new project. As a lead on the project, you are asked to determine the production readiness of the ML components. The team has already tested features and data, model development, and infrastructure. Which additional readiness check should you recommend to the team?

  A. Ensure that training is reproducible.
  B. Ensure that all hyperparameters are tuned.
  C. Ensure that model performance is monitored.
  D. Ensure that feature expectations are captured in the schema.
  C. Ensure that model performance is monitored.

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  Explanation/Reference:

  https://developers.google.com/machine-learning/testing-debugging/pipeline/deploying https://developers.google.com/machine-learning/testing-debugging/pipeline/production

• Question 122:

  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.
  D. Use the tf.distribute.Strategy API and run a distributed training job.

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  Explanation/Reference:

• Question 123:

  You have built a model that is trained on data stored in Parquet files. You access the data through a Hive table hosted on Google Cloud. You preprocessed these data with PySpark and exported it as a CSV file into Cloud Storage. After preprocessing, you execute additional steps to train and evaluate your model. You want to parametrize this model training in Kubeflow Pipelines. What should you do?

  A. Remove the data transformation step from your pipeline.
  B. Containerize the PySpark transformation step, and add it to your pipeline.
  C. Add a ContainerOp to your pipeline that spins a Dataproc cluster, runs a transformation, and then saves the transformed data in Cloud Storage.
  D. Deploy Apache Spark at a separate node pool in a Google Kubernetes Engine cluster. Add a ContainerOp to your pipeline that invokes a corresponding transformation job for this Spark instance.
  C. Add a ContainerOp to your pipeline that spins a Dataproc cluster, runs a transformation, and then saves the transformed data in Cloud Storage.

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  Explanation/Reference:

  https://kubeflow-pipelines.readthedocs.io/en/stable/source/kfp.dsl.html#kfp.dsl.ContainerOp https://medium.com/@vignesh093/running-preprocessing-and-ml-workflow-in-kubeflow-with-google-dataproc-84103a9ef67e

• Question 124:

  You work for a retail company. You have been tasked with building a model to determine the probability of churn for each customer. You need the predictions to be interpretable so the results can be used to develop marketing campaigns that target at-risk customers. What should you do?

  A. Build a random forest regression model in a Vertex AI Workbench notebook instance. Configure the model to generate feature importances after the model is trained.
  B. Build an AutoML tabular regression model. Configure the model to generate explanations when it makes predictions.
  C. Build a custom TensorFlow neural network by using Vertex AI custom training. Configure the model to generate explanations when it makes predictions.
  D. Build a random forest classification model in a Vertex AI Workbench notebook instance. Configure the model to generate feature importances after the model is trained.
  B. Build an AutoML tabular regression model. Configure the model to generate explanations when it makes predictions.

• Question 125:

  You are an ML engineer at a regulated insurance company. You are asked to develop an insurance approval model that accepts or rejects insurance applications from potential customers. What factors should you consider before building the model?

  A. Redaction, reproducibility, and explainability
  B. Traceability, reproducibility, and explainability
  C. Federated learning, reproducibility, and explainability D. Differential privacy, federated learning, and explainability
  B. Traceability, reproducibility, and explainability

• Question 126:

  You are using transfer learning to train an image classifier based on a pre-trained EfficientNet model. Your training dataset has 20,000 images. You plan to retrain the model once per day. You need to minimize the cost of infrastructure. What platform components and configuration environment should you use?

  A. A Deep Learning VM with 4 V100 GPUs and local storage.
  B. A Deep Learning VM with 4 V100 GPUs and Cloud Storage.
  C. A Google Kubernetes Engine cluster with a V100 GPU Node Pool and an NFS Server
  D. An AI Platform Training job using a custom scale tier with 4 V100 GPUs and Cloud Storage
  D. An AI Platform Training job using a custom scale tier with 4 V100 GPUs and Cloud Storage

• Question 127:

  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.
  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.

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  Explanation/Reference:

• Question 128:

  You are an ML engineer on an agricultural research team working on a crop disease detection tool to detect leaf rust spots in images of crops to determine the presence of a disease. These spots, which can vary in shape and size, are correlated to the severity of the disease. You want to develop a solution that predicts the presence and severity of the disease with high accuracy. What should you do?

  A. Create an object detection model that can localize the rust spots.
  B. Develop an image segmentation ML model to locate the boundaries of the rust spots.
  C. Develop a template matching algorithm using traditional computer vision libraries.
  D. Develop an image classification ML model to predict the presence of the disease.
  B. Develop an image segmentation ML model to locate the boundaries of the rust spots.

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  Explanation/Reference:

  An image segmentation model is well-suited for this task because it can identify the exact location and shape of the rust spots in the image, which is critical for determining the severity of the disease. Once the rust spots have been identified, other algorithms can be used to analyze the data and predict the severity of the disease. Object detection models are another option, but they may not be as accurate as image segmentation models when it comes to identifying the exact boundaries of the rust spots. Template matching algorithms using traditional computer vision libraries are generally not as accurate as ML models when it comes to image analysis.

• Question 129:

  You trained a model packaged it with a custom Docker container for serving, and deployed it to Vertex AI Model Registry. When you submit a batch prediction job, it fails with this error: "Error model server never became ready. Please validate that your model file or container configuration are valid. " There are no additional errors in the logs. What should you do?

  A. Add a logging configuration to your application to emit logs to Cloud Logging
  B. Change the HTTP port in your model's configuration to the default value of 8080
  C. Change the healthRoute value in your model's configuration to /healthcheck
  D. Pull the Docker image locally, and use the docker run command to launch it locally. Use the docker logs command to explore the error logs
  D. Pull the Docker image locally, and use the docker run command to launch it locally. Use the docker logs command to explore the error logs

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  Explanation/Reference:

• Question 130:

  You have been asked to build a model using a dataset that is stored in a medium-sized (~10 GB) BigQuery table. You need to quickly determine whether this data is suitable for model development. You want to create a one-time report that includes both informative visualizations of data distributions and more sophisticated statistical analyses to share with other ML engineers on your team. You require maximum flexibility to create your report. What should you do?

  A. Use Vertex AI Workbench user-managed notebooks to generate the report.
  B. Use the Google Data Studio to create the report.
  C. Use the output from TensorFlow Data Validation on Dataflow to generate the report.
  D. Use Dataprep to create the report.
  A. Use Vertex AI Workbench user-managed notebooks to generate the report.