Google Professional Machine Learning Engineer Practice Test - Questions Answers, Page 5

Dataflowis a fully managed service for executing Apache Beam pipelines that can process streaming or batch data1.

Al Platformis a unified platform that enables you to build and run machine learning applications across Google Cloud2.

BigQueryis a serverless, highly scalable, and cost-effective cloud data warehouse designed for business agility3.

These services are suitable for building an ML model to detect anomalies in real-time sensor data, as they can handle large-scale data ingestion, preprocessing, training, serving, storage, and visualization. The other options are not as suitable because:

DataProcis a service for running Apache Spark and Apache Hadoop clusters, which are not optimized for streaming data processing4.

AutoMLis a suite of machine learning products that enables developers with limited machine learning expertise to train high-quality models specific to their business needs5. However, it does not support custom models or real-time predictions.

Cloud Bigtableis a scalable, fully managed NoSQL database service for large analytical and operational workloads. However, it is not designed for ad hoc queries or interactive analysis.

Cloud Functionsis a serverless execution environment for building and connecting cloud services. However, it is not suitable for storing or visualizing data.

Cloud Storageis a service for storing and accessing data on Google Cloud. However, it is not a data warehouse and does not support SQL queries or visualization tools.

Hyperparameter tuning is the process of finding the optimal values for the parameters of a machine learning model that affect its performance. AI Platform provides a service for hyperparameter tuning that can run multiple trials in parallel and use different search algorithms to find the best combination of hyperparameters. However, hyperparameter tuning can be time-consuming and costly, especially if the search space is large and the model training is complex. Therefore, it is important to optimize the tuning job to reduce the time and resources required.

One way to speed up the tuning job is to set the early stopping parameter to TRUE. This means that the tuning service will automatically stop trials that are unlikely to perform well based on the intermediate results. This can save time and resources by avoiding unnecessary computations for trials that are not promising. The early stopping parameter can be set in thetrainingInput.hyperparametersfield of the training job request1

Another way to speed up the tuning job is to decrease the maximum number of trials during subsequent training phases. This means that the tuning service will use fewer trials to refine the search space after the initial phase. This can reduce the time required for the tuning job to converge to the optimal solution. The maximum number of trials can be set in thetrainingInput.hyperparameters.maxTrialsfield of the training job request1

The other options are not effective ways to speed up the tuning job. Decreasing the number of parallel trials will reduce the concurrency of the tuning job and increase the overall time required. Decreasing the range of floating-point values will reduce the diversity of the search space and may miss some optimal solutions.Changing the search algorithm from Bayesian search to random search will reduce the efficiency of the tuning job and may require more trials to find the best solution1

Cloud Build is a service that executes your builds on Google Cloud Platform infrastructure.Cloud Build can import source code from Cloud Source Repositories, Cloud Storage, GitHub, or Bitbucket, execute a build to your specifications, and produce artifacts such as Docker containers or Java archives1

Cloud Build allows you to set up automated triggers that start a build when changes are pushed to a source code repository.You can configure triggers to filter the changes based on the branch, tag, or file path2

To automate the execution of unit tests for a Kubeflow Pipeline that require custom libraries, you can use Cloud Build to set an automated trigger to execute the unit tests when changes are pushed to your development branch in Cloud Source Repositories. You can specify the steps of the build in a YAML or JSON file, such as installing the custom libraries, running the unit tests, and reporting the results.You can also use Cloud Build to build and deploy the Kubeflow Pipeline components if the unit tests pass3

The other options are not recommended or feasible. Writing a script that sequentially performs the push to your development branch and executes the unit tests on Cloud Run is not a good practice, as it does not leverage the benefits of Cloud Build and its integration with Cloud Source Repositories. Setting up a Cloud Logging sink to a Pub/Sub topic that captures interactions with Cloud Source Repositories and using a Pub/Sub trigger for Cloud Run or Cloud Function to execute the unit tests is unnecessarily complex and inefficient, as it adds extra steps and latency to the process.Cloud Run and Cloud Function are also not designed for executing unit tests, as they have limitations on the memory, CPU, and execution time45

Overfitting occurs when a model tries to fit the training data so closely that it does not generalize well to new data. Overfitting can be caused by having a model that is too complex for the data, such as having too many parameters or layers.Overfitting can lead to poor performance on the validation data, which reflects how the model will perform on unseen data1

To prevent overfitting, one strategy is to use regularization techniques that penalize the complexity of the model and encourage it to learn simpler patterns. Two common regularization techniques for deep neural networks are L2 regularization and dropout. L2 regularization adds a term to the loss function that is proportional to the squared magnitude of the model's weights. This term penalizes large weights and encourages the model to use smaller weights. Dropout randomly drops out some units in the network during training, which prevents co-adaptation of features and reduces the effective number of parameters.Both L2 regularization and dropout have hyperparameters that control the strength of the regularization effect23

Another strategy to prevent overfitting is to use hyperparameter tuning, which is the process of finding the optimal values for the parameters of the model that affect its performance. Hyperparameter tuning can help find the best combination of hyperparameters that minimize the validation loss and improve the generalization ability of the model. AI Platform provides a service for hyperparameter tuning that can run multiple trials in parallel and use different search algorithms to find the best solution.

Therefore, the best strategy to use when retraining the model is to run a hyperparameter tuning job on AI Platform to optimize for the L2 regularization and dropout parameters. This will allow the model to find the optimal balance between fitting the training data and generalizing to new data. The other options are not as effective, as they either use fixed values for the regularization parameters, which may not be optimal, or they do not address the issue of overfitting at all.

The tf.data dataset is a TensorFlow API that provides a way to create and manipulate data pipelines for machine learning. The tf.data dataset allows you to apply various transformations to the data, such as reading, shuffling, batching, prefetching, and interleaving.These transformations can affect the performance and efficiency of the model training process1

One of the common performance issues in model training is input-bound, which means that the model is waiting for the input data to be ready and is not fully utilizing the computational resources. Input-bound can be caused by slow data loading, insufficient parallelism, or large data size. Input-bound can be detected by using the Cloud TPU profiler plugin, which is a tool that helps you analyze the performance of your model on Cloud TPUs.The Cloud TPU profiler plugin can show you the percentage of time that the TPU cores are idle, which indicates input-bound2

To reduce the input-bound bottleneck and speed up the model training process, you can make some modifications to the tf.data dataset. Two of the modifications that can help are:

Use the interleave option for reading data. The interleave option allows you to read data from multiple files in parallel and interleave their records. This can improve the data loading speed and reduce the idle time of the TPU cores. The interleave option can be applied by using thetf.data.Dataset.interleavemethod, which takes a function that returns a dataset for each input element, and a number of parallel calls3

Set the prefetch option equal to the training batch size. The prefetch option allows you to prefetch the next batch of data while the current batch is being processed by the model. This can reduce the latency between batches and improve the throughput of the model training. The prefetch option can be applied by using thetf.data.Dataset.prefetchmethod, which takes a buffer size argument.The buffer size should be equal to the training batch size, which is the number of examples per batch4

The other options are not effective or counterproductive. Reducing the value of the repeat parameter will reduce the number of epochs, which is the number of times the model sees the entire dataset. This can affect the model's accuracy and convergence. Increasing the buffer size for the shuffle option will increase the randomness of the data, but also increase the memory usage and the data loading time. Decreasing the batch size argument in your transformation will reduce the number of examples per batch, which can affect the model's stability and performance.

The end-to-end architecture of the predictive model for estimating delay times for multiple transportation routes should be configured using Kubeflow Pipelines. Kubeflow Pipelines is a platform for building and deploying scalable, portable, and reusable machine learning pipelines on Kubernetes. Kubeflow Pipelines allows you to orchestrate your multi-step workflow from data preparation, model training, model evaluation, model deployment, and model serving.Kubeflow Pipelines also provides a user interface for managing and tracking your pipeline runs, experiments, and artifacts1

Using Kubeflow Pipelines has several advantages for this use case:

Full automation: You can define your pipeline as a Python script that specifies the steps and dependencies of your workflow, and use the Kubeflow Pipelines SDK to compile and upload your pipeline to the Kubeflow Pipelines service.You can also use the Kubeflow Pipelines UI to create, run, and monitor your pipeline2

Scalability: You can leverage the power of Kubernetes to scale your pipeline components horizontally and vertically, and use distributed training frameworks such as TensorFlow or PyTorch to train your model on multiple nodes or GPUs3

Portability: You can package your pipeline components as Docker containers that can run on any Kubernetes cluster, and use the Kubeflow Pipelines SDK to export and import your pipeline packages across different environments4

Reusability: You can reuse your pipeline components across different pipelines, and share your components with other users through the Kubeflow Pipelines Component Store.You can also use pre-built components from the Kubeflow Pipelines library or other sources5

Schedulability: You can use the Kubeflow Pipelines UI or the Kubeflow Pipelines SDK to schedule recurring pipeline runs based on cron expressions or intervals. For example, you can schedule your pipeline to run every month to retrain your model on the latest data.

The other options are not as suitable for this use case. Using a model trained and deployed on BigQuery ML is not recommended, as BigQuery ML is mainly designed for simple and quick machine learning tasks on large-scale data, and does not support complex models or custom code. Writing a Cloud Functions script that launches a training and deploying job on AI Platform is not ideal, as Cloud Functions has limitations on the memory, CPU, and execution time, and does not provide a user interface for managing and tracking your pipeline. Using Cloud Composer to programmatically schedule a Dataflow job that executes the workflow from training to deploying your model is not optimal, as Dataflow is mainly designed for data processing and streaming analytics, and does not support model serving or monitoring.

A recommender system is a type of machine learning system that suggests relevant items to users based on their preferences and behavior.Recommender systems are widely used in e-commerce, media, and entertainment industries to enhance user experience and increase revenue1

There are different types of recommender systems that use different filtering methods to generate recommendations. The most common types are:

Content-based filtering: This method uses the features of the items and the users to find the similarity between them.For example, a content-based recommender system for movies may use the genre, director, cast, and ratings of the movies, and the preferences, demographics, and history of the users, to recommend movies that are similar to the ones the user liked before2

Collaborative filtering: This method uses the feedback and ratings of the users to find the similarity between them and the items.For example, a collaborative filtering recommender system for books may use the ratings of the users for different books, and recommend books that are liked by other users who have similar ratings to the target user3

Hybrid method: This method combines content-based and collaborative filtering methods to overcome the limitations of each method and improve the accuracy and diversity of the recommendations.For example, a hybrid recommender system for music may use both the features of the songs and the artists, and the ratings and listening habits of the users, to recommend songs that match the user's taste and preferences4

Deep learning-based: This method uses deep neural networks to learn complex and non-linear patterns from the data and generate recommendations. Deep learning-based recommender systems can handle large-scale and high-dimensional data, and incorporate various types of information, such as text, images, audio, and video. For example, a deep learning-based recommender system for fashion may use the images and descriptions of the products, and the profiles and feedback of the users, to recommend products that suit the user's style and preferences.

For the use case of building a model that will recommend new products to the user based on their purchase behavior and similarity with other users, the best option is to build a collaborative-based filtering model. This is because collaborative filtering can leverage the implicit feedback and ratings of the users to find the items that are most likely to interest them.Collaborative filtering can also help discover new products that the user may not be aware of, and increase the diversity and serendipity of the recommendations3

The other options are not as suitable for this use case. Building a classification model or a regression model using the features as predictors is not a good idea, as these models are not designed for recommendation tasks, and may not capture the preferences and behavior of the users. Building a knowledge-based filtering model is not relevant, as this method uses the explicit knowledge and requirements of the users to find the items that meet their criteria, and does not rely on the purchase behavior or similarity with other users.

The training time of a machine learning model depends on several factors, such as the complexity of the model, the size of the data, the hardware resources, and the hyperparameters. To minimize the training time without significantly compromising the accuracy of the model, one should optimize these factors as much as possible.

One of the factors that can have a significant impact on the training time is the scale-tier parameter, which specifies the type and number of machines to use for the training job on AI Platform.The scale-tier parameter can be one of the predefined values, such as BASIC, STANDARD_1, PREMIUM_1, or BASIC_GPU, or a custom value that allows you to configure the machine type, the number of workers, and the number of parameter servers1

To speed up the training of an LSTM-based model on AI Platform, one should modify the scale-tier parameter to use a higher tier or a custom configuration that provides more computational resources, such as more CPUs, GPUs, or TPUs. This can reduce the training time by increasing the parallelism and throughput of the model training.However, one should also consider the trade-off between the training time and the cost, as higher tiers or custom configurations may incur higher charges2

The other options are not as effective or may have adverse effects on the model accuracy. Modifying the epochs parameter, which specifies the number of times the model sees the entire dataset, may reduce the training time, but also affect the model's convergence and performance. Modifying the batch size parameter, which specifies the number of examples per batch, may affect the model's stability and generalization ability, as well as the memory usage and the gradient update frequency.Modifying the learning rate parameter, which specifies the step size of the gradient descent optimization, may affect the model's convergence and performance, as well as the risk of overshooting or getting stuck in local minima3

Recommendations AI is a service that allows users to build, test, and deploy personalized product recommendations for their ecommerce websites. It uses Google's deep learning models to learn from user behavior and product data, and generate high-quality recommendations that can increase revenue, click-through rate, and customer satisfaction. One of the best practices for using Recommendations AI is to choose the right recommendation type for the business objective. The ''Frequently Bought Together'' recommendation type shows products that are often purchased together with the current product, and encourages users to add more items to their shopping cart. This can increase the average order value and the revenue for each transaction. The other options are not as effective or feasible for this objective. The ''Other Products You May Like'' recommendation type shows products that are similar to the current product, and may increase the click-through rate, but not necessarily the shopping cart size. Importing the user events and then the product catalog is not a recommended order, as it may cause data inconsistency and missing recommendations. The product catalog should be imported first, and then the user events. Using placeholder values for the product catalog is not a viable option, as it will not produce meaningful recommendations or reflect the real performance of the model.Reference:

Recommendations AI documentation

Choosing a recommendation type

Importing data to Recommendations AI