Exam Dumps Professional-Machine-Learning-Engineer Practice Free Latest Google Practice Tests [Q34-Q49]

Rate this post

Exam Dumps Professional-Machine-Learning-Engineer Practice Free Latest Google Practice Tests

Professional-Machine-Learning-Engineer Exam Questions | Real Professional-Machine-Learning-Engineer Practice Dumps

Google Professional Machine Learning Engineer Certification Exam is a professional certification that is designed to test an individual’s proficiency in designing, building, and deploying machine learning models on the Google Cloud Platform. Google Professional Machine Learning Engineer certification is intended for individuals who have a thorough understanding of machine learning principles and experience with the Google Cloud Platform. Professional-Machine-Learning-Engineer exam is designed to test an individual’s ability to analyze and interpret data, design machine learning models, train and optimize models, and deploy models into production.

The Google Professional-Machine-Learning-Engineer exam covers a wide range of topics, including data preparation and feature engineering, model selection and training, hyperparameter tuning, model evaluation, and deployment of machine learning models on Google Cloud. Candidates are expected to have a solid understanding of machine learning algorithms, statistical analysis, and data visualization.

NO.34 You need to analyze user activity data from your company’s mobile applications. Your team will use BigQuery for data analysis, transformation, and experimentation with ML algorithms. You need to ensure real-time ingestion of the user activity data into BigQuery. What should you do?

Configure Pub/Sub to stream the data into BigQuery.

Run an Apache Spark streaming job on Dataproc to ingest the data into BigQuery.

Run a Dataflow streaming job to ingest the data into BigQuery.

Configure Pub/Sub and a Dataflow streaming job to ingest the data into BigQuery,

NO.35 You work for a global footwear retailer and need to predict when an item will be out of stock based on historical inventory dat a. Customer behavior is highly dynamic since footwear demand is influenced by many different factors. You want to serve models that are trained on all available data, but track your performance on specific subsets of data before pushing to production. What is the most streamlined and reliable way to perform this validation?

Use the TFX ModelValidator tools to specify performance metrics for production readiness

Use k-fold cross-validation as a validation strategy to ensure that your model is ready for production.

Use the last relevant week of data as a validation set to ensure that your model is performing accurately on current data

Use the entire dataset and treat the area under the receiver operating characteristics curve (AUC ROC) as the main metric.

NO.36 You are building a linear regression model on BigQuery ML to predict a customer’s likelihood of purchasing your company’s products. Your model uses a city name variable as a key predictive component. In order to train and serve the model, your data must be organized in columns. You want to prepare your data using the least amount of coding while maintaining the predictable variables. What should you do?

Create a new view with BigQuery that does not include a column with city information

Use Dataprep to transform the state column using a one-hot encoding method, and make each city a column with binary values.

Use Cloud Data Fusion to assign each city to a region labeled as 1, 2, 3, 4, or 5r and then use that number to represent the city in the model.

Use TensorFlow to create a categorical variable with a vocabulary list Create the vocabulary file, and upload it as part of your model to BigQuery ML.

NO.37 Your team is building a convolutional neural network (CNN)-based architecture from scratch. The preliminary experiments running on your on-premises CPU-only infrastructure were encouraging, but have slow convergence. You have been asked to speed up model training to reduce time-to-market. You want to experiment with virtual machines (VMs) on Google Cloud to leverage more powerful hardware. Your code does not include any manual device placement and has not been wrapped in Estimator model-level abstraction. Which environment should you train your model on?

AVM on Compute Engine and 1 TPU with all dependencies installed manually.

AVM on Compute Engine and 8 GPUs with all dependencies installed manually.

A Deep Learning VM with an n1-standard-2 machine and 1 GPU with all libraries pre-installed.

A Deep Learning VM with more powerful CPU e2-highcpu-16 machines with all libraries pre-installed.

NO.38 You have deployed a model on Vertex AI for real-time inference. During an online prediction request, you get an “Out of Memory” error. What should you do?

Use batch prediction mode instead of online mode.

Send the request again with a smaller batch of instances.

Use base64 to encode your data before using it for prediction.

Apply for a quota increase for the number of prediction requests.

NO.39 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?

Significantly increase the max_batch_size TensorFlow Serving parameter

Switch to the tensorflow-model-server-universal version of TensorFlow Serving

Significantly increase the max_enqueued_batches TensorFlow Serving parameter

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

NO.40 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? (Choose Correct Answer and Give Reference and Explanation)

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

Package 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.

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

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

NO.41 You are an ML engineer in the contact center of a large enterprise. You need to build a sentiment analysis tool that predicts customer sentiment from recorded phone conversations. You need to identify the best approach to building a model while ensuring that the gender, age, and cultural differences of the customers who called the contact center do not impact any stage of the model development pipeline and results. What should you do?

Extract sentiment directly from the voice recordings

Convert the speech to text and build a model based on the words

Convert the speech to text and extract sentiments based on the sentences

Convert the speech to text and extract sentiment using syntactical analysis

NO.42 You work for a magazine publisher and have been tasked with predicting whether customers will cancel their annual subscription. In your exploratory data analysis, you find that 90% of individuals renew their subscription every year, and only 10% of individuals cancel their subscription. After training a NN Classifier, your model predicts those who cancel their subscription with 99% accuracy and predicts those who renew their subscription with 82% accuracy. How should you interpret these results?

This is not a good result because the model should have a higher accuracy for those who renew their subscription than for those who cancel their subscription.

This is not a good result because the model is performing worse than predicting that people will always renew their subscription.

This is a good result because predicting those who cancel their subscription is more difficult, since there is less data for this group.

This is a good result because the accuracy across both groups is greater than 80%.

NO.43 You are working on a classification problem with time series data and achieved an area under the receiver operating characteristic curve (AUC ROC) value of 99% for training data after just a few experiments. You haven’t explored using any sophisticated algorithms or spent any time on hyperparameter tuning. What should your next step be to identify and fix the problem?

Address the model overfitting by using a less complex algorithm.

Address data leakage by applying nested cross-validation during model training.

Address data leakage by removing features highly correlated with the target value.

Address the model overfitting by tuning the hyperparameters to reduce the AUC ROC value.

NO.44 A Machine Learning Specialist receives customer data for an online shopping website. The data includes demographics, past visits, and locality information. The Specialist must develop a machine learning approach to identify the customer shopping patterns, preferences, and trends to enhance the website for better service and smart recommendations.
Which solution should the Specialist recommend?

Latent Dirichlet Allocation (LDA) for the given collection of discrete data to identify patterns in the customer database.

A neural network with a minimum of three layers and random initial weights to identify patterns in the customer database.

Collaborative filtering based on user interactions and correlations to identify patterns in the customer database.

Random Cut Forest (RCF) over random subsamples to identify patterns in the customer database.

NO.45 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?

Use the batch prediction functionality of Al Platform

Create a serving pipeline in Compute Engine for prediction

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

Deploy the model on Al Platform and create a version of it for online inference.

NO.46 A Machine Learning Specialist is implementing a full Bayesian network on a dataset that describes public transit in New York City. One of the random variables is discrete, and represents the number of minutes New Yorkers wait for a bus given that the buses cycle every 10 minutes, with a mean of 3 minutes.
Which prior probability distribution should the ML Specialist use for this variable?

Poisson distribution

Uniform distribution

Normal distribution

Binomial distribution

NO.47 You work for a company that provides an anti-spam service that flags and hides spam posts on social media platforms. Your company currently uses a list of 200,000 keywords to identify suspected spam posts. If a post contains more than a few of these keywords, the post is identified as spam. You want to start using machine learning to flag spam posts for human review. What is the main advantage of implementing machine learning for this business case?

Posts can be compared to the keyword list much more quickly.

New problematic phrases can be identified in spam posts.

A much longer keyword list can be used to flag spam posts.

Spam posts can be flagged using far fewer keywords.

NO.48 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?

1 = Al Platform, 2 = Al Platform, 3 = AutoML Vision

1 = Al Platform, 2 = Al Platform, 3 = AutoML Natural Language

1 = Al Platform, 2 = Al Platform, 3 = Cloud Natural Language API

1 = cloud Natural Language API, 2 = Al Platform, 3 = Cloud Vision API

NO.49 You are designing an ML recommendation model for shoppers on your company’s ecommerce website. You will use Recommendations Al to build, test, and deploy your system. How should you develop recommendations that increase revenue while following best practices?

Use the “Other Products You May Like” recommendation type to increase the click-through rate

Use the “Frequently Bought Together’ recommendation type to increase the shopping cart size for each order.

Import your user events and then your product catalog to make sure you have the highest quality event stream

Because it will take time to collect and record product data, use placeholder values for the product catalog to test the viability of the model.

Verified Professional-Machine-Learning-Engineer Exam Dumps Q&As – Provide Professional-Machine-Learning-Engineer with Correct Answers: https://www.pdf4test.com/Professional-Machine-Learning-Engineer-dump-torrent.html

Free certification test prep

Exam Dumps Professional-Machine-Learning-Engineer Practice Free Latest Google Practice Tests [Q34-Q49]

Leave a Reply Cancel reply