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Google Professional Machine Learning Engineer Sample Questions (Q75-Q80):

NEW QUESTION # 75
You recently joined a machine learning team that will soon release a new project. As a lead on the project, you are asked to determine the production readiness of the ML components. The team has already tested features and data, model development, and infrastructure. Which additional readiness check should you recommend to the team?

A. Ensure that model performance is monitored
B. Ensure that feature expectations are captured in the schema
C. Ensure that all hyperparameters are tuned
D. Ensure that training is reproducible

Answer: D

NEW QUESTION # 76
You are designing an architecture with a serverless ML system to enrich customer support tickets with informative metadata before they are routed to a support agent. You need a set of models to predict ticket priority, predict ticket resolution time, and perform sentiment analysis to help agents make strategic decisions when they process support requests. Tickets are not expected to have any domain-specific terms or jargon.
The proposed architecture has the following flow:

Which endpoints should the Enrichment Cloud Functions call?

A. 1 = Al Platform, 2 = Al Platform, 3 = Cloud Natural Language API
B. 1 = cloud Natural Language API, 2 = Al Platform, 3 = Cloud Vision API
C. 1 = Al Platform, 2 = Al Platform, 3 = AutoML Natural Language
D. 1 = Al Platform, 2 = Al Platform, 3 = AutoML Vision

Answer: A

Explanation:
https://cloud.google.com/architecture/architecture-of-a-serverless-ml-model#architecture The architecture has the following flow:
A user writes a ticket to Firebase, which triggers a Cloud Function.
-The Cloud Function calls 3 different endpoints to enrich the ticket:
-An AI Platform endpoint, where the function can predict the priority.
-An AI Platform endpoint, where the function can predict the resolution time.
-The Natural Language API to do sentiment analysis and word salience.
-For each reply, the Cloud Function updates the Firebase real-time database.
-The Cloud Function then creates a ticket into the helpdesk platform using the RESTful API.

NEW QUESTION # 77
You are an ML engineer at a large grocery retailer with stores in multiple regions. You have been asked to create an inventory prediction model. Your models features include region, location, historical demand, and seasonal popularity. You want the algorithm to learn from new inventory data on a daily basis. Which algorithms should you use to build the model?

A. Convolutional Neural Networks (CNN)
B. Recurrent Neural Networks (RNN)
C. Classification
D. Reinforcement Learning

Answer: D

Explanation:
Reinforcement learning is a machine learning technique that enables an agent to learn from its own actions and feedback in an environment. Reinforcement learning does not require labeled data or explicit rules, but rather relies on trial and error and reward and punishment mechanisms to optimize the agent's behavior and achieve a goal. Reinforcement learning can be used to solve complex and dynamic problems that involve sequential decision making and adaptation to changing situations1.
For the use case of creating an inventory prediction model for a large grocery retailer with stores in multiple regions, reinforcement learning is a suitable algorithm to use. This is because the problem involves multiple factors that affect the inventory demand, such as region, location, historical demand, and seasonal popularity, and the inventory manager needs to make optimal decisions on how much and when to order, store, and distribute the products. Reinforcement learning can help the inventory manager to learn from the new inventory data on a daily basis, and adjust the inventory policy accordingly. Reinforcement learning can also handle the uncertainty and variability of the inventory demand, and balance the trade-off between overstocking and understocking2.
The other options are not as suitable as option B, because they are not designed to handle sequential decision making and adaptation to changing situations. Option A, classification, is a machine learning technique that assigns a label to an input based on predefined categories. Classification can be used to predict the inventory demand for a single product or a single period, but it cannot optimize the inventory policy over multiple products and periods. Option C, recurrent neural networks (RNN), are a type of neural network that can process sequential data, such as text, speech, or time series. RNN can be used to model the temporal patterns and dependencies of the inventory demand, but they cannot learn from feedback and rewards. Option D, convolutional neural networks (CNN), are a type of neural network that can process spatial data, such as images, videos, or graphs. CNN can be used to extract features and patterns from the inventory data, but they cannot optimize the inventory policy over multiple actions and states. Therefore, option B, reinforcement learning, is the best answer for this question.
Reference:
Reinforcement learning - Wikipedia
Reinforcement Learning for Inventory Optimization

NEW QUESTION # 78
You need to train a ControlNet model with Stable Diffusion XL for an image editing use case. You want to train this model as quickly as possible. Which hardware configuration should you choose to train your model?

A. Configure one a2-highgpu-1g instance with an NVIDIA A100 GPU with 80 GB of RAM. Use bfloat16 quantization during model training.
B. Configure four n1-standard-16 instances, each with one NVIDIA Tesla T4 GPU with 16 GB of RAM. Use float16 quantization during model training.
C. Configure four n1-standard-16 instances, each with one NVIDIA Tesla T4 GPU with 16 GB of RAM. Use float32 precision during model training.
D. Configure one a2-highgpu-1g instance with an NVIDIA A100 GPU with 80 GB of RAM. Use float32 precision during model training.

Answer: D

Explanation:
NVIDIA A100 GPUs are optimized for training complex models like Stable Diffusion XL. Using float32 precision ensures high model accuracy during training, whereas float16 or bfloat16 may cause lower precision in gradients, especially important for image editing. Distributing across multiple instances with T4 GPUs (Options C and D) would not speed up the process effectively due to lower power and more complex setup requirements.

NEW QUESTION # 79
You recently developed a deep learning model using Keras, and now you are experimenting with different training strategies. First, you trained the model using a single GPU, but the training process was too slow.
Next, you distributed the training across 4 GPUs using tf.distribute.MirroredStrategy (with no other changes), but you did not observe a decrease in training time. What should you do?

A. Use a TPU with tf.distribute.TPUStrategy.
B. Distribute the dataset with tf.distribute.Strategy.experimental_distribute_dataset
C. Create a custom training loop.
D. Increase the batch size.

Answer: D

Explanation:
* Option A is incorrect because distributing the dataset with tf.distribute.Strategy.
experimental_distribute_dataset is not the most effective way to decrease the training time. This method allows you to distribute your dataset across multiple devices or machines, by creating a tf.data.Dataset instance that can be iterated over in parallel1. However, this option may not improve the training time significantly, as it does not change the amount of data or computation that each device or machine has to process. Moreover, this option may introduce additional overhead or complexity, as it requires you to handle the data sharding, replication, and synchronization across the devices or machines1.
* Option B is incorrect because creating a custom training loop is not the easiest way to decrease the training time. A custom training loop is a way to implement your own logic for training your model, by using low-level TensorFlow APIs, such as tf.GradientTape, tf.Variable, or tf.function2. A custom training loop may give you more flexibility and control over the training process, but it also requires more effort and expertise, as you have to write and debug the code for each step of the training loop, such as computing the gradients, applying the optimizer, or updating the metrics2. Moreover, a custom training loop may not improve the training time significantly, as it does not change the amount of data or computation that each device or machine has to process.
* Option C is incorrect because using a TPU with tf.distribute.TPUStrategy is not a valid way to decrease the training time. A TPU (Tensor Processing Unit) is a custom hardware accelerator designed for high- performance ML workloads3. A tf.distribute.TPUStrategy is a distribution strategy that allows you to distribute your training across multiple TPUs, by creating a tf.distribute.TPUStrategy instance that can be used with high-level TensorFlow APIs, such as Keras4. However, this option is not feasible, as Vertex AI Training does not support TPUs as accelerators for custom training jobs5. Moreover, this option may require significant code changes, as TPUs have different requirements and limitations than GPUs.
* Option D is correct because increasing the batch size is the best way to decrease the training time. The batch size is a hyperparameter that determines how many samples of data are processed in each iteration of the training loop. Increasing the batch size may reduce the training time, as it reduces the number of iterations needed to train the model, and it allows each device or machine to process more data in parallel. Increasing the batch size is also easy to implement, as it only requires changing a single hyperparameter. However, increasing the batch size may also affect the convergence and the accuracy of the model, so it is important to find the optimal batch size that balances the trade-off between the training time and the model performance.
References:
* tf.distribute.Strategy.experimental_distribute_dataset
* Custom training loop
* TPU overview
* tf.distribute.TPUStrategy
* Vertex AI Training accelerators
* [TPU programming model]
* [Batch size and learning rate]
* [Keras overview]
* [tf.distribute.MirroredStrategy]
* [Vertex AI Training overview]
* [TensorFlow overview]

NEW QUESTION # 80
......

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Practical Professional-Machine-Learning-Engineer Latest Exam Duration & Leader in Qualification Exams & Hot Professional-Machine-Learning-Engineer: Google Professional Machine Learning Engineer

How much Professional Machine Learning Engineer - Google Cost

New Google Professional-Machine-Learning-Engineer Test Question & Official Professional-Machine-Learning-Engineer Study Guide

Google Professional Machine Learning Engineer Sample Questions (Q75-Q80):

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