Google Professional Machine Learning Engineer Practice Exam

Notes: Hi all, Google Professional Machine Learning Engineer Practice Exam will familiarize you with types of questions you may encounter on the certification exam and help you determine your readiness or if you need more preparation and/or experience. Successful completion of the practice exam does not guarantee you will pass the certification exam as the actual exam is longer and covers a wider range of topics.
We highly recommend you should take Google Professional Machine Learning Engineer Actual Exam Version because it include actual exam questions and highlighted answers are collected and verified in our exam. It will help you pass exam in easier way.

1. Your company manages a video sharing website where users can watch and upload videos. You need to create an ML model to predict which newly uploaded videos will be the most popular so that those videos can be prioritized on your company’s website. Which result should you use to determine whether the model is successful?

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

2. 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?

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

3. 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?

A. Use Kubeflow Pipelines to execute the experiments. Export the metrics file, and query the results using the Kubeflow Pipelines API.
B. Use AI Platform Training to execute the experiments. Write the accuracy metrics to BigQuery, and query the results using the BigQuery API.
C. Use AI Platform Training to execute the experiments. Write the accuracy metrics to Cloud Monitoring, and query the results using the Monitoring API.
D. 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.

4. 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?

A. Write your data in TFRecords.
B. Z-normalize all the numeric features.
C. Oversample the fraudulent transaction 10 times.
D. Use one-hot encoding on all categorical features.

5. Your team is working on an NLP research project to predict 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 80-10-10 proportion?
A. Distribute texts randomly across the train-test-eval subsets: Train set: [TextA1, TextB2, …] Test set: [TextA2, TextC1, TextD2, …] Eval set: [TextB1, TextC2, TextD1, …] B. Distribute authors randomly across the train-test-eval subsets: (*) Train set: [TextA1, TextA2, TextD1, TextD2, …] Test set: [TextB1, TextB2, …] Eval set: [TexC1,TextC2 …] C. 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 …] D. Distribute paragraphs of texts (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 …]

6. 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?

A. Use the Natural Language API to classify support requests.
B. Use AutoML Natural Language to build the support requests classifier.
C. Use an established text classification model on AI Platform to perform transfer learning.
D. Use an established text classification model on AI Platform as-is to classify support requests.

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

8. 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 detection of fraudulent transactions while minimizing false positives. Which optimization objective should you use when training the model?

A. An optimization objective that minimizes Log loss
B. An optimization objective that maximizes the Precision at a Recall value of 0.50
C. An optimization objective that maximizes the area under the precision-recall curve (AUC PR) value
D. An optimization objective that maximizes the area under the receiver operating characteristic curve (AUC ROC) value

9. 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?

A. Embed the client on the website, and then deploy the model on AI Platform Prediction.
B. Embed the client on the website, deploy the gateway on App Engine, and then deploy the model on AI Platform Prediction.
C. 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.
D. 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.

10. 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 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?

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