PROFESSIONAL-MACHINE-LEARNING-ENGINEER Online Practice Questions and Answers

You are developing a Kubeflow pipeline on Google Kubernetes Engine. The first step in the pipeline is to issue a query against BigQuery. You plan to use the results of that query as the input to the next step in your pipeline. You want to achieve this in the easiest way possible. What should you do?

A. Use the BigQuery console to execute your query, and then save the query results into a new BigQuery table.

B. Write a Python script that uses the BigQuery API to execute queries against BigQuery. Execute this script as the first step in your Kubeflow pipeline.

C. Use the Kubeflow Pipelines domain-specific language to create a custom component that uses the Python BigQuery client library to execute queries.

D. Locate the Kubeflow Pipelines repository on GitHub. Find the BigQuery Query Component, copy that component's URL, and use it to load the component into your pipeline. Use the component to execute queries against BigQuery.

You are developing models to classify customer support emails. You created models with TensorFlow Estimators using small datasets on your on-premises system, but you now need to train the models using large datasets to ensure high performance. You will port your models to Google Cloud and want to minimize code refactoring and infrastructure overhead for easier migration from on-prem to cloud. What should you do?

A. Use AI Platform for distributed training.

B. Create a cluster on Dataproc for training.

C. Create a Managed Instance Group with autoscaling.

D. Use Kubeflow Pipelines to train on a Google Kubernetes Engine cluster.

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.

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.

You work as an ML engineer at a social media company, and you are developing a visual filter for users' profile photos. This requires you to train an ML model to detect bounding boxes around human faces. You want to use this filter in your company's iOS-based mobile phone application. You want to minimize code development and want the model to be optimized for inference on mobile phones. What should you do?

A. Train a model using AutoML Vision and use the "export for Core ML" option.

B. Train a model using AutoML Vision and use the "export for Coral" option.

C. Train a model using AutoML Vision and use the "export for TensorFlow.js" option.

D. Train a custom TensorFlow model and convert it to TensorFlow Lite (TFLite).

You are an ML engineer at a manufacturing company. You need to build a model that identifies defects in products based on images of the product taken at the end of the assembly line. You want your model to preprocess the images with lower computation to quickly extract features of defects in products. Which approach should you use to build the model?

A. Reinforcement learning

B. Recommender system

C. Recurrent Neural Networks (RNN)

D. Convolutional Neural Networks (CNN)

You need to build classification workflows over several structured datasets currently stored in BigQuery. Because you will be performing the classification several times, you want to complete the following steps without writing code: exploratory data analysis, feature selection, model building, training, and hyperparameter tuning and serving. What should you do?

A. Train a TensorFlow model on Vertex AI.

B. Train a classification Vertex AutoML model.

C. Run a logistic regression job on BigQuery ML.

D. Use scikit-learn in Notebooks with pandas library.

You work for a gaming company that manages a popular online multiplayer game where teams with 6 players play against each other in 5-minute battles. There are many new players every day. You need to build a model that automatically assigns available players to teams in real time. User research indicates that the game is more enjoyable when battles have players with similar skill levels. Which business metrics should you track to measure your model's performance?

A. Average time players wait before being assigned to a team

B. Precision and recall of assigning players to teams based on their predicted versus actual ability

C. User engagement as measured by the number of battles played daily per user

D. Rate of return as measured by additional revenue generated minus the cost of developing a new model

You have trained a DNN regressor with TensorFlow to predict housing prices using a set of predictive features. Your default precision is tf.float64, and you use a standard TensorFlow estimator:

Your model performs well, but just before deploying it to production, you discover that your current serving latency is 10ms @ 90 percentile and you currently serve on CPUs. Your production requirements expect a model latency of 8ms @ 90 percentile. You're willing to accept a small decrease in performance in order to reach the latency requirement.

Therefore your plan is to improve latency while evaluating how much the model's prediction decreases. What should you first try to quickly lower the serving latency?

A. Switch from CPU to GPU serving.

B. Apply quantization to your SavedModel by reducing the floating point precision to tf.float16.

C. Increase the dropout rate to 0.8 and retrain your model.

D. Increase the dropout rate to 0.8 in _PREDICT mode by adjusting the TensorFlow Serving parameters.

You are developing an ML model using a dataset with categorical input variables. You have randomly split half of the data into training and test sets. After applying one-hot encoding on the categorical variables in the training set, you discover that one categorical variable is missing from the test set. What should you do?

A. Use sparse representation in the test set.

B. Randomly redistribute the data, with 70% for the training set and 30% for the test set

C. Apply one-hot encoding on the categorical variables in the test data

D. Collect more data representing all categories