All About 100% Guarantee Professional-Machine-Learning-Engineer Exam Dumps

NEW QUESTION 1
You are building a real-time prediction engine that streams files which may contain Personally Identifiable Information (Pll) to Google Cloud. You want to use the Cloud Data Loss Prevention (DLP) API to scan the files. How should you ensure that the Pll is not accessible by unauthorized individuals?

• A. Stream all files to Google CloudT and then write the data to BigQuery Periodically conduct a bulk scan of the table using the DLP API.
• B. Stream all files to Google Cloud, and write batches of the data to BigQuery While the data is being written to BigQuery conduct a bulk scan of the data using the DLP API.
• C. Create two buckets of data Sensitive and Non-sensitive Write all data to the Non-sensitive bucket Periodically conduct a bulk scan of that bucket using the DLP API, and move the sensitive data to the Sensitive bucket
• D. Create three buckets of data: Quarantine, Sensitive, and Non-sensitive Write all data to the Quarantine bucket.
• E. Periodically conduct a bulk scan of that bucket using the DLP API, and move the data to either the Sensitive or Non-Sensitive bucket

Answer: A

NEW QUESTION 2
You are an ML engineer at a global shoe store. You manage the ML models for the company's website. You are asked to build a model that will recommend new products to the user based on their purchase behavior and similarity with other users. What should you do?

• A. Build a classification model
• B. Build a knowledge-based filtering model
• C. Build a collaborative-based filtering model
• D. Build a regression model using the features as predictors

Answer: C

NEW QUESTION 3
You work on a growing team of more than 50 data scientists who all use Al 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. Set up restrictive I AM permissions on the Al Platform notebooks so that only a single user or group can access a given instance.
• B. 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.
• C. Use labels to organize resources into descriptive categorie
• D. Apply a label to each created resource so that users can filter the results by label when viewing or monitoring the resources
• E. Set up a BigQuery sink for Cloud Logging logs that is appropriately filtered to capture information about Al Platform resource usage In BigQuery create a SQL view that maps users to the resources they are using.

Answer: B

NEW QUESTION 4
You work for a toy manufacturer that has been experiencing a large increase in demand. You need to build an ML model to reduce the amount of time spent by quality control inspectors checking for product defects. Faster defect detection is a priority. The factory does not have reliable Wi-Fi. Your company wants to implement the new ML model as soon as possible. Which model should you use?

• A. AutoML Vision model
• B. AutoML Vision Edge mobile-versatile-1 model
• C. AutoML Vision Edge mobile-low-latency-1 model
• D. AutoML Vision Edge mobile-high-accuracy-1 model

Answer: A

NEW QUESTION 5
You developed an ML model with Al 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. Significantly increase the max_batch_size TensorFlow Serving parameter
• B. Switch to the tensorflow-model-server-universal version of TensorFlow Serving
• C. Significantly increase the max_enqueued_batches TensorFlow Serving parameter
• D. Recompile TensorFlow Serving using the source to support CPU-specific optimizations Instruct GKE to choose an appropriate baseline minimum CPU platform for serving nodes

Answer: A

NEW QUESTION 6
You are an ML engineer at a large grocery retailer with stores in multiple regions. You have been asked to create an inventory prediction model. Your models features include region, location, historical demand, and seasonal popularity. You want the algorithm to learn from new inventory data on a daily basis. Which algorithms should you use to build the model?

• A. Classification
• B. Reinforcement Learning
• C. Recurrent Neural Networks (RNN)
• D. Convolutional Neural Networks (CNN)

Answer: B

NEW QUESTION 7
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:

A)

B)

C)

D)

• A. Option A
• B. Option B
• C. Option C
• D. Option D

Answer: D

NEW QUESTION 8
During batch training of a neural network, you notice that there is an oscillation in the loss. How should you adjust your model to ensure that it converges?

• A. Increase the size of the training batch
• B. Decrease the size of the training batch
• C. Increase the learning rate hyperparameter
• D. Decrease the learning rate hyperparameter

Answer: C

NEW QUESTION 9
You built and manage a production system that is responsible for predicting sales numbers. Model accuracy is crucial, because the production model is required to keep up with market changes. Since being deployed to production, the model hasn't changed; however the accuracy of the model has steadily deteriorated. What issue is most likely causing the steady decline in model accuracy?

• A. Poor data quality
• B. Lack of model retraining
• C. Too few layers in the model for capturing information
• D. Incorrect data split ratio during model training, evaluation, validation, and test

Answer: D

NEW QUESTION 10
You have a functioning end-to-end ML pipeline that involves tuning the hyperparameters of your ML model using Al Platform, and then using the best-tuned parameters for training. Hypertuning is taking longer than expected and is delaying the downstream processes. You want to speed up the tuning job without significantly compromising its effectiveness. Which actions should you take?
Choose 2 answers

• A. Decrease the number of parallel trials
• B. Decrease the range of floating-point values
• C. Set the early stopping parameter to TRUE
• D. Change the search algorithm from Bayesian search to random search.
• E. Decrease the maximum number of trials during subsequent training phases.

Answer: DE

NEW QUESTION 11
You are building a model to predict daily temperatures. You split the data randomly and then transformed the training and test datasets. Temperature data for model training is uploaded hourly. During testing, your model performed with 97% accuracy; however, after deploying to production, the model's accuracy dropped to 66%. How can you make your production model more accurate?

• A. Normalize the data for the training, and test datasets as two separate steps.
• B. Split the training and test data based on time rather than a random split to avoid leakage
• C. Add more data to your test set to ensure that you have a fair distribution and sample for testing
• D. Apply data transformations before splitting, and cross-validate to make sure that the transformations are applied to both the training and test sets.

Answer: C

NEW QUESTION 12
You work for a social media company. You need to detect whether posted images contain cars. Each training example is a member of exactly one class. You have trained an object detection neural network and deployed the model version to Al Platform Prediction for evaluation. Before deployment, you created an evaluation job and attached it to the Al Platform Prediction model version. You notice that the precision is lower than your business requirements allow. How should you adjust the model's final layer softmax threshold to increase precision?

• A. Increase the recall
• B. Decrease the recall.
• C. Increase the number of false positives
• D. Decrease the number of false negatives

Answer: D

NEW QUESTION 13
You need to design a customized deep neural network in Keras that will predict customer purchases based on their purchase history. You want to explore model performance using multiple model architectures, store training data, and be able to compare the evaluation metrics in the same dashboard. What should you do?

• A. Create multiple models using AutoML Tables
• B. Automate multiple training runs using Cloud Composer
• C. Run multiple training jobs on Al Platform with similar job names
• D. Create an experiment in Kubeflow Pipelines to organize multiple runs

Answer: C

NEW QUESTION 14
You are designing an architecture with a serveress ML system to enrich customer support tickets with informative metadata before they are routed to a support agent. You need a set of models to predict ticket priority, predict ticket resolution time, and perform sentiment analysis to help agents make strategic decisions when they process support requests. Tickets are not expected to have any domain-specific terms or jargon.
The proposed architecture has the following flow:

Which endpoints should the Enrichment Cloud Functions call?

• A. 1 = Al Platform, 2 = Al Platform, 3 = AutoML Vision
• B. 1 = Al Platform, 2 = Al Platform, 3 = AutoML Natural Language
• C. 1 = Al Platform, 2 = Al Platform, 3 = Cloud Natural Language API
• D. 1 = cloud Natural Language API, 2 = Al Platform, 3 = Cloud Vision API

Answer: B

NEW QUESTION 15
You were asked to investigate failures of a production line component based on sensor readings. After receiving the dataset, you discover that less than 1% of the readings are positive examples representing failure incidents. You have tried to train several classification models, but none of them converge. How should you resolve the class imbalance problem?

• A. Use the class distribution to generate 10% positive examples
• B. Use a convolutional neural network with max pooling and softmax activation
• C. Downsample the data with upweighting to create a sample with 10% positive examples
• D. Remove negative examples until the numbers of positive and negative examples are equal

Answer: D

NEW QUESTION 16
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