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| Certification Vendor: | Google Cloud |
|---|---|
| Exam Name: | Google Cloud Certified - Professional Machine Learning Engineer |
| Exam Number: | Professional-Machine-Learning-Engineer |
| Available Languages: | English, Japanese |
| Exam Duration: | 120 minutes |
| Exam Format: | Multiple choice, Multiple select |
| Exam Price: | $200 USD |
| Certificate Validity Period: | 2 years |
| Real Exam Qty: | 50-60 |
| Related Certifications: | Google Cloud Certified - Professional Data Engineer |
| Passing Score: | Not publicly disclosed (Pass/Fail) |
| Sample Questions: | Google Professional-Machine-Learning-Engineer Sample Questions |
| Exam Way: | Online (proctored) or Test center (Kryterion) |
| Pre Condition: | Recommended 3+ years of industry experience with ML models and 1+ year of experience using Google Cloud. |
| Official Syllabus URL: | https://cloud.google.com/learn/certification/machine-learning-engineer |
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Google Professional Machine Learning Engineer Certification Exam is an opportunity for individuals to validate their expertise in the field of machine learning. Google Professional Machine Learning Engineer certification exam is designed to test the individual's knowledge of machine learning concepts and their ability to apply these concepts in real-world scenarios. It is a rigorous exam that requires individuals to demonstrate their ability to design, build, and deploy scalable machine learning models using Google Cloud Platform.
NEW QUESTION # 278
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?
Answer: B
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 performance, you can recompile TensorFlow Serving using the source code and enable CPU-specific optimizations, such as AVX, AVX2, and FMA1. 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 match your workload requirements2.
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.
Reference:
Building TensorFlow Serving from source
Specifying a minimum CPU platform for a node pool
NEW QUESTION # 279
A city wants to monitor its air quality to address the consequences of air pollution. A Machine Learning Specialist needs to forecast the air quality in parts per million of contaminates for the next 2 days in the city. As this is a prototype, only daily data from the last year is available.
Which model is MOST likely to provide the best results in Amazon SageMaker?
Answer: A
Explanation:
Explanation/Reference: https://aws.amazon.com/blogs/machine-learning/build-a-model-to-predict-the-impact-of-weather- on-urban-air-quality-using-amazon-sagemaker/?ref=Welcome.AI
NEW QUESTION # 280
You have trained a deep neural network model on Google Cloud. The model has low loss on the training data, but is performing worse on the validation dat a. You want the model to be resilient to overfitting. Which strategy should you use when retraining the model?
Answer: D
Explanation:
Applying a L2 regularization parameter of 0.4 and decreasing the learning rate by a factor of 10 can help to reduce overfitting and make the model more resilient. Source: Google Cloud
NEW QUESTION # 281
You need to execute a batch prediction on 100 million records in a BigQuery table with a custom TensorFlow DNN regressor model, and then store the predicted results in a BigQuery table. You want to minimize the effort required to build this inference pipeline. What should you do?
Answer: B
Explanation:
Option A is correct because importing the TensorFlow model with BigQuery ML, and running the ml.predict function is the easiest way to execute a batch prediction on a large BigQuery table with a custom TensorFlow model, and store the predicted results in another BigQuery table. BigQuery ML allows you to import TensorFlow models that are stored in Cloud Storage, and use them for prediction with SQL queries1. The ml.predict function returns a table with the predicted values, which can be saved to another BigQuery table2.
Option B is incorrect because using the TensorFlow BigQuery reader to load the data, and using the BigQuery API to write the results to BigQuery requires more effort to build the inference pipeline than option A. The TensorFlow BigQuery reader is a way to read data from BigQuery into TensorFlow datasets, which can be used for training or prediction3. However, this option also requires writing code to load the TensorFlow model, run the prediction, and use the BigQuery API to write the results back to BigQuery4.
Option C is incorrect because creating a Dataflow pipeline to convert the data in BigQuery to TFRecords, running a batch inference on Vertex AI Prediction, and writing the results to BigQuery requires more effort to build the inference pipeline than option A. Dataflow is a service for creating and running data processing pipelines, such as ETL (extract, transform, load) or batch processing5. Vertex AI Prediction is a service for deploying and serving ML models for online or batch prediction. However, this option also requires writing code to create the Dataflow pipeline, convert the data to TFRecords, run the batch inference, and write the results to BigQuery.
Option D is incorrect because loading the TensorFlow SavedModel in a Dataflow pipeline, using the BigQuery I/O connector with a custom function to perform the inference within the pipeline, and writing the results to BigQuery requires more effort to build the inference pipeline than option A. The BigQuery I/O connector is a way to read and write data from BigQuery within a Dataflow pipeline. However, this option also requires writing code to load the TensorFlow SavedModel, create the custom function for inference, and write the results to BigQuery.
Reference:
Importing models into BigQuery ML
Using imported models for prediction
TensorFlow BigQuery reader
BigQuery API
Dataflow overview
[Vertex AI Prediction overview]
[Batch prediction with Dataflow]
[BigQuery I/O connector]
[Using TensorFlow models in Dataflow]
NEW QUESTION # 282
You trained a text classification model. You have the following SignatureDefs:
You started a TensorFlow-serving component server and tried to send an HTTP request to get a prediction using:
headers = {"content-type": "application/json"}
json_response = requests.post('http:
//localhost:8501/v1/models/text_model:predict', data=data,
headers=headers)
What is the correct way to write the predict request?
Answer: D
Explanation:
A negative number in the shape enables auto expand
(https://stackoverflow.com/questions/37956197/what-is-the-negative-index-in-shape-arrays-used- for-tensorflow).
Then the first number -1 out of the shape (-1, 2) speaks the number of 1 dimensional arrays within the tensor (and it can autoexpand) while the second numer (2) sets the number of elements in the inner array at 2.
NEW QUESTION # 283
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