Newest Google Professional-Machine-Learning-Engineer Practice Questions at Zero Cost

Get a glimpse of the real Professional-Machine-Learning-Engineer certification exam challenges with our free Google Professional-Machine-Learning-Engineer practice test questions.

Question 1

You work for a company that is developing an application to help users with meal planning You want to use machine learning to scan a corpus of recipes and extract each ingredient (e g carrot, rice pasta) and each kitchen cookware (e.g. bowl, pot spoon) mentioned Each recipe is saved in an unstructured text file What should you do?

ACreate a text dataset on Vertex Al for entity extraction Create two entities called ingredient' and cookware' and label at least 200 examples of each entity Train an AutoML entity extraction model to extract occurrences of these entity types Evaluate performance on a holdout dataset.

BCreate a multi-label text classification dataset on Vertex Al Create a test dataset and label each recipe that corresponds to its ingredients and cookware Train a multi-class classification model Evaluate the model's performance on a holdout dataset.

CUse the Entity Analysis method of the Natural Language API to extract the ingredients and cookware from each recipe Evaluate the model's performance on a prelabeled dataset.

DCreate a text dataset on Vertex Al for entity extraction Create as many entities as there are different ingredients and cookware Train an AutoML entity extraction model to extract those entities Evaluate the models performance on a holdout dataset.

Correct Answer: 1

Question 2

You work for a retail company. You have been tasked with building a model to determine the probability of churn for each customer. You need the predictions to be interpretable so the results can be used to develop marketing campaigns that target at-risk customers. What should you do?

ABuild a random forest regression model in a Vertex Al Workbench notebook instance Configure the model to generate feature importance's after the model is trained.

BBuild an AutoML tabular regression model Configure the model to generate explanations when it makes predictions.

CBuild a custom TensorFlow neural network by using Vertex Al custom training Configure the model to generate explanations when it makes predictions.

DBuild a random forest classification model in a Vertex Al Workbench notebook instance Configure the model to generate feature importance's after the model is trained.

Correct Answer: 2

Question 3

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?

ACreate a Google Kubernetes Engine cluster with a node pool that has 4 V100 GPUs Prepare and submit a TFJob operator to this node pool.

BConfigure a Compute Engine VM with all the dependencies that launches the training Tram your model with Vertex Al using a custom tier that contains the required GPUs.

CCreate a Vertex Al Workbench user-managed notebooks instance with 4 V100 GPUs, and use it to tram your model.

DPackage 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.

Correct Answer: 3

Question 4

You are developing a custom image classification model in Python. You plan to run your training application on Vertex Al Your input dataset contains several hundred thousand small images You need to determine how to store and access the images for training. You want to maximize data throughput and minimize training time while reducing the amount of additional code. What should you do?

AStore image files in Cloud Storage and access them directly.

BStore image files in Cloud Storage and access them by using serialized records.

CStore image files in Cloud Filestore, and access them by using serialized records.

DStore image files in Cloud Filestore and access them directly by using an NFS mount point.

Correct Answer: 4

Question 5

You are developing a custom TensorFlow classification model based on tabular dat

a. Your raw data is stored in BigQuery contains hundreds of millions of rows, and includes both categorical and numerical features. You need to use a MaxMin scaler on some numerical features, and apply a one-hot encoding to some categorical features such as SKU names. Your model will be trained over multiple epochs. You want to minimize the effort and cost of your solution. What should you do?

A1 Write a SQL query to create a separate lookup table to scale the numerical features.
2. Deploy a TensorFlow-based model from Hugging Face to BigQuery to encode the text features.
3. Feed the resulting BigQuery view into Vertex Al Training.

B1 Use BigQuery to scale the numerical features.
2. Feed the features into Vertex Al Training.
3 Allow TensorFlow to perform the one-hot text encoding.

C1 Use TFX components with Dataflow to encode the text features and scale the numerical features.
2 Export results to Cloud Storage as TFRecords.
3 Feed the data into Vertex Al Training.

D1 Write a SQL query to create a separate lookup table to scale the numerical features.
2 Perform the one-hot text encoding in BigQuery.
3. Feed the resulting BigQuery view into Vertex Al Training.

Correct Answer: 5

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