support@Free-Braindumps.com
Register Login
Cisco
CompTIA
ISC
EC-Council
EXIN
Fortinet
ITIL
Juniper
Microsoft
VMware
Avaya
SAP
Google
NVIDIA
Salesforce
Amazon
Palo Alto Networks
ISC2
CrowdStrike
Anthropic
All Vendors
  1. Home
  2. Exams
  3. Google
  4. PROFESSIONAL MACHINE LEARNING ENGINEER

Free PROFESSIONAL MACHINE LEARNING ENGINEER Exam Braindumps
Professional Machine Learning Engineer Questions 11–15 (page: 4)

Updated On: 25-Oct-2026
Page 4 of 82
PDF with 402 Questions

You are training a ResNet model on Vertex AI using TPUs to visually categorize types of defects in automobile engines. You capture the training profile using the Cloud TPU profiler plugin and observe that it is highly input-bound. You want to reduce the bottleneck and speed up your model training process.
Which modifications should you make to the tf.data dataset? (Choose two.)

  1. Use the interleave option for reading data.
  2. Reduce the value of the repeat parameter.
  3. Increase the buffer size for the shuttle option.
  4. Set the prefetch option equal to the training batch size.
  5. Decrease the batch size argument in your transformation.

Answer(s): A,D



You are training a custom language model for your company using a large dataset. You plan to use the Reduction Server strategy on Vertex AI. You need to configure the worker pools of the distributed training job.
What should you do?

  1. Configure the machines of the first two worker pools to have GPUs, and to use a container image where your training code runs. Configure the third worker pool to have GPUs, and use the reductionserver container image.
  2. Configure the machines of the first two worker pools to have GPUs, and to use a container image where your training code runs. Configure the third worker pool to use the reductionserver container image without accelerators, and choose a machine type that prioritizes bandwidth.
  3. Configure the machines of the first two worker pools to have TPUs, and to use a container image where your training code runs. Configure the third worker pool without accelerators, and use the reductionserver container image without accelerators, and choose a machine type that prioritizes bandwidth.
  4. Configure the machines of the first two pools to have TPUs, and to use a container image where your training code runs. Configure the third pool to have TPUs, and use the reductionserver container image.

Answer(s): B



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?

  1. 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
  2. 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.
  3. 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
  4. 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

Answer(s): C



You developed a Transformer model in TensorFlow to translate text. Your training data includes millions of documents in a Cloud Storage bucket. You plan to use distributed training to reduce training time. You need to configure the training job while minimizing the effort required to modify code and to manage the cluster’s configuration.
What should you do?

  1. Create a Vertex AI custom training job with GPU accelerators for the second worker pool. Use tf.distribute.MultiWorkerMirroredStrategy for distribution.
  2. Create a Vertex AI custom distributed training job with Reduction Server. Use N1 high-memory machine type instances for the first and second pools, and use N1 high-CPU machine type instances for the third worker pool.
  3. Create a training job that uses Cloud TPU VMs. Use tf.distribute.TPUStrategy for distribution.
  4. Create a Vertex AI custom training job with a single worker pool of A2 GPU machine type instances. Use tf.distribute.MirroredStrategv for distribution.

Answer(s): A



You are training an LSTM-based model on AI Platform to summarize text using the following job submission script:
gcloud ai-platform jobs submit training $JOB_NAME \
--package-path $TRAINER_PACKAGE_PATH \
--module-name $MAIN_TRAINER_MODULE \
--job-dir $JOB_DIR \
--region $REGION \
--scale-tier basic \
-- \
--epochs 20 \
--batch_size=32 \
--learning_rate=0.001 \
You want to ensure that training time is minimized without significantly compromising the accuracy of your model.
What should you do?

  1. Modify the ‘epochs’ parameter.
  2. Modify the ‘scale-tier’ parameter.
  3. Modify the ‘batch size’ parameter.
  4. Modify the ‘learning rate’ parameter.

Answer(s): B



  PDF
Viewing page 4 of 82
Viewing questions 16 - 20 out of 402 questions


Post your Comments and Discuss Google PROFESSIONAL MACHINE LEARNING ENGINEER exam prep with other Community members:

AI Tutor AI Tutor 👋 I’m here to help!