Google Professional-Machine-Learning-Engineer Exam Dumps

Google Professional Machine Learning Engineer

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Google Professional-Machine-Learning-Engineer Sample Questions

Question # 1

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. 

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Question # 2

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.  

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Question # 3

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.  

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Question # 4

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.

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Question # 5

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. 

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