Google Google Certifications PROFESSIONAL-MACHINE-LEARNING-ENGINEER Questions & Answers

• Question 151:

  You work for an online retail company that is creating a visual search engine. You have set up an end-to-end ML pipeline on Google Cloud to classify whether an image contains your company's product. Expecting the release of new products in the near future, you configured a retraining functionality in the pipeline so that new data can be fed into your ML models. You also want to use AI Platform's continuous evaluation service to ensure that the models have high accuracy on your test dataset. What should you do?

  A. Keep the original test dataset unchanged even if newer products are incorporated into retraining.

  B. Extend your test dataset with images of the newer products when they are introduced to retraining.

  C. Replace your test dataset with images of the newer products when they are introduced to retraining.

  D. Update your test dataset with images of the newer products when your evaluation metrics drop below a pre-decided threshold.

  Correct Answer: B

  You need to correctly classify newer products, so you need the new training data ==> A is wrong;

  You need to keep doing a good job on older dataset, you can't just ignore it ==> C is wrong;

  You know when you are introducing new products, there is no need to wait for a drop in preformaces ==> D is wrong;

  B is correct

• Question 152:

  You were asked to investigate failures of a production line component based on sensor readings. After receiving the dataset, you discover that less than 1% of the readings are positive examples representing failure incidents. You have tried to train several classification models, but none of them converge. How should you resolve the class imbalance problem?

  A. Use the class distribution to generate 10% positive examples.

  B. Use a convolutional neural network with max pooling and softmax activation.

  C. Downsample the data with upweighting to create a sample with 10% positive examples.

  D. Remove negative examples until the numbers of positive and negative examples are equal.

  Correct Answer: C

  https://developers.google.com/machine-learning/data-prep/construct/sampling-splitting/imbalanced-data#downsampling-and-upweighting

  -less than 1% of the readings are positive

  -none of them converge.

  Downsampling (in this context) means training on a disproportionately low subset of the majority class examples.

• Question 153:

  You want to rebuild your ML pipeline for structured data on Google Cloud. You are using PySpark to conduct data transformations at scale, but your pipelines are taking over 12 hours to run. To speed up development and pipeline run time, you want to use a serverless tool and SQL syntax. You have already moved your raw data into Cloud Storage. How should you build the pipeline on Google Cloud while meeting the speed and processing requirements?

  A. Use Data Fusion's GUI to build the transformation pipelines, and then write the data into BigQuery.

  B. Convert your PySpark into SparkSQL queries to transform the data, and then run your pipeline on Dataproc to write the data into BigQuery.

  C. Ingest your data into Cloud SQL, convert your PySpark commands into SQL queries to transform the data, and then use federated queries from BigQuery for machine learning.

  D. Ingest your data into BigQuery using BigQuery Load, convert your PySpark commands into BigQuery SQL queries to transform the data, and then write the transformations to a new table.

  Correct Answer: D

• Question 154:

  You manage a team of data scientists who use a cloud-based backend system to submit training jobs. This system has become very difficult to administer, and you want to use a managed service instead. The data scientists you work with use many different frameworks, including Keras, PyTorch, theano, Scikit-learn, and custom libraries. What should you do?

  A. Use the AI Platform custom containers feature to receive training jobs using any framework.

  B. Configure Kubeflow to run on Google Kubernetes Engine and receive training jobs through TF Job.

  C. Create a library of VM images on Compute Engine, and publish these images on a centralized repository.

  D. Set up Slurm workload manager to receive jobs that can be scheduled to run on your cloud infrastructure.

  Correct Answer: A

  https://cloud.google.com/ai-platform/training/docs/getting-started-pytorch

• Question 155:

  You are building an ML model to detect anomalies in real-time sensor data. You will use Pub/Sub to handle incoming requests. You want to store the results for analytics and visualization. How should you configure the pipeline?

  A. 1 = Dataflow, 2 = AI Platform, 3 = BigQuery

  B. 1 = DataProc, 2 = AutoML, 3 = Cloud Bigtable

  C. 1 = BigQuery, 2 = AutoML, 3 = Cloud Functions

  D. 1 = BigQuery, 2 = AI Platform, 3 = Cloud Storage

  Correct Answer: A

• Question 156:

  Your organization wants to make its internal shuttle service route more efficient. The shuttles currently stop at all pick-up points across the city every 30 minutes between 7 am and 10 am. The development team has already built an application on Google Kubernetes Engine that requires users to confirm their presence and shuttle station one day in advance. What approach should you take?

  A. 1. Build a tree-based regression model that predicts how many passengers will be picked up at each shuttle station.

  2. Dispatch an appropriately sized shuttle and provide the map with the required stops based on the prediction.

  B. 1. Build a tree-based classification model that predicts whether the shuttle should pick up passengers at each shuttle station.

  2. Dispatch an available shuttle and provide the map with the required stops based on the prediction.

  C. 1. Define the optimal route as the shortest route that passes by all shuttle stations with confirmed attendance at the given time under capacity constraints.

  2. Dispatch an appropriately sized shuttle and indicate the required stops on the map.

  D. 1. Build a reinforcement learning model with tree-based classification models that predict the presence of passengers at shuttle stops as agents and a reward function around a distance-based metric.

  2. Dispatch an appropriately sized shuttle and provide the map with the required stops based on the simulated outcome.

  Correct Answer: C

• Question 157:

  You are a data scientist at an industrial equipment manufacturing company. You are developing a regression model to estimate the power consumption in the company's manufacturing plants based on sensor data collected from all of the plants. The sensors collect tens of millions of records every day. You need to schedule daily training runs for your model that use all the data collected up to the current date. You want your model to scale smoothly and require minimal development work. What should you do?

  A. Develop a custom TensorFlow regression model, and optimize it using Vertex AI Training.

  B. Develop a regression model using BigQuery ML.

  C. Develop a custom scikit-learn regression model, and optimize it using Vertex AI Training.

  D. Develop a custom PyTorch regression model, and optimize it using Vertex AI Training.

  Correct Answer: B

  Minimal development effort => BigQueryML

• Question 158:

  Your organization manages an online message board. A few months ago, you discovered an increase in toxic language and bullying on the message board. You deployed an automated text classifier that flags certain comments as toxic or harmful. Now some users are reporting that benign comments referencing their religion are being misclassified as abusive. Upon further inspection, you find that your classifier's false positive rate is higher for comments that reference certain underrepresented religious groups. Your team has a limited budget and is already overextended. What should you do?

  A. Add synthetic training data where those phrases are used in non-toxic ways.

  B. Remove the model and replace it with human moderation.

  C. Replace your model with a different text classifier.

  D. Raise the threshold for comments to be considered toxic or harmful.

  Correct Answer: D

• Question 159:

  You work for a magazine distributor and need to build a model that predicts which customers will renew their subscriptions for the upcoming year. Using your company's historical data as your training set, you created a TensorFlow model and deployed it to Vertex AI. You need to determine which customer attribute has the most predictive power for each prediction served by the model. What should you do?

  A. Stream prediction results to BigQuery. Use BigQuery's CORR(X1, X2) function to calculate the Pearson correlation coefficient between each feature and the target variable.

  B. Use Vertex Explainable AI. Submit each prediction request with the explain' keyword to retrieve feature attributions using the sampled Shapley method.

  C. Use Vertex AI Workbench user-managed notebooks to perform a Lasso regression analysis on your model, which will eliminate features that do not provide a strong signal.

  D. Use the What-If tool in Google Cloud to determine how your model will perform when individual features are excluded. Rank the feature importance in order of those that caused the most significant performance drop when removed from the model.

  Correct Answer: B

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• Question 160:

  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 Vertex AI Workbench user-managed notebooks with pandas library.

  Correct Answer: B