Free Google Professional-Machine-Learning-Engineer Exam Questions

Question 1

You are collaborating on a model prototype with your team. You need to create a Vertex Al 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 Al User role to each team member.4. Provision a Vertex Al Workbench user-managed notebook instance that uses the new service account.

B : 1. Grant the Vertex Al 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 serviceaccount.3. Provision a Vertex Al Workbench user-managed notebook instance that uses the default ComputeEngine service account.

C : 1 Create a new service account and grant it the Vertex Al 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 Al Workbench user-managed notebook instance that uses the new service account.

D : 1 Grant the Vertex Al User role to the primary team member.2. Grant the Notebook Viewer role to the other team members.3. Provision a Vertex Al Workbench user-managed notebook instance that uses the primary usersaccount.

Answer: C

Question 2

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 : crease the instance memory to 512 GB and increase the batch size.

B : place the NVIDIA P100 GPU with a v3-32 TPU in the training job.

C : able early stopping in your Vertex AI Training job.

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

Answer: C

Question 3

You work for a food product company. Your company’s historical sales data is stored in BigQuery.You need to use Vertex AI’s custom training service to train multiple TensorFlow models that read the data from BigQuery and predict future sales. You plan to implement a data preprocessing algorithm that performs mm-max scaling and bucketing on a large number of features before you start experimenting with the models. You want to minimize preprocessing time, cost, and development effort. How should you configure this workflow?

A : Write the transformations into Spark that uses the spark-bigquery-connector, and use Dataproc to preprocess the data.

B : Write SQL queries to transform the data in-place in BigQuery.

C : Add the transformations as a preprocessing layer in the TensorFlow models.

D : Create a Dataflow pipeline that uses the BigQuerylO connector to ingest the data, process it, and write it back to BigQuery.

Answer: B

Question 4

You work for a semiconductor manufacturing company. You need to create a real-time application that automates the quality control process High-definition images of each semiconductor are taken at the end of the assembly line in real time. The photos are uploaded to a Cloud Storage bucket along with tabular data that includes each semiconductor's batch number serial number dimensions, and weight You need to configure model training and serving while maximizing model accuracy. What should you do?

A : Use Vertex Al Data Labeling Service to label the images and train an AutoML image classification model. Deploy the model and configure Pub/Sub to publish a message when an image is categorized into the failing class.

B : Use Vertex Al Data Labeling Service to label the images and train an AutoML image classification model. Schedule a daily batch prediction job that publishes a Pub/Sub message when the job completes.

C : Convert the images into an embedding representation Import this data into BigQuery, and train a BigQuery. ML K-means clustenng model with two clusters Deploy the model and configure Pub/Sub to publish a message when a semiconductor's data is categorized into the failing cluster.

D : Import the tabular data into BigQuery use Vertex Al Data Labeling Service to label the data and train an AutoML tabular classification model Deploy the model and configure Pub/Sub to publish a message when a semiconductor's data is categorized into the failing class.

Answer: D

Question 5

While running a model training pipeline on Vertex Al, you discover that the evaluation step is failing because of an out-of-memory error. You are currently using TensorFlow Model Analysis (TFMA) with a standard Evaluator TensorFlow Extended (TFX) pipeline component for the evaluation step. You want to stabilize the pipeline without downgrading the evaluation quality while minimizing infrastructure overhead. What should you do?

A : clude the flag -runner=DataflowRunner in beam_pipeline_args to run the evaluation step on Dataflow.

B : ve the evaluation step out of your pipeline and run it on custom Compute Engine VMs with sufficient memory.

C : grate your pipeline to Kubeflow hosted on Google Kubernetes Engine, and specify the appropriate node parameters for the evaluation step.

D : tfma.MetricsSpec () to limit the number of metrics in the evaluation step.

Answer: C