GCP Machine Learning Certification Dumps

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This article gives you a brief about the GCP Machine Learning Certification, including exam insights, preparation strategies, and sample GCP Machine Learning Certification exam questions.

1. What Is the Google Cloud Machine Learning Certification?

The Google Cloud Machine Learning Engineer Certification validates your ability to design, build, and deploy ML models on GCP. It’s designed for professionals who:

  • Develop ML pipelines using TensorFlow, Vertex AI, and BigQuery.
  • Optimize models for scalability and performance.
  • Implement MLOps practices for continuous integration and delivery.

This certification is ideal for roles like ML engineers, data engineers, and cloud architects. Google’s rigorous exam ensures holders possess hands-on experience and theoretical knowledge, making it a trusted credential globally.

2. Why Pursue the Google ML Certification?

  1. High Demand for GCP Skills
    • Google Cloud holds 10% of the cloud market, with enterprises like Spotify and PayPal relying on GCP for ML solutions. Certified professionals are prioritized in hiring for roles with salaries averaging $150,000+ (source: Glassdoor).
  2. Comprehensive Skill Validation
    • The exam tests real-world skills, including:
      • Data preprocessing with Apache Beam and Dataflow.
      • Model training using AutoML and custom TensorFlow models.
      • Deployment on the AI Platform and monitoring with Cloud Logging.
  3. Industry Recognition
    • Google certifications are respected by employers like Deloitte, HSBC, and NASA, enhancing your credibility in AI/ML projects.

3. How to Prepare for the GCP Machine Learning Exam

  • Step 1: Master Core Concepts
    • Study Topics:
      • ML fundamentals (supervised vs. unsupervised learning).
      • GCP services (Vertex AI, BigQuery ML, TensorFlow Extended).
      • Model optimization (hyperparameter tuning, distributed training).
  • Recommended Resources:
    • Coursera’s “Preparing for Google Cloud Machine Learning Engineer” course.
    • Official Google Cloud documentation and whitepapers.
  • Step 2: Hands-On Practice
    • Complete labs on Google Cloud Skills Boost to build pipelines and deploy models.
    • Experiment with real datasets in Kaggle competitions using GCP tools.
  • Step 3: Mock Exams
    • Take practice tests to identify gaps.

4. Exam Details: What to Expect

  • Format: 60+ multiple-choice and scenario-based questions.
  • Duration: 2 hours.
  • Cost: $200 (discounts available for Google Cloud partners).
  • Registration: Schedule via Webassessor.

4.1 Pro Tips for Success:

  • Focus on Vertex AI’s end-to-end workflow.
  • Review case studies on fraud detection and recommendation systems.
GCP Machine Learning Certification

GCP Machine Learning Certification Dumps

Q1. Your team is building an application for a global bank that will be used by millions of customers. You built a forecasting model that predicts customers’ account balances 3 days in the future. Your team will use the results in a new feature that will notify users when their account balance is likely to drop below $25. How should you serve your predictions?

  1. Create a Pub/Sub topic for each user. Deploy a Cloud Function that sends a notification when your model predicts that a user’s account balance will drop below the $25 threshold.
  2. Create a Pub/Sub topic for each user. Deploy an application on the App Engine standard environment that sends a notification when your model predicts that a user’s account balance will drop below the $25 threshold.
  3. Build a notification system on Firebase. Register each user with a user ID on the Firebase Cloud Messaging server, which sends a notification when the average of all account balance predictions drops below the $25 threshold.
  4. Build a notification system on Firebase. Register each user with a user ID on the Firebase Cloud Messaging server, which sends a notification when your model predicts that a user’s account balance will drop below the $25 threshold.✔️

Links: Firebase Cloud Messaging (FCM)

Q2. You work for an advertising company and want to understand the effectiveness of your company’s latest advertising campaign. You have streamed 500 MB of campaign data into BigQuery. You want to query the table and then manipulate the results of that query with a pandas dataframe in an AI Platform notebook. What should you do?

  1. Use AI Platform Notebooks’ BigQuery cell magic to query the data, and ingest the results as a pandas dataframe.✔️
  2. Export your table as a CSV file from BigQuery to Google Drive, and use the Google Drive API to ingest the file into your notebook instance.
  3. Download your table from BigQuery as a local CSV file, and upload it to your AI Platform notebook instance. Use pandas.read_csv to ingest he file as a pandas dataframe.
  4. From a bash cell in your AI Platform notebook, use the bq extract command to export the table as a CSV file to Cloud Storage, and then use gsutil cp to copy the data into the notebook. Use pandas.read_csv to ingest the file as a pandas dataframe.

Links: BigQuery cell magic to query the data

Q3. You are an ML engineer at a global car manufacturer. You need to build an ML model to predict car sales in different cities around the world. Which features or feature crosses should you use to train city-specific relationships between car type and the number of sales?

  1. Three individual features: binned latitude, binned longitude, and one-hot encoded car type.
  2. One feature is obtained as an element-wise product between latitude, longitude, and car type.
  3. One feature obtained as an element-wise product between binned latitude, binned longitude, and one-hot encoded car type.✔️
  4. Two feature crosses as an element-wise product: the first between binned latitude and one-hot encoded car type, and the second between binned longitude and one-hot encoded car type.

Links: Feature cross

Q4. You work for a large technology company that wants to modernize its contact center. You have been asked to develop a solution to classify incoming calls by product so that requests can be more quickly routed to the correct support team. You have already transcribed the calls using the Speech-to-Text API. You want to minimize data preprocessing and development time. How should you build the model?

  1. Use the AI Platform Training built-in algorithms to create a custom model.
  2. Use AutoML Natural Language to extract custom entities for classification.✔️
  3. Use the Cloud Natural Language API to extract custom entities for classification.
  4. Build a custom model to identify the product keywords from the transcribed calls, and then run the keywords through a classification algorithm.

Links: AutoML Natural Language AI

Q5. You are training a TensorFlow model on a structured dataset with 10 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).

Links: Anatomy of a BigQuery Query

Q6. As the lead ML Engineer for your company, you are responsible for building ML models to digitize scanned customer forms. You have developed a TensorFlow model that converts the scanned images into text and stores them in Cloud Storage. You need to use your ML model on the aggregated data collected at the end of each day with minimal manual intervention. What should you do?

  1. Use the batch prediction functionality of AI Platform.✔️
  2. Create a serving pipeline in Compute Engine for prediction.
  3. Use Cloud Functions for prediction each time a new data point is ingested.
  4. Deploy the model on the AI Platform and create a version of it for online inference.

Links: AI Platform Batch Predictions (refer output path)

Q7. You recently joined an enterprise-scale company that has thousands of datasets. You know that there are accurate descriptions for each table in BigQuery, and you are searching for the proper BigQuery table to use for a model you are building on AI Platform. How should you find the data that you need?

  1. Use Data Catalog to search the BigQuery datasets by using keywords in the table description.✔️
  2. Tag each of your models and version resources on AI Platform with the name of the BigQuery table that was used for training.
  3. Maintain a lookup table in BigQuery that maps the table descriptions to the table ID. Query the lookup table to find the correct table ID for the data that you need.
  4. Execute a query in BigQuery to retrieve all the existing table names in your project using the INFORMATION_SCHEMA metadata tables that are native to BigQuery. Use the result to find the table that you need.

Links: Data Catalog overview

Q8. You started working on a classification problem with time-series data and achieved an area under the receiver operating characteristic curve (AUC ROC) value of 99% for training data after just a few experiments. You haven’t explored using any sophisticated algorithms or spent any time on hyperparameter tuning. What should your next step be to identify and fix the problem?

  1. Address the model overfitting by using a less complex algorithm.✔️
  2. Address data leakage by applying nested cross-validation during model training.
  3. Address data leakage by removing features highly correlated with the target value.
  4. Address the model overfitting by tuning the hyperparameters to reduce the AUC ROC value.

Links: Overfitting explained

Q9. You work for an online travel agency that also sells advertising placements on its website to other companies. You have been asked to predict the most relevant web banner that a user should see next. Security is important to your company. The model latency requirements are 300ms@p99, the inventory is thousands of web banners, and your exploratory analysis has shown that navigation context is a good predictor. You want to implement the simplest solution. How should you configure the prediction pipeline?

  1. Embed the client on the website, and then deploy the model on AI Platform Prediction.✔️
  2. Embed the client on the website, deploy the gateway on App Engine, and then deploy the model on AI Platform Prediction.
  3. Embed the client on the website, deploy the gateway on App Engine, deploy the database on Cloud Bigtable for writing and for reading the user’s navigation context, and then deploy the model on AI Platform Prediction.
  4. Embed the client on the website, deploy the gateway on App Engine, deploy the database on Memorystore for writing and for reading the user’s navigation context, and then deploy the model on Google Kubernetes Engine.

Links: AI Platform online prediction refer REST API

Q10. Your team is building a convolutional neural network (CNN)-based architecture from scratch. The preliminary experiments running on your on-premises CPU-only infrastructure were encouraging, but they have slow convergence. You have been asked to speed up model training to reduce time-to-market. You want to experiment with virtual machines (VMs) on Google Cloud to leverage more powerful hardware. Your code does not include any manual device placement and has not been wrapped in Estimator model-level abstraction. Which environment should you train your model on?

  1. AVM on Compute Engine and 1 TPU with all dependencies installed manually.
  2. AVM on Compute Engine and 8 GPUs with all dependencies installed manually.
  3. A Deep Learning VM with an n1-standard-2 machine and 1 GPU with all libraries pre-installed.
  4. A Deep Learning VM with more powerful CPU e2-highcpu-16 machines, with all libraries pre-installed.✔️

Links: GPU manual device placement

Q11. You work on a growing team of more than 50 data scientists who all use the AI Platform. You are designing a strategy to organize your jobs, models, and versions in a clean and scalable way. Which strategy should you choose?

  1. Set up restrictive IAM permissions on the AI Platform notebooks so that only a single user or group can access a given instance.
  2. Separate each data scientist’s work into a different project to ensure that the jobs, models, and versions created by each data scientist are accessible only to that user.
  3. Use labels to organize resources into descriptive categories. Apply a label to each created resource so that users can filter the results by label when viewing or monitoring the resources.✔️
  4. Set up a BigQuery sink for Cloud Logging logs that is appropriately filtered to capture information about AI Platform resource usage. In BigQuery, create a SQL view that maps users to the resources they are using

Links: Labelling resources on AI-Platform

Q12. You are training a deep learning model for semantic image segmentation with reduced training time. While using a Deep Learning VM Image, you receive the following error: The resource ‘projects/deeplearning-platform/zones/europe-west4c/acceleratorTypes/nvidia-tesla-k80’ was not found. What should you do?

  1. Ensure that you have a GPU quota in the selected region.
  2. Ensure that the required GPU is available in the selected region.✔️
  3. Ensure that you have a preemptible GPU quota in the selected region.
  4. Ensure that the selected GPU has enough GPU memory for the workload.

Links: Troubleshooting Deep learning VMs

Q13. Your team is working on an NLP research project to predict the political affiliation of authors based on articles they have written. You have a large training dataset that is structured like this: You followed the standard 80%-10%-10% data distribution across the training, testing, and evaluation subsets. How should you distribute the training examples across the train-test-eval subsets while maintaining the 8010-10 proportion?

  1. Distribute texts randomly across the train-test-eval subsets: Train set: [TextA1, TextB2, …]
    Test set: [TextA2, TextC1, TextD2, …] Eval set: [TextB1, TextC2, TextD1, …]
  2. Distribute authors randomly across the train-test-eval subsets:
    (*) Train set: [TextA1, TextA2, TextD1, TextD2, …]
    Test set: [TextB1, TextB2, …] Eval set: [TexC1,TextC2 …]✔️
  3. Distribute sentences randomly across the train-test-eval subsets:
    Train set: [SentenceA11, SentenceA21, SentenceB11, SentenceB21, SentenceC11, SentenceD21 …]
    Test set: [SentenceA12, SentenceA22, SentenceB12, SentenceC22, SentenceC12, SentenceD22 …] Eval set: [SentenceA13, SentenceA23, SentenceB13, SentenceC23, SentenceC13, SentenceD31 …]
  4. Distribute paragraphs of text (i.e., chunks of consecutive sentences)
    across the train-test-eval subsets: Train set: [SentenceA11, SentenceA12, SentenceD11, SentenceD12 …]
    Test set: [SentenceA13, SentenceB13, SentenceB21, SentenceD23, SentenceC12, SentenceD13 …] Eval set: [SentenceA11, SentenceA22, SentenceB13, SentenceD22, SentenceC23, SentenceD11 …]

Links: Splitting medical dataset

Q14. Your team has been tasked with creating an ML solution in Google Cloud to classify support requests for one of your platforms. You analyzed the requirements and decided to use TensorFlow to build the classifier so that you have full control of the model’s code, serving, and deployment. You will use Kubeflow pipelines for the ML platform. To save time, you want to build on existing resources and use managed services instead of building a completely new model. How should you build the classifier?

  1. Use the Natural Language API to classify support requests.
  2. Use AutoML Natural Language to build the support requests classifier.
  3. Use an established text classification model on the AI Platform to perform transfer learning.
  4. Use an established text classification model on the AI Platform as-is to classify support requests.✔️

Links: AI Platform BERT

Q15. 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?

  1. Ensure that training is reproducible.
  2. Ensure that all hyperparameters are tuned.
  3. Ensure that model performance is monitored.✔️
  4. Ensure that feature expectations are captured in the schema.

Links: A Rubric for ML Production Readiness and Technical Debt Reduction (Paper), Refer section V: Monitoring

Q16. You work for a credit card company and have been asked to create a custom fraud detection model based on historical data using AutoML Tables. You need to prioritize the detection of fraudulent transactions while minimizing false positives. Which optimization objective should you use when training the model?

  1. An optimization objective that minimizes Log loss
  2. An optimization objective that maximizes the Precision at a Recall value of 0.50✔️
  3. An optimization objective that maximizes the area under the precision-recall curve (AUC PR) value
  4. An optimization objective that maximizes the area under the receiver operating characteristic curve (AUC ROC) value

Links: Precision-recall

  1. The model predicts videos as popular if the user who uploads them has over 10,000 likes.
  2. The model predicts 97.5% of the most popular clickbait videos measured by number of clicks.
  3. The model predicts 95% of the most popular videos measured by watch time within 30 days of being uploaded.✔️
  4. The Pearson correlation coefficient between the log-transformed number of views after 7 days and 30 days after publication is equal to 0.

Q18. You are working on a Neural Network-based project. The dataset provided to you has columns with different ranges. While preparing the data for model training, you discover that gradient optimization is having difficulty moving weights to a good solution. What should you do?

  1. Use feature construction to combine the strongest features.
  2. Use the representation transformation (normalization) technique.✔️
  3. Improve the data cleaning step by removing features with missing values.
  4. Change the partitioning step to reduce the dimension of the test set and have a larger training set.

Links: Scaling for neural networks

Q19. Your data science team needs to rapidly experiment with various features, model architectures, and hyperparameters. They need to track the accuracy metrics for various experiments and use an API to query the metrics over time. What should they use to track and report their experiments while minimizing manual effort?

  1. Use Kubeflow Pipelines to execute the experiments. Export the metrics file, and query the results using the Kubeflow Pipelines API.✔️
  2. Use AI Platform Training to execute the experiments. Write the accuracy metrics to BigQuery, and query the results using the BigQuery API.
  3. Use AI Platform Training to execute the experiments. Write the accuracy metrics to Cloud Monitoring, and query the results using the Monitoring API.
  4. Use AI Platform Notebooks to execute the experiments. Collect the results in a shared Google Sheets file, and query the results using the Google Sheets API.

Links: Kubeflow metrics

Q20. You work for a bank and are building a random forest model for fraud detection. You have a dataset that includes transactions, of which 1% are identified as fraudulent. Which data transformation strategy would likely improve the performance of your classifier?

  1. Write your data in TFRecords.
  2. Z-normalize all the numeric features.
  3. Oversample the fraudulent transaction 10 times.✔️
  4. Use one-hot encoding on all categorical features.

Links: Credit card fraud detection with Data imbalance

Q21. 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 the 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?

  1. Default Strategy: Custom tier with a single master node and four v100 GPUs.
  2. One Device Strategy: Custom tier with a single master node and four v100 GPUs.
  3. One Device Strategy: Custom tier with a single master node and eight v100 GPUs.
  4. MirroredStrategy; Custom tier with a single master node and four v100 GPUs.✔️

Links

Q22. You work for a manufacturing company that owns a high-value machine that has several machine settings and multiple sensors. A history of the machine’s hourly sensor readings and known failure event data is stored in BigQuery. You need to predict if the machine will fail within the next 3 days in order to schedule maintenance before the machine fails. Which data preparation and model training steps should you take?

  1. Data preparation: Daily max value feature engineering; Model training: AutoML classification with BQML
  2. Data preparation: Daily min value feature engineering; Model training: Logistic regression with BQML and AUTO_CLASS_WEIGHTS set to True
  3. Data preparation: Rolling average feature engineering; Model training: Logistic regression with BQML and AUTO_CLASS_WEIGHTS set to False
  4. Data preparation: Rolling average feature engineering; Model training: Logistic regression with BQML and AUTO_CLASS_WEIGHTS set to True✔️

Links

Q23. You need to build an object detection model for a small startup company to identify if and where the company’s logo appears in an image. You were given a large repository of images, some with logos and some without. These images are not yet labeled. You need to label these pictures and then train and deploy the model. What should you do?

  1. Use Google Cloud’s Data Labelling Service to label your data. Use AutoML Object Detection to train and deploy the model.✔️
  2. Use Vision API to detect and identify logos in pictures and use them as labels. Usethe AI Platform to build and train a convolutional neural network.
  3. Create two folders: one where the logo appears and one where it doesn’t. Manually place images in each folder. Use the AI Platform to build and train a convolutional neural network.
  4. Create two folders: one where the logo appears and one where it doesn’t. Manually place images in each folder. Use the AI Platform to build and train a real-time object detection model.

Links

Q24. You are developing an application on Google Cloud that will automatically generate subject labels for users’ blog posts. You are under competitive pressure to add this feature quickly, and you have no additional developer resources. No one on your team has experience with machine learning. What should you do?

  1. Call the Cloud Natural Language API from your application. Process the generated Entity Analysis as labels.✔️
  2. Call the Cloud Natural Language API from your application. Process the generated Sentiment Analysis as labels.
  3. Build and train a text classification model using TensorFlow. Deploy the model using AI Platform Prediction. Call the model from your application and process the results as labels.
  4. Build and train a text classification model using TensorFlow. Deploy the model using a Kubernetes Engine cluster. Call the model from your application and process the results as labels.

Q25. You are developing an application on Google Cloud that will label famous landmarks in users’ photos. You are under competitive pressure to develop a predictive model quickly. You need to keep service costs low. What should you do?

  1. Build an application that calls the Cloud Vision API. Inspect the generated MID values to supply the image labels.
  2. Build an application that calls the Cloud Vision API. Pass landmark location as base64-encoded strings.✔️
  3. Build and train a classification model with TensorFlow. Deploy the model using AI Platform Prediction. Pass client image locations as base64-encoded strings.
  4. Build and train a classification model with TensorFlow. Deploy the model using AI Platform Prediction. Inspect the generated MID values to supply the image labels.

Links

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