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

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

• Question 211:

  You built a deep learning-based image classification model by using on-premises data. You want to use Vertex AI to deploy the model to production. Due to security concerns, you cannot move your data to the cloud. You are aware that the input data distribution might change over time. You need to detect model performance changes in production. What should you do?

  A. Use Vertex Explainable AI for model explainability. Configure feature-based explanations.
  B. Use Vertex Explainable AI for model explainability. Configure example-based explanations.
  C. Create a Vertex AI Model Monitoring job. Enable training-serving skew detection for your model.
  D. Create a Vertex AI Model Monitoring job. Enable feature attribution skew and drift detection for your model.
  D. Create a Vertex AI Model Monitoring job. Enable feature attribution skew and drift detection for your model.

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  Explanation/Reference:

• Question 212:

  Your team is training a large number of ML models that use different algorithms, parameters, and datasets. Some models are trained in Vertex AI Pipelines, and some are trained on Vertex AI Workbench notebook instances. Your team wants to compare the performance of the models across both services. You want to minimize the effort required to store the parameters and metrics. What should you do?

  A. Implement an additional step for all the models running in pipelines and notebooks to export parameters and metrics to BigQuery.
  B. Create a Vertex AI experiment. Submit all the pipelines as experiment runs. For models trained on notebooks log parameters and metrics by using the Vertex AI SDK.
  C. Implement all models in Vertex AI Pipelines Create a Vertex AI experiment, and associate all pipeline runs with that experiment.
  D. Store all model parameters and metrics as model metadata by using the Vertex AI Metadata API.
  B. Create a Vertex AI experiment. Submit all the pipelines as experiment runs. For models trained on notebooks log parameters and metrics by using the Vertex AI SDK.

• Question 213:

  You are collaborating on a model prototype with your team. You need to create a Vertex AI Workbench environment for the members of your team and also limit access to other employees in your project. What should you do?

  A. 1. Create a new service account and grant it the Notebook Viewer role 2. Grant the Service Account User role to each team member on the service account 3. Grant the Vertex AI User role to each team member 4. Provision a Vertex AI Workbench user-managed notebook instance that uses the new service account
  B. 1. Grant the Vertex AI User role to the default Compute Engine service account 2. Grant the Service Account User role to each team member on the default Compute Engine service account 3. Provision a Vertex AI Workbench user-managed notebook instance that uses the default Compute Engine service account.
  C. 1. Create a new service account and grant it the Vertex AI User role 2. Grant the Service Account User role to each team member on the service account 3. Grant the Notebook Viewer role to each team member. 4. Provision a Vertex AI Workbench user-managed notebook instance that uses the new service account
  D. 1. Grant the Vertex AI User role to the primary team member 2. Grant the Notebook Viewer role to the other team members 3. Provision a Vertex AI Workbench user-managed notebook instance that uses the primary user's account
  C. 1. Create a new service account and grant it the Vertex AI User role 2. Grant the Service Account User role to each team member on the service account 3. Grant the Notebook Viewer role to each team member. 4. Provision a Vertex AI Workbench user-managed notebook instance that uses the new service account

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  Explanation/Reference:

• Question 214:

  Your data science team has requested a system that supports scheduled model retraining, Docker containers, and a service that supports autoscaling and monitoring for online prediction requests. Which platform components should you choose for this system?

  A. Vertex AI Pipelines and App Engine
  B. Vertex AI Pipelines, Vertex AI Prediction, and Vertex AI Model Monitoring
  C. Cloud Composer, BigQuery ML, and Vertex AI Prediction
  D. Cloud Composer, Vertex AI Training with custom containers, and App Engine
  B. Vertex AI Pipelines, Vertex AI Prediction, and Vertex AI Model Monitoring

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  Explanation/Reference:

  https://cloud.google.com/vertex-ai/docs/training/containers-overview

• Question 215:

  You are an ML engineer in the contact center of a large enterprise. You need to build a sentiment analysis tool that predicts customer sentiment from recorded phone conversations. You need to identify the best approach to building a model while ensuring that the gender, age, and cultural differences of the customers who called the contact center do not impact any stage of the model development pipeline and results. What should you do?

  A. Convert the speech to text and extract sentiments based on the sentences.
  B. Convert the speech to text and build a model based on the words.
  C. Extract sentiment directly from the voice recordings.
  D. Convert the speech to text and extract sentiment using syntactical analysis.
  A. Convert the speech to text and extract sentiments based on the sentences.

• Question 216:

  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.
  D. Configure a n1-standard-4 VM with 4 NVIDIA P100 GPUs. SSH into the VM and use MultiWorkerMirroredStrategy to train the model.

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  Explanation/Reference:

• Question 217:

  Your team has a model deployed to a Vertex AI endpoint. You have created a Vertex AI pipeline that automates the model training process and is triggered by a Cloud Function. You need to prioritize keeping the model up-to-date, but also minimize retraining costs. How should you configure retraining?

  A. Configure Pub/Sub to call the Cloud Function when a sufficient amount of new data becomes available
  B. Configure a Cloud Scheduler job that calls the Cloud Function at a predetermined frequency that fits your team's budget
  C. Enable model monitoring on the Vertex AI endpoint. Configure Pub/Sub to call the Cloud Function when anomalies are detected
  D. Enable model monitoring on the Vertex AI endpoint. Configure Pub/Sub to call the Cloud Function when feature drift is detected
  D. Enable model monitoring on the Vertex AI endpoint. Configure Pub/Sub to call the Cloud Function when feature drift is detected

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  Explanation/Reference:

• Question 218:

  Your data science team is training a PyTorch model for image classification based on a pre-trained RestNet model. You need to perform hyperparameter tuning to optimize for several parameters. What should you do?

  A. Convert the model to a Keras model, and run a Keras Tuner job.
  B. Run a hyperparameter tuning job on AI Platform using custom containers.
  C. Create a Kuberflow Pipelines instance, and run a hyperparameter tuning job on Katib.
  D. Convert the model to a TensorFlow model, and run a hyperparameter tuning job on AI Platform.
  B. Run a hyperparameter tuning job on AI Platform using custom containers.

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  Explanation/Reference:

  https://cloud.google.com/blog/topics/developers-practitioners/pytorch-google-cloud-how-train-and-tune-pytorch-models-vertex-ai https://cloud.google.com/blog/topics/developers-practitioners/pytorch-google-cloud-how-deploy-pytorch-modelsvertex-ai

• Question 219:

  You are analyzing customer data for a healthcare organization that is stored in Cloud Storage. The data contains personally identifiable information (PII). You need to perform data exploration and preprocessing while ensuring the security and privacy of sensitive fields. What should you do?

  A. Use the Cloud Data Loss Prevention (DLP) API to de-identify the PII before performing data exploration and preprocessing.
  B. Use customer-managed encryption keys (CMEK) to encrypt the PII data at rest, and decrypt the PII data during data exploration and preprocessing.
  C. Use a VM inside a VPC Service Controls security perimeter to perform data exploration and preprocessing.
  D. Use Google-managed encryption keys to encrypt the PII data at rest, and decrypt the PII data during data exploration and preprocessing.
  A. Use the Cloud Data Loss Prevention (DLP) API to de-identify the PII before performing data exploration and preprocessing.

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  Explanation/Reference:

• Question 220:

  You developed an ML model with AI Platform, and you want to move it to production. You serve a few thousand queries per second and are experiencing latency issues. Incoming requests are served by a load balancer that distributes them across multiple Kubeflow CPU-only pods running on Google Kubernetes Engine (GKE). Your goal is to improve the serving latency without changing the underlying infrastructure. What should you do?

  A. Significantly increase the max_batch_size TensorFlow Serving parameter.
  B. Switch to the tensorflow-model-server-universal version of TensorFlow Serving.
  C. Significantly increase the max_enqueued_batches TensorFlow Serving parameter.
  D. Recompile TensorFlow Serving using the source to support CPU-specific optimizations. Instruct GKE to choose an appropriate baseline minimum CPU platform for serving nodes.
  D. Recompile TensorFlow Serving using the source to support CPU-specific optimizations. Instruct GKE to choose an appropriate baseline minimum CPU platform for serving nodes.