Free Google Professional-Machine-Learning-Engineer Exam Questions

Question 1

Your team is working on an NLP research project to predict political affiliation of authors based on articles they have written. You have a large training dataset that is structured like this:

You followed the standard 80%-10%-10?ta distribution across the training, testing, and evaluation subsets. How should you distribute the training examples across the train-test-eval subsets while maintaining the 80-10-10 proportion?

A : tribute texts randomly across the train-test-eval subsets: Train set: [TextA1, TextB2, ...] Test set: [TextA2, TextC1, TextD2, ...] Eval set: [TextB1, TextC2, TextD1, ...]

B : tribute authors randomly across the train-test-eval subsets: (*) Train set: [TextA1, TextA2, TextD1, TextD2, ...] Test set: [TextB1, TextB2, ...] Eval set: [TexC1,TextC2 ...]

C : tribute sentences randomly across the train-test-eval subsets: Train set: [SentenceA11, SentenceA21, SentenceB11, SentenceB21, SentenceC11, SentenceD21 ...] Test set: [SentenceA12, SentenceA22, SentenceB12, SentenceC22, SentenceC12, SentenceD22 ...] Eval set: [SentenceA13, SentenceA23, SentenceB13, SentenceC23, SentenceC13, SentenceD31 ...]

D : tribute paragraphs of texts (i.e., chunks of consecutive sentences) across the train-test-eval subsets: Train set: [SentenceA11, SentenceA12, SentenceD11, SentenceD12 ...] Test set: [SentenceA13, SentenceB13, SentenceB21, SentenceD23, SentenceC12, SentenceD13 ...] Eval set: [SentenceA11, SentenceA22, SentenceB13, SentenceD22, SentenceC23, SentenceD11 ...]

Answer: C

Question 2

As the lead ML Engineer for your company, you are responsible for building ML models to digitize scanned customer forms. You have developed a TensorFlow model that converts the scanned images into text and stores them in Cloud Storage. You need to use your ML model on the aggregated data collected at the end of each day with minimal manual intervention. What should you do?

A : the batch prediction functionality of AI Platform.

B : eate a serving pipeline in Compute Engine for prediction.

C : Cloud Functions for prediction each time a new data point is ingested

D : ploy the model on AI Platform and create a version of it for online inference.

Answer: D

Question 3

You developed an ML model with AI Platform, and you want to move it to production. You serve a few thousand queries per second and are experiencing latency issues. Incoming requests are served by a load balancer that distributes them across multiple Kubeflow CPU-only pods running on Google Kubernetes Engine

(GKE). Your goal is to improve the serving latency without changing the underlying infrastructure. What should you do?

A : gnificantly increase the max_batch_size TensorFlow Serving parameter.

B : itch to the tensorflow-model-server-universal version of TensorFlow Serving.

C : gnificantly increase the max_enqueued_batches TensorFlow Serving parameter.

D : compile TensorFlow Serving using the source to support CPU-specific optimizations. Instruct GKE to choose an appropriate baseline minimum CPU platform for serving nodes

Answer: D

Question 4

You work at a subscription-based company. You have trained an ensemble of trees and neural networks to predict customer churn, which is the likelihood that customers will not renew their yearly subscription. The average prediction is a 15% churn rate, but for a particular customer the model predicts that they are 70% likely to churn. The customer has a product usage history of 30%, is located in New York City, and became a customer in 1997. You need to explain the difference between the actual prediction, a 70% churn rate, and the average prediction. You want to use Vertex Explainable AI. What should you do?

A : ain local surrogate models to explain individual predictions.

B : nfigure sampled Shapley explanations on Vertex Explainable AI.

C : nfigure integrated gradients explanations on Vertex Explainable AI.

D : asure the effect of each feature as the weight of the feature multiplied by the feature value.

Answer: A

Question 5

You work for a large social network service provider whose users post articles and discuss news. Millions of comments are posted online each day, and more than 200 human moderators constantly review comments and flag those that are inappropriate. Your team is building an ML model to help human moderators check content on the platform. The model scores each comment and flags suspicious comments to be reviewed by a human. Which metric(s) should you use to monitor the model’s performance?

A : mber of messages flagged by the model per minute

B : mber of messages flagged by the model per minute confirmed as being inappropriate by humans.

C : ecision and recall estimates based on a random sample of 0.1% of raw messages each minute sent to a human for review

D : ecision and recall estimates based on a sample of messages flagged by the model as potentially inappropriate each minute

Answer: B