Free Google Professional-Machine-Learning-Engineer Exam Dumps Questions & Answers

You are an ML engineer responsible for designing and implementing training pipelines for ML models. You need to create an end-to-end training pipeline for a TensorFlow model. The TensorFlow model will be trained on several terabytes of structured data. You need the pipeline to include data quality checks before training and model quality checks after training but prior to deployment. You want to minimize development time and the need for infrastructure maintenance. How should you build and orchestrate your training pipeline?

• A.  Create the pipeline using Kubeflow Pipelines domain-specific language (DSL) and predefined Google Cloud components. Orchestrate the pipeline using Kubeflow Pipelines deployed on Google Kubernetes Engine.
• B.  Create the pipeline using TensorFlow Extended (TFX) and standard TFX components. Orchestrate the pipeline using Kubeflow Pipelines deployed on Google Kubernetes Engine.
• C.  Create the pipeline using TensorFlow Extended (TFX) and standard TFX components. Orchestrate the pipeline using Vertex AI Pipelines.
• D.  Create the pipeline using Kubeflow Pipelines domain-specific language (DSL) and predefined Google Cloud components. Orchestrate the pipeline using Vertex AI Pipelines.

You work for a startup that has multiple data science workloads. Your compute infrastructure is currently on- premises. and the data science workloads are native to PySpark Your team plans to migrate their data science workloads to Google Cloud You need to build a proof of concept to migrate one data science job to Google Cloud You want to propose a migration process that requires minimal cost and effort. What should you do first?

• A.  Create a Google Kubemetes Engine cluster with a basic node pool configuration install Java Scala, and Apache Spark dependencies on it.
• B.  Create a Vertex Al Workbench notebook with instance type n2-standard-4.
• C.  Create a n2-standard-4 VM instance and install Java, Scala and Apache Spark dependencies on it.
• D.  Create a Standard (1 master. 3 workers) Dataproc cluster, and run a Vertex Al Workbench notebook instance on it.

You are implementing a batch inference ML pipeline in Google Cloud. The model was developed using TensorFlow and is stored in SavedModel format in Cloud Storage You need to apply the model to a historical dataset containing 10 TB of data that is stored in a BigQuery table How should you perform the inference?

• A.  Export the historical data to Cloud Storage in CSV format Configure a Vertex Al batch prediction job to generate predictions for the exported data.
• B.  Configure a Vertex Al batch prediction job to apply the model to the historical data in BigQuery
• C.  Export the historical data to Cloud Storage in Avro format. Configure a Vertex Al batch prediction job to generate predictions for the exported data.
• D.  Import the TensorFlow model by using the create model statement in BigQuery ML Apply the historical data to the TensorFlow model.

You work for a bank with strict data governance requirements. You recently implemented a custom model to detect fraudulent transactions You want your training code to download internal data by using an API endpoint hosted in your projects network You need the data to be accessed in the most secure way, while mitigating the risk of data exfiltration. What should you do?

• A.  Download the data to a Cloud Storage bucket before calling the training job
• B.  Create a Cloud Run endpoint as a proxy to the data Use Identity and Access Management (1AM) authentication to secure access to the endpoint from the training job.
• C.  Configure VPC Peering with Vertex Al and specify the network of the training job
• D.  Enable VPC Service Controls for peering's, and add Vertex Al to a service perimeter

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