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Google Professional Machine Learning Engineer certification exam is divided into two sections: a multiple choice section and a practical section. The multiple choice section covers topics such as data preparation, feature engineering, model selection, and model evaluation. The practical section requires candidates to complete a set of tasks related to building, training, and deploying machine learning models using Google Cloud Platform.

Artificial Intelligence is significantly shaping the world as we know it, from personal virtual assistants to self-driving cars. Hence, having a Google Professional Machine Learning Engineer certification induces offers of exciting career paths with high-paid salaries. Google Professional Machine Learning Engineer certification offers knowledge and industry recognition of abilities that enable one to design, develop, productionalize, and monitor ML models and coordinate with across teams.

Google Professional Machine Learning Engineer certification exam is a challenging but rewarding experience for professionals who are looking to advance their careers in the field of machine learning. By preparing thoroughly and demonstrating your proficiency in machine learning concepts and techniques, you can achieve this prestigious certification and open up new opportunities for your career.

Google Professional Machine Learning Engineer Sample Questions (Q256-Q261):

NEW QUESTION # 256
You recently developed a wide and deep model in TensorFlow. You generated training datasets using a SQL script that preprocessed raw data in BigQuery by performing instance-level transformations of the data. You need to create a training pipeline to retrain the model on a weekly basis. The trained model will be used to generate daily recommendations. You want to minimize model development and training time. How should you develop the training pipeline?

• A. Use the TensorFlow Extended SDK to implement the pipeline Implement the preprocessing steps as part of the input_fn of the model Use the ExampleGen component with the BigQuery executor to ingest the data and the Trainer component to launch a Vertex Al training job.
• B. Use the Kubeflow Pipelines SDK to implement the pipeline. Use the dataflowpythonjobopcomponent to preprocess the data and the customTraining JobOp component to launch a Vertex Al training job.
• C. Use the Kubeflow Pipelines SDK to implement the pipeline Use the BigQueryJobop component to run the preprocessing script and the customTrainingJobop component to launch a Vertex Al training job.
• D. Use the TensorFlow Extended SDK to implement the pipeline Use the Examplegen component with the BigQuery executor to ingest the data the Transform component to preprocess the data, and the Trainer component to launch a Vertex Al training job.

Answer: D

Explanation:
* Explanation: TensorFlow Extended (TFX) is a platform for building end-to-end machine learning pipelines using TensorFlow. TFX provides a set of components that can be orchestrated using either the TFX SDK or Kubeflow Pipelines. TFX components can handle different aspects of the pipeline, such as
* data ingestion, data validation, data transformation, model training, model evaluation, model serving, and more. TFX components can also leverage other Google Cloud services, such as BigQuery, Dataflow, and Vertex AI.
* Why not A: Using the Kubeflow Pipelines SDK to implement the pipeline is a valid option, but using the BigQueryJobOp component to run the preprocessing script is not optimal. This would require writing and maintaining a separate SQL script for data transformation, which could introduce inconsistencies and errors. It would also make it harder to reuse the same preprocessing logic for both training and serving.
* Why not B: Using the Kubeflow Pipelines SDK to implement the pipeline is a valid option, but using the DataflowPythonJobOp component to preprocess the data is not optimal. This would require writing and maintaining a separate Python script for data transformation, which could introduce inconsistencies and errors. It would also make it harder to reuse the same preprocessing logic for both training and serving.
* Why not D: Using the TensorFlow Extended SDK to implement the pipeline is a valid option, but implementing the preprocessing steps as part of the input_fn of the model is not optimal. This would make the preprocessing logic tightly coupled with the model code, which could reduce modularity and flexibility. It would also make it harder to reuse the same preprocessing logic for both training and serving.

NEW QUESTION # 257
You are building an ML model to predict trends in the stock market based on a wide range of factors. While exploring the data, you notice that some features have a large range. You want to ensure that the features with the largest magnitude don't overfit the model. What should you do?

• A. Use a binning strategy to replace the magnitude of each feature with the appropriate bin number.
• B. Apply a principal component analysis (PCA) to minimize the effect of any particular feature.
• C. Normalize the data by scaling it to have values between 0 and 1.
• D. Standardize the data by transforming it with a logarithmic function.

Answer: C

Explanation:
The best option to ensure that the features with the largest magnitude don't overfit the model is to normalize the data by scaling it to have values between 0 and 1. This is also known as min-max scaling or feature scaling, and it can reduce the variance and skewness of the data, as well as improve the numerical stability and convergence of the model. Normalizing the data can also make the model less sensitive to the scale of the features, and more focused on the relative importance of each feature. Normalizing the data can be done using various methods, such as dividing each value by the maximum value, subtracting the minimum value and dividing by the range, or using the sklearn.preprocessing.MinMaxScaler function in Python.
The other options are not optimal for the following reasons:
* A. Standardizing the data by transforming it with a logarithmic function is not a good option, as it can distort the distribution and relationship of the data, and introduce bias and errors. Moreover, the logarithmic function is not defined for negative or zero values, which can limit its applicability and cause problems for the model.
* B. Applying a principal component analysis (PCA) to minimize the effect of any particular feature is not a good option, as it can reduce the interpretability and explainability of the data and the model. PCA is a dimensionality reduction technique that transforms the data into a new set of orthogonal features that capture the most variance in the data. However, these new features are not directly related to the original features, and can lose some information and meaning in the process. Moreover, PCA can be computationally expensive and complex, and may not be necessary for the problem at hand.
* C. Using a binning strategy to replace the magnitude of each feature with the appropriate bin number is not a good option, as it can lose the granularity and precision of the data, and introduce noise and outliers. Binning is a discretization technique that groups the continuous values of a feature into a finite number of bins or categories. However, this can reduce the variability and diversity of the data, and create artificial boundaries and gaps that may not reflect the true nature of the data. Moreover, binning can be arbitrary and subjective, and depend on the choice of the bin size and number.
References:
* Professional ML Engineer Exam Guide
* Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate
* Google Cloud launches machine learning engineer certification
* Feature Scaling for Machine Learning: Understanding the Difference Between Normalization vs.
Standardization
* sklearn.preprocessing.MinMaxScaler documentation
* Principal Component Analysis Explained Visually
* Binning Data in Python

NEW QUESTION # 258
You are developing a model to identify traffic signs in images extracted from videos taken from the dashboard of a vehicle. You have a dataset of 100 000 images that were cropped to show one out of ten different traffic signs. The images have been labeled accordingly for model training and are stored in a Cloud Storage bucket You need to be able to tune the model during each training run. How should you train the model?

• A. Train a model for image classification by using Vertex Al AutoML.
• B. Develop the model training code for object detection and tram a model by using Vertex Al custom training.
• C. Train a model for object detection by using Vertex Al AutoML.
• D. Develop the model training code for image classification and train a model by using Vertex Al custom training.

Answer: D

NEW QUESTION # 259
You are working with a dataset that contains customer transactions. You need to build an ML model to predict customer purchase behavior You plan to develop the model in BigQuery ML, and export it to Cloud Storage for online prediction You notice that the input data contains a few categorical features, including product category and payment method You want to deploy the model as quickly as possible. What should you do?

• A. Use the ML. ONE_HOT_ENCODER function on the categorical features, and select the encoded categorical features and non-categorical features as inputs to create your model.
• B. Use the create model statement and select the categorical and non-categorical features.
• C. Use the ML. ONE_HOT_ENCODER function on the categorical features, and select the encoded categorical features and non-categorical features as inputs to create your model.
• D. Use the transform clause with the ML. ONE_HOT_ENCODER function on the categorical features at model creation and select the categorical and non-categorical features.

Answer: D

Explanation:
The best option for building an ML model to predict customer purchase behavior in BigQuery ML is to use the transform clause with the ML.ONE_HOT_ENCODER function on the categorical features at model creation and select the categorical and non-categorical features. This option allows you to encode the categorical features as one-hot vectors, which are binary vectors that have only one non-zero element. One-hot encoding is a common technique for handling categorical features in ML models, as it can reduce the dimensionality and sparsity of the data, and avoid the ordinality problem that arises when using numerical labels for categorical values1. The transform clause is a feature of BigQuery ML that lets you apply SQL expressions to transform the input data at model creation time. The transform clause can perform feature engineering, such as one-hot encoding, on the fly, without requiring you to create and store a new table with the transformed data2. By using the transform clause with the ML.ONE_HOT_ENCODER function, you can create and train an ML model in BigQuery ML with a single SQL statement, and export it to Cloud Storage for online prediction.
The other options are not as good as option A, for the following reasons:
Option B: Using the ML.ONE_HOT_ENCODER function on the categorical features, and selecting the encoded categorical features and non-categorical features as inputs to create your model, would require more steps and storage than using the transform clause. The ML.ONE_HOT_ENCODER function is a BigQuery ML function that returns a one-hot encoded vector for a given categorical value. However, using this function alone would not apply the one-hot encoding to the input data at model creation time. You would need to create a new table with the encoded features, and use that table as the input to create your model. This would incur additional storage costs and reduce the performance of the queries.
Option C: Using the create model statement and selecting the categorical and non-categorical features, would not handle the categorical features properly and could result in a poor model performance. The create model statement is a BigQuery ML statement that creates and trains an ML model from a SQL query. However, if the input data contains categorical features, you need to encode them as one-hot vectors or use the category_count option to specify the number of categories for each feature. Otherwise, BigQuery ML would treat the categorical features as numerical values, which can introduce bias and noise into the model3.
Option D: Using the ML.ONE_HOT_ENCODER function on the categorical features, and selecting the encoded categorical features and non-categorical features as inputs to create your model, is the same as option B, and has the same drawbacks.
Reference:
Preparing for Google Cloud Certification: Machine Learning Engineer, Course 2: Data Engineering for ML on Google Cloud, Week 2: Feature Engineering Google Cloud Professional Machine Learning Engineer Exam Guide, Section 1: Architecting low-code ML solutions, 1.1 Developing ML models by using BigQuery ML Official Google Cloud Certified Professional Machine Learning Engineer Study Guide, Chapter 3: Data Engineering for ML, Section 3.2: BigQuery for ML One-hot encoding Using the TRANSFORM clause for feature engineering Creating a model ML.ONE_HOT_ENCODER function

NEW QUESTION # 260
You are developing an image recognition model using PyTorch based on ResNet50 architecture. Your code is working fine on your local laptop on a small subsample. Your full dataset has 200k labeled images You want to quickly scale your training workload while minimizing cost. You plan to use 4 V100 GPUs. What should you do? (Choose Correct Answer and Give References and Explanation)

• A. Create a Google Kubernetes Engine cluster with a node pool that has 4 V100 GPUs Prepare and submit a TFJob operator to this node pool.
• B. Configure a Compute Engine VM with all the dependencies that launches the training Train your model with Vertex Al using a custom tier that contains the required GPUs.
• C. Package your code with Setuptools. and use a pre-built container Train your model with Vertex Al using a custom tier that contains the required GPUs.
• D. Create a Vertex Al Workbench user-managed notebooks instance with 4 V100 GPUs, and use it to train your model

Answer: C

Explanation:
The best option for scaling the training workload while minimizing cost is to package the code with Setuptools, and use a pre-built container. Train the model with Vertex AI using a custom tier that contains the required GPUs. This option has the following advantages:
* It allows the code to be easily packaged and deployed, as Setuptools is a Python tool that helps to create and distribute Python packages, and pre-built containers are Docker images that contain all the dependencies and libraries needed to run the code. By packaging the code with Setuptools, and using a pre-built container, you can avoid the hassle and complexity of building and maintaining your own custom container, and ensure the compatibility and portability of your code across different environments.
* It leverages the scalability and performance of Vertex AI, which is a fully managed service that provides various tools and features for machine learning, such as training, tuning, serving, and monitoring. By training the model with Vertex AI, you can take advantage of the distributed and parallel training capabilities of Vertex AI, which can speed up the training process and improve the model quality.
Vertex AI also supports various frameworks and models, such as PyTorch and ResNet50, and allows you to use custom containers and custom tiers to customize your training configuration and resources.
* It reduces the cost and complexity of the training process, as Vertex AI allows you to use a custom tier that contains the required GPUs, which can optimize the resource utilization and allocation for your training job. By using a custom tier that contains 4 V100 GPUs, you can match the number and type of GPUs that you plan to use for your training job, and avoid paying for unnecessary or underutilized resources. Vertex AI also offers various pricing options and discounts, such as per-second billing, sustained use discounts, and preemptible VMs, that can lower the cost of the training process.
The other options are less optimal for the following reasons:
* Option A: Configuring a Compute Engine VM with all the dependencies that launches the training.
Train the model with Vertex AI using a custom tier that contains the required GPUs, introduces additional complexity and overhead. This option requires creating and managing a Compute Engine VM, which is a virtual machine that runs on Google Cloud. However, using a Compute Engine VM to launch the training may not be necessary or efficient, as it requires installing and configuring all the dependencies and libraries needed to run the code, and maintaining and updating the VM. Moreover, using a Compute Engine VM to launch the training may incur additional cost and latency, as it requires paying for the VM usage and transferring the data and the code between the VM and Vertex AI.
* Option C: Creating a Vertex AI Workbench user-managed notebooks instance with 4 V100 GPUs, and using it to train the model, introduces additional cost and risk. This option requires creating and managing a Vertex AI Workbench user-managed notebooks instance, which is a service that allows you to create and run Jupyter notebooks on Google Cloud. However, using a Vertex AI Workbench user-managed notebooks instance to train the model may not be optimal or secure, as it requires paying for the notebooks instance usage, which can be expensive and wasteful, especially if the notebooks instance is not used for other purposes. Moreover, using a Vertex AI Workbench user-managed notebooks instance to train the model may expose the model and the data to potential security or privacy issues, as the notebooks instance is not fully managed by Google Cloud, and may be accessed or modified by unauthorized users or malicious actors.
* Option D: Creating a Google Kubernetes Engine cluster with a node pool that has 4 V100 GPUs.
Prepare and submit a TFJob operator to this node pool, introduces additional complexity and cost. This option requires creating and managing a Google Kubernetes Engine cluster, which is a fully managed
* service that runs Kubernetes clusters on Google Cloud. Moreover, this option requires creating and managing a node pool that has 4 V100 GPUs, which is a group of nodes that share the same configuration and resources. Furthermore, this option requires preparing and submitting a TFJob operator to this node pool, which is a Kubernetes custom resource that defines a TensorFlow training job. However, using Google Kubernetes Engine, node pool, and TFJob operator to train the model may not be necessary or efficient, as it requires configuring and maintaining the cluster, the node pool, and the TFJob operator, and paying for their usage. Moreover, using Google Kubernetes Engine, node pool, and TFJob operator to train the model may not be compatible or scalable, as they are designed for TensorFlow models, not PyTorch models, and may not support distributed or parallel training.
References:
* [Vertex AI: Training with custom containers]
* [Vertex AI: Using custom machine types]
* [Setuptools documentation]
* [PyTorch documentation]
* [ResNet50 | PyTorch]

NEW QUESTION # 261
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