Google Professional-Machine-Learning-Engineer Exam Dumps

Google Professional Machine Learning Engineer

Google Professional-Machine-Learning-Engineer Sample Questions

You want to train an AutoML model to predict house prices by using a small public dataset stored in BigQuery. You need to prepare the data and want to use the simplest most efficient approach. What should you do? 

A. Write a query that preprocesses the data by using BigQuery and creates a new table Create a Vertex Al managed dataset with the new table as the data source. 
B. Use Dataflow to preprocess the data Write the output in TFRecord format to a Cloud Storage bucket. 
C. Write a query that preprocesses the data by using BigQuery Export the query results as CSV files and use those files to create a Vertex Al managed dataset.  
D. Use a Vertex Al Workbench notebook instance to preprocess the data by using the pandas library Export the data as CSV files, and use those files to create a Vertex Al managed dataset. 

You are training an ML model using data stored in BigQuery that contains several values that are considered Personally Identifiable Information (Pll). You need to reduce the sensitivity of the dataset before training your model. Every column is critical to your model. How should you proceed?  

A. Using Dataflow, ingest the columns with sensitive data from BigQuery, and then randomize the values in each sensitive column. 
B. Use the Cloud Data Loss Prevention (DLP) API to scan for sensitive data, and use Dataflow with the DLP API to encrypt sensitive values with Format Preserving Encryption 
C. Use the Cloud Data Loss Prevention (DLP) API to scan for sensitive data, and use Dataflow to replace all sensitive data by using the encryption algorithm AES-256 with a salt. 
D. Before training, use BigQuery to select only the columns that do not contain sensitive data Create an authorized view of the data so that sensitive values cannot be accessed by unauthorized individuals.  

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; estimator = tf.estimator.DNNRegressor( feature_columns=[YOUR_LIST_OF_FEATURES], hidden_units-[1024, 512, 256], dropout=None) 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 are 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. Increase the dropout rate to 0.8 in_PREDICT mode by adjusting the TensorFlow Serving parameters 
B. Increase the dropout rate to 0.8 and retrain your model.  
C. Switch from CPU to GPU serving  
D. Apply quantization to your SavedModel by reducing the floating point precision to tf.float16.  

You developed a Vertex Al ML pipeline that consists of preprocessing and training steps and each setof steps runs on a separate custom Docker image Your organization uses GitHub and GitHub Actionsas CI/CD to run unit and integration tests You need to automate the model retraining workflow sothat it can be initiated both manually and when a new version of the code is merged in the mainbranch You want to minimize the steps required to build the workflow while also allowing formaximum flexibility How should you configure the CI/CD workflow?

A. Trigger a Cloud Build workflow to run tests build custom Docker images, push the images toArtifact Registry and launch the pipeline in Vertex Al Pipelines.
B. Trigger GitHub Actions to run the tests launch a job on Cloud Run to build custom Docker imagespush the images to Artifact Registry and launch the pipeline in Vertex Al Pipelines.
C. Trigger GitHub Actions to run the tests build custom Docker images push the images to ArtifactRegistry, and launch the pipeline in Vertex Al Pipelines.
D. Trigger GitHub Actions to run the tests launch a Cloud Build workflow to build custom Dickerimages, push the images to Artifact Registry, and launch the pipeline in Vertex Al Pipelines.

You work on the data science team at a manufacturing company. You are reviewing the company's historical sales data, which has hundreds of millions of records. For your exploratory data analysis, you need to calculate descriptive statistics such as mean, median, and mode; conduct complex statistical tests for hypothesis testing; and plot variations of the features over time You want to use as much of the sales data as possible in your analyses while minimizing computational resources. What should you do?

A. Spin up a Vertex Al Workbench user-managed notebooks instance and import the dataset Use this data to create statistical and visual analyses
B. Visualize the time plots in Google Data Studio. Import the dataset into Vertex Al Workbench usermanaged notebooks Use this data to calculate the descriptive statistics and run the statistical analyses 
C. Use BigQuery to calculate the descriptive statistics. Use Vertex Al Workbench user-managed notebooks to visualize the time plots and run the statistical analyses.
D Use BigQuery to calculate the descriptive statistics, and use Google Data Studio to visualize the time plots. Use Vertex Al Workbench user-managed notebooks to run the statistical analyses. 

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  

You are working with a dataset that contains customer transactions. You need to build an ML modelto predict customer purchase behavior You plan to develop the model in BigQuery ML, and export itto Cloud Storage for online prediction You notice that the input data contains a few categoricalfeatures, including product category and payment method You want to deploy the model as quicklyas possible. What should you do?

A. Use the transform clause with the ML. ONE_HOT_ENCODER function on the categorical features atmodel creation and select the categorical and non-categorical features.
B. Use the ML. ONE_HOT_ENCODER function on the categorical features, and select the encodedcategorical features and non-categorical features as inputs to create your model.
C. Use the create model statement and select the categorical and non-categorical features.
D. Use the ML. ONE_HOT_ENCODER function on the categorical features, and select the encodedcategorical features and non-categorical features as inputs to create your model.

You are an ML engineer at a manufacturing company You are creating a classification model for a predictive maintenance use case You need to predict whether a crucial machine will fail in the next three days so that the repair crew has enough time to fix the machine before it breaks. Regular maintenance of the machine is relatively inexpensive, but a failure would be very costly You have trained several binary classifiers to predict whether the machine will fail. where a prediction of 1 means that the ML model predicts a failure. You are now evaluating each model on an evaluation dataset. You want to choose a model that prioritizes detection while ensuring that more than 50% of the maintenance jobs triggered by your model address an imminent machine failure. Which model should you choose? 

A. The model with the highest area under the receiver operating characteristic curve (AUC ROC) and precision greater than 0 5 
B. The model with the lowest root mean squared error (RMSE) and recall greater than 0.5.  
C. The model with the highest recall where precision is greater than 0.5.  
D. The model with the highest precision where recall is greater than 0.5.  

You need to develop an image classification model by using a large dataset that contains labeledimages in a Cloud Storage Bucket. What should you do?

A. Use Vertex Al Pipelines with the Kubeflow Pipelines SDK to create a pipeline that reads the imagesfrom Cloud Storage and trains the model.
B. Use Vertex Al Pipelines with TensorFlow Extended (TFX) to create a pipeline that reads the imagesfrom Cloud Storage and trams the model.
C. Import the labeled images as a managed dataset in Vertex Al: and use AutoML to tram the model.
D. Convert the image dataset to a tabular format using Dataflow Load the data into BigQuery and useBigQuery ML to tram the model.

You are developing an image recognition model using PyTorch based on ResNet50 architecture. Your code is working fine on your local laptop on a small subsample. Your full dataset has 200k labeled images You want to quickly scale your training workload while minimizing cost. You plan to use 4 V100 GPUs. What should you do? (Choose Correct Answer and Give Reference and Explanation) 

A. Configure a Compute Engine VM with all the dependencies that launches the training Train your model with Vertex Al using a custom tier that contains the required GPUs
B. Package your code with Setuptools. and use a pre-built container Train your model with Vertex Al using a custom tier that contains the required GPUs
C. Create a Vertex Al Workbench user-managed notebooks instance with 4 V100 GPUs, and use it to train your model 
D. Create a Google Kubernetes Engine cluster with a node pool that has 4 V100 GPUs Prepare and submit a TFJob operator to this node pool. 

You are developing a mode! to detect fraudulent credit card transactions. You need to prioritizedetection because missing even one fraudulent transaction could severely impact the credit cardholder. You used AutoML to tram a model on users' profile information and credit card transactiondata. After training the initial model, you notice that the model is failing to detect many fraudulenttransactions. How should you adjust the training parameters in AutoML to improve modelperformance?Choose 2 answers

A. Increase the score threshold.
B. Decrease the score threshold.
C. Add more positive examples to the training set.
D. Add more negative examples to the training set.
E. Reduce the maximum number of node hours for training.

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. Randomly redistribute the data, with 70% for the training set and 30% for the test set  
B. Use sparse representation in the test set  
C. Apply one-hot encoding on the categorical variables in the test data.  
D. Collect more data representing all categories  

You have built a model that is trained on data stored in Parquet files. You access the data through a Hive table hosted on Google Cloud. You preprocessed these data with PySpark and exported it as a CSV file into Cloud Storage. After preprocessing, you execute additional steps to train and evaluate your model. You want to parametrize this model training in Kubeflow Pipelines. What should you do?  

A. Remove the data transformation step from your pipeline.  
B. Containerize the PySpark transformation step, and add it to your pipeline.  
C. Add a ContainerOp to your pipeline that spins a Dataproc cluster, runs a transformation, and then saves the transformed data in Cloud Storage. 
D. Deploy Apache Spark at a separate node pool in a Google Kubernetes Engine cluster. Add a ContainerOp to your pipeline that invokes a corresponding transformation job for this Spark instance. 

You work for a magazine publisher and have been tasked with predicting whether customers will cancel their annual subscription. In your exploratory data analysis, you find that 90% of individuals renew their subscription every year, and only 10% of individuals cancel their subscription. After training a NN Classifier, your model predicts those who cancel their subscription with 99% accuracy and predicts those who renew their subscription with 82% accuracy. How should you interpret these results? 

A. This is not a good result because the model should have a higher accuracy for those who renew their subscription than for those who cancel their subscription. 
B. This is not a good result because the model is performing worse than predicting that people will always renew their subscription. 
C. This is a good result because predicting those who cancel their subscription is more difficult, since there is less data for this group. 
D. This is a good result because the accuracy across both groups is greater than 80%.  

You work for a retailer that sells clothes to customers around the world. You have been tasked with ensuring that ML models are built in a secure manner. Specifically, you need to protect sensitive customer data that might be used in the models. You have identified four fields containing sensitive data that are being used by your data science team: AGE, IS_EXISTING_CUSTOMER, LATITUDE_LONGITUDE, and SHIRT_SIZE. What should you do with the data before it is made available to the data science team for training purposes? 

A. Tokenize all of the fields using hashed dummy values to replace the real values.  
B. Use principal component analysis (PCA) to reduce the four sensitive fields to one PCA vector.  
C. Coarsen the data by putting AGE into quantiles and rounding LATITUDE_LONGTTUDE into single precision. The other two fields are already as coarse as possible. 
D. Remove all sensitive data fields, and ask the data science team to build their models using nonsensitive data. 

You work for a company that manages a ticketing platform for a large chain of cinemas. Customers use a mobile app to search for movies theyre interested in and purchase tickets in the app. Ticket purchase requests are sent to Pub/Sub and are processed with a Dataflow streaming pipeline configured to conduct the following steps: 1. Check for availability of the movie tickets at the selected cinema. 2. Assign the ticket price and accept payment. 3. Reserve the tickets at the selected cinema. 4. Send successful purchases to your database. Each step in this process has low latency requirements (less than 50 milliseconds). You have developed a logistic regression model with BigQuery ML that predicts whether offering a promo code for free popcorn increases the chance of a ticket purchase, and this prediction should be added to the ticket purchase process. You want to identify the simplest way to deploy this model to production while adding minimal latency. What should you do? 

A. Run batch inference with BigQuery ML every five minutes on each new set of tickets issued.  
B. Export your model in TensorFlow format, and add a tfx_bsl.public.beam.RunInference step to the Dataflow pipeline.
C. Export your model in TensorFlow format, deploy it on Vertex AI, and query the prediction endpoint from your streaming pipeline.  
D. Convert your model with TensorFlow Lite (TFLite), and add it to the mobile app so that the promo code and the incoming request arrive together in Pub/Sub. 

You deployed an ML model into production a year ago. Every month, you collect all raw requests that were sent to your model prediction service during the previous month. You send a subset of these requests to a human labeling service to evaluate your models performance. After a year, you notice that your model's performance sometimes degrades significantly after a month, while other times it takes several months to notice any decrease in performance. The labeling service is costly, but you also need to avoid large performance degradations. You want to determine how often you should retrain your model to maintain a high level of performance while minimizing cost. What should you do? 

A. Train an anomaly detection model on the training dataset, and run all incoming requests through this model. If an anomaly is detected, send the most recent serving data to the labeling service. 
B. Identify temporal patterns in your models performance over the previous year. Based on these patterns, create a schedule for sending serving data to the labeling service for the next year. C. Compare the cost of the labeling service with the lost revenue due to model performance
C. Compare the cost of the labeling service with the lost revenue due to model performance degradation over the past year. If the lost revenue is greater than the cost of the labeling service, increase the frequency of model retraining; otherwise, decrease the model retraining frequency.
D. Run training-serving skew detection batch jobs every few days to compare the aggregate statistics of the features in the training dataset with recent serving data. If skew is detected, send the most recent serving data to the labeling service. 

You work for an online publisher that delivers news articles to over 50 million readers. You have built an AI model that recommends content for the companys weekly newsletter. A recommendation is considered successful if the article is opened within two days of the newsletters published date and the user remains on the page for at least one minute. All the information needed to compute the success metric is available in BigQuery and is updated hourly. The model is trained on eight weeks of data, on average its performance degrades below the acceptable baseline after five weeks, and training time is 12 hours. You want to ensure that the models performance is above the acceptable baseline while minimizing cost. How should you monitor the model to determine when retraining is necessary? 

A. Use Vertex AI Model Monitoring to detect skew of the input features with a sample rate of 100% and a monitoring frequency of two days. 
B. Schedule a cron job in Cloud Tasks to retrain the model every week before the newsletter is created.
C. Schedule a weekly query in BigQuery to compute the success metric.  
D. Schedule a daily Dataflow job in Cloud Composer to compute the success metric.  

You need to deploy a scikit-learn classification model to production. The model must be able to serverequests 24 and you expect millions of requests per second to the production application from 8am to 7 pm. You need to minimize the cost of deployment What should you do?

A. Deploy an online Vertex Al prediction endpoint Set the max replica count to 1
B. Deploy an online Vertex Al prediction endpoint Set the max replica count to 100
C. Deploy an online Vertex Al prediction endpoint with one GPU per replica Set the max replica countto 1.
D. Deploy an online Vertex Al prediction endpoint with one GPU per replica Set the max replica countto 100.