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Google Professional Machine Learning Engineer Exam (page: 5)
Google Professional Machine Learning Engineer
Updated on: 25-Aug-2025

Viewing Page 5 of 37
Professional Machine Learning Engineer PDF 283 Questions

You have written unit tests for a Kubeflow Pipeline that require custom libraries. You want to automate the execution of unit tests with each new push to your development branch in Cloud Source Repositories.
What should you do?

  1. Write a script that sequentially performs the push to your development branch and executes the unit tests on Cloud Run
  2. Using Cloud Build, set an automated trigger to execute the unit tests when changes are pushed to your development branch.
  3. Set up a Cloud Logging sink to a Pub/Sub topic that captures interactions with Cloud Source Repositories Configure a Pub/Sub trigger for Cloud Run, and execute the unit tests on Cloud Run.
  4. Set up a Cloud Logging sink to a Pub/Sub topic that captures interactions with Cloud Source Repositories. Execute the unit tests using a Cloud Function that is triggered when messages are sent to the Pub/Sub topic

Answer(s): B

Explanation:

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


Reference:

1: Cloud Build overview 2: Creating and managing build triggers 3: Building and deploying Kubeflow Pipelines using Cloud Build 4: Cloud Run documentation 5: Cloud Functions documentation



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

  1. You want the model to be resilient to overfitting.
    Which strategy should you use when retraining the model?
  2. Apply a dropout parameter of 0 2, and decrease the learning rate by a factor of 10
  3. Apply a L2 regularization parameter of 0.4, and decrease the learning rate by a factor of 10.
  4. Run a hyperparameter tuning job on Al Platform to optimize for the L2 regularization and dropout parameters
  5. Run a hyperparameter tuning job on Al Platform to optimize for the learning rate, and increase the number of neurons by a factor of 2.

Answer(s): C

Explanation:

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. Reference: 1: Generalization: Peril of Overfitting 2: Regularization for Deep Learning 3: Dropout: A

Simple Way to Prevent Neural Networks from Overfitting : [Hyperparameter tuning overview]



You are training a Resnet model on Al Platform using TPUs to visually categorize types of defects in automobile engines. You capture the training profile using the Cloud TPU profiler plugin and observe that it is highly input-bound. You want to reduce the bottleneck and speed up your model training process.
Which modifications should you make to the tf .data dataset? Choose 2 answers

  1. Use the interleave option for reading data
  2. Reduce the value of the repeat parameter
  3. Increase the buffer size for the shuffle option.
  4. Set the prefetch option equal to the training batch size
  5. Decrease the batch size argument in your transformation

Answer(s): A,D

Explanation:

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 the tf.data.Dataset.interleave method, 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 the tf.data.Dataset.prefetch method, 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.


Reference:

1: tf.data: Build TensorFlow input pipelines 2: Cloud TPU Tools in TensorBoard 3: tf.data.Dataset.interleave 4: tf.data.Dataset.prefetch : [Better performance with the tf.data API]



You work for a public transportation company and need to build a model to estimate delay times for multiple transportation routes. Predictions are served directly to users in an app in real time. Because different seasons and population increases impact the data relevance, you will retrain the model every month. You want to follow Google-recommended best practices. How should you configure the end-to-end architecture of the predictive model?

  1. Configure Kubeflow Pipelines to schedule your multi-step workflow from training to deploying your model.
  2. Use a model trained and deployed on BigQuery ML and trigger retraining with the scheduled query feature in BigQuery
  3. Write a Cloud Functions script that launches a training and deploying job on Ai Platform that is triggered by Cloud Scheduler
  4. Use Cloud Composer to programmatically schedule a Dataflow job that executes the workflow from training to deploying your model

Answer(s): A

Explanation:

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.


Reference:

1: Kubeflow Pipelines overview 2: Build a pipeline 3: Scale your machine learning training and prediction workloads 4: Export and import pipelines 5: Build components and pipelines :
[Schedule recurring pipeline runs] : [BigQuery ML overview] : [Cloud Functions documentation] :
[Dataflow documentation]



You are an ML engineer at a global shoe store. You manage the ML models for the company's website. You are asked to build a model that will recommend new products to the user based on their purchase behavior and similarity with other users.
What should you do?

  1. Build a classification model
  2. Build a knowledge-based filtering model
  3. Build a collaborative-based filtering model
  4. Build a regression model using the features as predictors

Answer(s): C

Explanation:

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.


Reference:

1: Recommender system 2: Content-based filtering 3: Collaborative filtering 4: Hybrid recommender system : [Deep learning for recommender systems] : [Knowledge-based recommender system]



You are training an LSTM-based model on Al Platform to summarize text using the following job submission script:



You want to ensure that training time is minimized without significantly compromising the accuracy of your model.
What should you do?

  1. Modify the 'epochs' parameter
  2. Modify the 'scale-tier' parameter
  3. Modify the batch size' parameter
  4. Modify the 'learning rate' parameter

Answer(s): B

Explanation:

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


Reference:

1: Using predefined machine types 2: Distributed training 3: Hyperparameter tuning overview



You are training a TensorFlow model on a structured data set with 100 billion records stored in several CSV files. You need to improve the input/output execution performance.
What should you do?

  1. Load the data into BigQuery and read the data from BigQuery.
  2. Load the data into Cloud Bigtable, and read the data from Bigtable
  3. Convert the CSV files into shards of TFRecords, and store the data in Cloud Storage
  4. Convert the CSV files into shards of TFRecords, and store the data in the Hadoop Distributed File System (HDFS)

Answer(s): C

Explanation:

The input/output execution performance of a TensorFlow model depends on how efficiently the model can read and process the data from the data source. Reading and processing data from CSV files can be slow and inefficient, especially if the data is large and distributed. Therefore, to improve the input/output execution performance, one should use a more suitable data format and storage system.
One of the best options for improving the input/output execution performance is to convert the CSV files into shards of TFRecords, and store the data in Cloud Storage. TFRecord is a binary data format that can store a sequence of serialized TensorFlow examples. TFRecord has several advantages over CSV, such as:
Faster data loading: TFRecord can be read and processed faster than CSV, as it avoids the overhead of parsing and decoding the text data. TFRecord also supports compression and checksums, which can reduce the data size and ensure data integrity1
Better performance: TFRecord can improve the performance of the model, as it allows the model to access the data in a sequential and streaming manner, and leverage the tf.data API to build efficient data pipelines. TFRecord also supports sharding and interleaving, which can increase the parallelism and throughput of the data processing2
Easier integration: TFRecord can integrate seamlessly with TensorFlow, as it is the native data format for TensorFlow. TFRecord also supports various types of data, such as images, text, audio, and video, and can store the data schema and metadata along with the data3 Cloud Storage is a scalable and reliable object storage service that can store any amount of data. Cloud Storage has several advantages over other storage systems, such as:
High availability: Cloud Storage can provide high availability and durability for the data, as it replicates the data across multiple regions and zones, and supports versioning and lifecycle management. Cloud Storage also offers various storage classes, such as Standard, Nearline, Coldline, and Archive, to meet different performance and cost requirements4 Low latency: Cloud Storage can provide low latency and high bandwidth for the data, as it supports HTTP and HTTPS protocols, and integrates with other Google Cloud services, such as AI Platform, Dataflow, and BigQuery. Cloud Storage also supports resumable uploads and downloads, and parallel composite uploads, which can improve the data transfer speed and reliability5 Easy access: Cloud Storage can provide easy access and management for the data, as it supports various tools and libraries, such as gsutil, Cloud Console, and Cloud Storage Client Libraries. Cloud Storage also supports fine-grained access control and encryption, which can ensure the data security and privacy.
The other options are not as effective or feasible. Loading the data into BigQuery and reading the data from BigQuery is not recommended, as BigQuery is mainly designed for analytical queries on large-scale data, and does not support streaming or real-time data processing. Loading the data into Cloud Bigtable and reading the data from Bigtable is not ideal, as Cloud Bigtable is mainly designed for low-latency and high-throughput key-value operations on sparse and wide tables, and does not support complex data types or schemas. Converting the CSV files into shards of TFRecords and storing the data in the Hadoop Distributed File System (HDFS) is not optimal, as HDFS is not natively supported by TensorFlow, and requires additional configuration and dependencies, such as Hadoop, Spark, or Beam.


Reference:

1: TFRecord and tf.Example 2: Better performance with the tf.data API 3: TensorFlow Data Validation 4: Cloud Storage overview 5: Performance : [How-to guides]



You have deployed multiple versions of an image classification model on Al Platform. You want to monitor the performance of the model versions overtime. How should you perform this comparison?

  1. Compare the loss performance for each model on a held-out dataset.
  2. Compare the loss performance for each model on the validation data
  3. Compare the receiver operating characteristic (ROC) curve for each model using the What-lf Tool
  4. Compare the mean average precision across the models using the Continuous Evaluation feature

Answer(s): D

Explanation:

The performance of an image classification model can be measured by various metrics, such as accuracy, precision, recall, F1-score, and mean average precision (mAP). These metrics can be calculated based on the confusion matrix, which compares the predicted labels and the true labels of the images1
One of the best ways to monitor the performance of multiple versions of an image classification model on AI Platform is to compare the mean average precision across the models using the Continuous Evaluation feature. Mean average precision is a metric that summarizes the precision and recall of a model across different confidence thresholds and classes. Mean average precision is especially useful for multi-class and multi-label image classification problems, where the model has to assign one or more labels to each image from a set of possible labels. Mean average precision can range from 0 to 1, where a higher value indicates a better performance2 Continuous Evaluation is a feature of AI Platform that allows you to automatically evaluate the performance of your deployed models using online prediction requests and responses. Continuous Evaluation can help you monitor the quality and consistency of your models over time, and detect any issues or anomalies that may affect the model performance. Continuous Evaluation can also provide various evaluation metrics and visualizations, such as accuracy, precision, recall, F1-score, ROC curve, and confusion matrix, for different types of models, such as classification, regression, and object detection3
To compare the mean average precision across the models using the Continuous Evaluation feature, you need to do the following steps:
Enable the online prediction logging for each model version that you want to evaluate. This will allow AI Platform to collect the prediction requests and responses from your models and store them in BigQuery4

Create an evaluation job for each model version that you want to evaluate. This will allow AI Platform to compare the predicted labels and the true labels of the images, and calculate the evaluation metrics, such as mean average precision. You need to specify the BigQuery table that contains the prediction logs, the data schema, the label column, and the evaluation interval. View the evaluation results for each model version on the AI Platform Models page in the Google Cloud console. You can see the mean average precision and other metrics for each model version over time, and compare them using charts and tables. You can also filter the results by different classes and confidence thresholds.
The other options are not as effective or feasible. Comparing the loss performance for each model on a held-out dataset or on the validation data is not a good idea, as the loss function may not reflect the actual performance of the model on the online prediction data, and may vary depending on the choice of the loss function and the optimization algorithm. Comparing the receiver operating characteristic (ROC) curve for each model using the What-If Tool is not possible, as the What-If Tool does not support image data or multi-class classification problems.


Reference:

1: Confusion matrix 2: Mean average precision 3: Continuous Evaluation overview 4: Configure online prediction logging : [Create an evaluation job] : [View evaluation results] : [What-If Tool overview]



Viewing Page 5 of 37



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