Google Professional Machine Learning Engineer (Professional-Machine-Learning-Engineer) Free Practice Test

Question 1

You have built a custom model that performs several memory-intensive preprocessing tasks before it makes a prediction. You deployed the model to a Vertex Al endpoint. and validated that results were received in a reasonable amount of time After routing user traffic to the endpoint, you discover that the endpoint does not autoscale as expected when receiving multiple requests What should you do?

A. Decrease the number of workers per machine

B. Increase the CPU utilization target in the autoscaling configurations

C. Use a machine type with more memory

D. Decrease the CPU utilization target in the autoscaling configurations

Correct Answer: D

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Question 2

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. 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.

B. Use principal component analysis (PCA) to reduce the four sensitive fields to one PCA vector.

C. Remove all sensitive data fields, and ask the data science team to build their models using non-sensitive data.

D. Tokenize all of the fields using hashed dummy values to replace the real values.

Correct Answer: A

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Question 3

You are responsible for managing and monitoring a Vertex AI model that is deployed in production. You want to automatically retrain the model when its performance deteriorates. What should you do?

A. Use Vertex Explainable AI to analyze feature attributions and identify potential biases in the model.
Retrain when significant shifts in feature importance or biases are detected.

B. Create a Vertex AI Model Monitoring job to track the model ' s performance with production data, and trigger retraining when specific metrics drop below predefined thresholds.

C. Configure a scheduled job to evaluate the model ' s performance on a static dataset, and retrain the model if the performance drops below predefined thresholds.

D. Collect feedback from end users, and retrain the model based on their assessment of its performance.

Correct Answer: B

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Question 4

You are an ML engineer at a global car manufacturer. You need to build an ML model to predict car sales in different cities around the world. Which features or feature crosses should you use to train city-specific relationships between car type and number of sales?

A. One feature obtained as an element-wise product between binned latitude, binned longitude, and one- hot encoded car type

B. Two feature crosses as a element-wise product the first between binned latitude and one-hot encoded car type, and the second between binned longitude and one-hot encoded car type

C. One feature obtained as an element-wise product between latitude, longitude, and car type

D. Three individual features binned latitude, binned longitude, and one-hot encoded car type

Correct Answer: A

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Question 5

You are pre-training a large language model on Google Cloud. This model includes custom TensorFlow operations in the training loop Model training will use a large batch size, and you expect training to take several weeks You need to configure a training architecture that minimizes both training time and compute costs What should you do?

A.

B.

C.

D.

Correct Answer: A

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Question 6

You have created a Vertex Al pipeline that includes two steps. The first step preprocesses 10 TB data completes in about 1 hour, and saves the result in a Cloud Storage bucket The second step uses the processed data to train a model You need to update the model ' s code to allow you to test different algorithms You want to reduce pipeline execution time and cost, while also minimizing pipeline changes What should you do?

A. Enable caching for the pipeline job. and disable caching for the model training step.

B. Add a pipeline parameter and an additional pipeline step Depending on the parameter value the pipeline step conducts or skips data preprocessing and starts model training.

C. Configure a machine with more CPU and RAM from the compute-optimized machine family for the data preprocessing step.

D. Create another pipeline without the preprocessing step, and hardcode the preprocessed Cloud Storage file location for model training.

Correct Answer: A

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Question 7

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, 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.

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 Memorystore for writing and for reading the user's navigation context, and then deploy the model on Google Kubernetes Engine.

D. Embed the client on the website, and then deploy the model on AI Platform Prediction.

Correct Answer: D

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Question 8

You are developing a model to help your company create more targeted online advertising campaigns. You need to create a dataset that you will use to train the model. You want to avoid creating or reinforcing unfair bias in the model. What should you do?
Choose 2 answers

A. Include a comprehensive set of demographic features.

B. Collect a stratified sample of production traffic to build the training dataset.

C. Conduct fairness tests across sensitive categories and demographics on the trained model.

D. include only the demographic groups that most frequently interact with advertisements.

E. Collect a random sample of production traffic to build the training dataset.

Correct Answer: C,E

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Question 9

You work on a growing team of more than 50 data scientists who all use AI Platform. You are designing a strategy to organize your jobs, models, and versions in a clean and scalable way. Which strategy should you choose?

A. Use labels to organize resources into descriptive categories. Apply a label to each created resource so that users can filter the results by label when viewing or monitoring the resources.

B. Set up restrictive IAM permissions on the AI Platform notebooks so that only a single user or group can access a given instance.

C. Separate each data scientist's work into a different project to ensure that the jobs, models, and versions created by each data scientist are accessible only to that user.

D. Set up a BigQuery sink for Cloud Logging logs that is appropriately filtered to capture information about AI Platform resource usage. In BigQuery, create a SQL view that maps users to the resources they are using

Correct Answer: A

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Question 10

You received a training-serving skew alert from a Vertex Al Model Monitoring job running in production.
You retrained the model with more recent training data, and deployed it back to the Vertex Al endpoint but you are still receiving the same alert. What should you do?

A. Update the model monitoring job to use the more recent training data that was used to retrain the model.

B. Temporarily disable the alert Enable the alert again after a sufficient amount of new production traffic has passed through the Vertex Al endpoint.

C. Update the model monitoring job to use a lower sampling rate.

D. Temporarily disable the alert until the model can be retrained again on newer training data Retrain the model again after a sufficient amount of new production traffic has passed through the Vertex Al endpoint

Correct Answer: A

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Question 11

You are creating a social media app where pet owners can post images of their pets. You have one million user uploaded images with hashtags. You want to build a comprehensive system that recommends images to users that are similar in appearance to their own uploaded images.
What should you do?

A. Download a pretrained convolutional neural network, and fine-tune the model to predict hashtags based on the input images. Use the predicted hashtags to make recommendations.

B. Download a pretrained convolutional neural network, and use the model to generate embeddings of the input images. Measure similarity between embeddings to make recommendations.

C. Retrieve image labels and dominant colors from the input images using the Vision API. Use these properties and the hashtags to make recommendations.

D. Use the provided hashtags to create a collaborative filtering algorithm to make recommendations.

Correct Answer: B

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Question 12

You are building a linear model with over 100 input features, all with values between -1 and 1. You suspect that many features are non-informative. You want to remove the non-informative features from your model while keeping the informative ones in their original form. Which technique should you use?

A. Use an iterative dropout technique to identify which features do not degrade the model when removed.

B. After building your model, use Shapley values to determine which features are the most informative.

C. Use Principal Component Analysis to eliminate the least informative features.

D. Use L1 regularization to reduce the coefficients of uninformative features to 0.

Correct Answer: D

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Question 13

Your task is classify if a company logo is present on an image. You found out that 96% of a data does not include a logo. You are dealing with data imbalance problem. Which metric do you use to evaluate to model?

A. F Score with higher recall weighted than precision

B. F Score with higher precision weighting than recall

C. RMSE

D. F1 Score

Correct Answer: D

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Question 14

You are implementing a batch inference ML pipeline in Google Cloud. The model was developed by using TensorFlow and is stored in SavedModel format in Cloud Storage. You need to apply the model to a historical dataset that is stored in a BigQuery table. You want to perform inference with minimal effort. What should you do?

A. Import the TensorFlow model by using the create model statement in BigQuery ML. Apply the historical data to the TensorFlow model.

B. Configure and deploy a Vertex Al endpoint. Use the endpoint to get predictions from the historical data in BigQuery.

C. Export the historical data to Cloud Storage in Avro format. Configure a Vertex Al batch prediction job to generate predictions for the exported data.

D. Export the historical data to Cloud Storage in CSV format. Configure a Vertex Al batch prediction job to generate predictions for the exported data.

Correct Answer: C

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Google Professional Machine Learning Engineer (Professional-Machine-Learning-Engineer) Free Practice Test