Unveil details about the GCP-PMLE exam topics and questions. Become familiar with the proven practice tests, study guide materials, and sample questions. It will help to make your Google Cloud Platform – Professional Machine Learning Engineer (GCP-PMLE) certification journey easy.
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Google GCP-PMLE Exam Summary:-
- Name of the Exam: Google Professional Machine Learning Engineer
- Exam Code: GCP-PMLE
- Exam Price: $200 USD
- Duration: 120 minutes
- Number of Questions: 60
- Passing Score: Pass / Fail (Approx 70%)
Google GCP-PMLE Exam Topics:-
- Framing ML problems
- Architecting ML solutions
- Designing data preparation and processing systems
- Developing ML models
- Automating and orchestrating ML pipelines
- Monitoring, optimizing, and maintaining ML solutions
Improve Your Knowledge-Base:
Get access to some of the sample GCP-PMLE practice test questions to boost your knowledge base. Preparation is a steady process for some time, and you must always try to keep improving via learning. Access the questions from anywhere and from any device. The easy access to the questions would help you to get better with the GCP-PMLE exam topics. You can solve these questions easily or learn new things from them.
Here Are Some Useful Questions:
01. You work on a team where the process for deploying a model into production starts with data scientists training different versions of models in a Kubeflow pipeline.
The workflow then stores the new model artifact into the corresponding Cloud Storage bucket. You need to build the next steps of the pipeline after the submitted model is ready to be tested and deployed in production on AI Platform.
How should you configure the architecture before deploying the model to production?
a) Deploy model in test environment -> Evaluate and test model -> Create a new AI Platform model version
b) Validate model -> Deploy model in test environment -> Create a new AI Platform model version
c) Create a new AI Platform model version -> Evaluate and test model -> Deploy model in test environment
d) Create a new AI Platform model version – > Deploy model in test environment -> Validate model
Answer: a
02. You need to write a generic test to verify whether Dense Neural Network (DNN) models automatically released by your team have a sufficient number of parameters to learn the task for which they were built.
What should you do?
a) Train the model for a few iterations, and check for NaN values.
b) Train the model with no regularization, and verify that the loss function is close to zero.
c) Train a simple linear model, and determine if the DNN model outperforms it.
d) Train the model for a few iterations, and verify that the loss is constant.
Answer: b
03. You work for a gaming company that develops and manages a popular massively multiplayer online (MMO) game.
The game’s environment is open-ended, and a large number of positions and moves can be taken by a player. Your team has developed an ML model with TensorFlow that predicts the next move of each player.
Edge deployment is not possible, but low-latency serving is required. How should you configure the deployment?
a) Use a Cloud TPU to optimize model training speed.
b) Use AI Platform Prediction with a NVIDIA GPU to make real-time predictions.
c) Use AI Platform Prediction with a high-CPU machine type to get a batch prediction for the players.
d) Use AI Platform Prediction with a high-memory machine type to get a batch prediction for the players.
Answer: b
04. You work for a large financial institution that is planning to use Dialogflow to create a chatbot for the company’s mobile app.
You have reviewed old chat logs and tagged each conversation for intent based on each customer’s stated intention for contacting customer service.
About 70% of customer inquiries are simple requests that are solved within 10 intents. The remaining 30% of inquiries require much longer and more complicated requests.
Which intents should you automate first?
a) Automate a blend of the shortest and longest intents to be representative of all intents.
b) Automate the more complicated requests first because those require more of the agents’ time.
c) Automate the 10 intents that cover 70% of the requests so that live agents can handle the more complicated requests.
d) Automate intents in places where common words such as “payment” only appear once to avoid confusing the software.
Answer: c
05. Your team is using a TensorFlow Inception-v3 CNN model pretrained on ImageNet for an image classification prediction challenge on 10,000 images. You will use AI Platform to perform the model training.
What TensorFlow distribution strategy and AI Platform training job configuration should you use to train the model and optimize for wall-clock time?
a) Default Strategy; Custom tier with a single master node and four v100 GPUs.
b) One Device Strategy; Custom tier with a single master node and four v100 GPUs.
c) One Device Strategy; Custom tier with a single master node and eight v100 GPUs.
d) MirroredStrategy; Custom tier with a single master node and four v100 GPUs.
Answer: d
Self-Assessment with GCP-PMLE Practice Test:
You can check preparation before the final exam by trying the Google GCP-PMLE practice test. The tests are handy in web-based and desktop versions. You can track your progress, with our customizable mock exams. The tests help to identify the weaker areas of preparation. They also help remove mistakes before the actual GCP-PMLE Professional Machine Learning Engineer exam. These exam simulation tests help you get rid of anxiety about the actual exam by offering a real exam-like scenario.
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