Google chinesedumps legit Professional-machine-learning-engineer Exam Download by Lilly-mae q116 vce pdf

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Exam Name:	Google Professional Machine Learning Engineer
Exam Code:	Professional-Machine-Learning-Engineer Dumps
Vendor:	Google	Certification:	Machine Learning Engineer
Questions:	296 Q&A's	Shared By:	lilly-mae

Question 24

You developed a Python module by using Keras to train a regression model. You developed two model architectures, linear regression and deep neural network (DNN). within the same module. You are using the – raining_method argument to select one of the two methods, and you are using the Learning_rate-and num_hidden_layers arguments in the DNN. You plan to use Vertex Al ' s hypertuning service with a Budget to perform 100 trials. You want to identify the model architecture and hyperparameter values that minimize training loss and maximize model performance What should you do?

Options:

Run one hypertuning job for 100 trials. Set num hidden_layers as a conditional hypetparameter based on its parent hyperparameter training_mothod. and set learning rate as a non-conditional hyperparameter

Run two separate hypertuning jobs. a linear regression job for 50 trials, and a DNN job for 50 trials Compare their final performance on a

common validation set. and select the set of hyperparameters with the least training loss

Run one hypertuning job for 100 trials Set num_hidden_layers and learning_rate as conditional hyperparameters based on their parent hyperparameter training method.

Run one hypertuning job with training_method as the hyperparameter for 50 trials Select the architecture with the lowest training loss. and further hypertune It and its corresponding hyperparameters for 50 trials

Discussion

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Question 25

You developed an ML model with Al 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?

Options:

Significantly increase the max_batch_size TensorFlow Serving parameter

Switch to the tensorflow-model-server-universal version of TensorFlow Serving

Significantly increase the max_enqueued_batches TensorFlow Serving parameter

Recompile TensorFlow Serving using the source to support CPU-specific optimizations Instruct GKE to choose an appropriate baseline minimum CPU platform for serving nodes

Discussion

Answer:

Explanation:

TensorFlow Serving is a service that allows you to deploy and serve TensorFlow models in a scalable and efficient way. TensorFlow Serving supports various platforms and hardware, such as CPU, GPU, and TPU. However, the default TensorFlow Serving binaries are built with generic CPU instructions, which may not leverage the full potential of the CPU architecture. To improve the serving latency and p erformance, you can recompile TensorFlow Serving using the source code and enable CPU-specific optimizations, such as AVX, AVX2, and FMA 1 . These optimizations can speed up the computation and inference of the TensorFlow models, especially for deep neural networks.

Google Kubernetes Engine (GKE) is a service that allows you to run and manage containerized applications on Google Cloud using Kubernetes. GKE supports various types and sizes of nodes, which are the virtual machines that run the containers. GKE also supports different CPU platforms, which are the generations and models of the CPUs that power the nodes. GKE allows you to choose a baseline minimum CPU platform for your node pool, which is a group of nodes with the same configuration. By choosing a baseline minimum CPU platform, you can ensure that your nodes have the CPU features and capabilities that ma tch your workload requirements 2 .

For the use case of serving a few thousand queries per second and experiencing latency issues, the best option is to recompile TensorFlow Serving using the source to support CPU-specific optimizations, and instruct GKE to choose an appropriate baseline minimum CPU platform for serving nodes. This option can improve the serving latency and performance without changing the underlying infrastructure, as it only involves rebuilding the TensorFlow Serving binary and selecting the CPU platform for the GKE nodes. This option can also take advantage of the CPU-only pods that are running on GKE, as it can optimize the CPU utilization and efficiency. Therefore, recompiling TensorFlow Serving using the source to support CPU-specific optimizations and instructing GKE to choose an appropriate baseline minimum CPU platform for serving nodes is the best option for this use case.

[References:, Building TensorFlow Serving from source, Specifying a minimum CPU platform for a node pool, ]

Question 26

You need to use TensorFlow to train an image classification model. Your dataset is located in a Cloud Storage directory and contains millions of labeled images Before training the model, you need to prepare the data. You want the data preprocessing and model training workflow to be as efficient scalable, and low maintenance as possible. What should you do?

Options:

1 Create a Dataflow job that creates sharded TFRecord files in a Cloud Storage directory.

2 Reference tf .data.TFRecordDataset in the training script.

3. Train the model by using Vertex Al Training with a V100 GPU.

1 Create a Dataflow job that moves the images into multiple Cloud Storage directories, where each directory is named according to the corresponding label.

2 Reference tfds.fclder_da-asst.imageFclder in the training script.

3. Train the model by using Vertex AI Training with a V100 GPU.

1 Create a Jupyter notebook that uses an n1-standard-64, V100 GPU Vertex Al Workbench instance.

2 Write a Python script that creates sharded TFRecord files in a directory inside the instance

3. Reference tf. da-a.TFRecrrdDataset in the training script.

4. Train the model by using the Workbench instance.

1 Create a Jupyter notebook that uses an n1-standard-64, V100 GPU Vertex Al Workbench instance.

2 Write a Python scnpt that copies the images into multiple Cloud Storage directories, where each directory is named according to the corresponding label.

3 Reference tf ds. f older_dataset. imageFolder in the training script.

4. Train the model by using the Workbench instance.

Discussion

Question 27

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?

Options:

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.

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.

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.

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.