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New PremiumVCEDump Professional-Machine-Learning-Engineer Exam Questions Real Professional-Machine-Learning-Engineer Dumps Updated on Jun 05, 2022 [Q34-Q54]

New PremiumVCEDump Professional-Machine-Learning-Engineer Exam Questions| Real Professional-Machine-Learning-Engineer Dumps Updated on Jun 05, 2022

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NEW QUESTION 34
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?

Increase the recall

Decrease the recall.

Increase the number of false positives

Decrease the number of false negatives

NEW QUESTION 35
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)

Option A

Option B

Option C

Option D

NEW QUESTION 36
You are working on a Neural Network-based project. The dataset provided to you has columns with different ranges. While preparing the data for model training, you discover that gradient optimization is having difficulty moving weights to a good solution. What should you do?

Use feature construction to combine the strongest features.

Use the representation transformation (normalization) technique.

Improve the data cleaning step by removing features with missing values.

Change the partitioning step to reduce the dimension of the test set and have a larger training set.

NEW QUESTION 37
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.

NEW QUESTION 38
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.

NEW QUESTION 39
Your organization’s call center has asked you to develop a model that analyzes customer sentiments in each call. The call center receives over one million calls daily, and data is stored in Cloud Storage. The data collected must not leave the region in which the call originated, and no Personally Identifiable Information (Pll) can be stored or analyzed. The data science team has a third-party tool for visualization and access which requires a SQL ANSI-2011 compliant interface. You need to select components for data processing and for analytics. How should the data pipeline be designed?

1 = Dataflow, 2 = BigQuery

1 = Pub/Sub, 2 = Datastore

1 = Dataflow, 2 = Cloud SQL

1 = Cloud Function, 2 = Cloud SQL

NEW QUESTION 40
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.

NEW QUESTION 41
A web-based company wants to improve its conversion rate on its landing page. Using a large historical dataset of customer visits, the company has repeatedly trained a multi-class deep learning network algorithm on Amazon SageMaker. However, there is an overfitting problem: training data shows 90% accuracy in predictions, while test data shows 70% accuracy only.
The company needs to boost the generalization of its model before deploying it into production to maximize conversions of visits to purchases.
Which action is recommended to provide the HIGHEST accuracy model for the company’s test and validation data?

Increase the randomization of training data in the mini-batches used in training

Allocate a higher proportion of the overall data to the training dataset

Apply L1 or L2 regularization and dropouts to the training

Reduce the number of layers and units (or neurons) from the deep learning network

NEW QUESTION 42
A company is observing low accuracy while training on the default built-in image classification algorithm in Amazon SageMaker. The Data Science team wants to use an Inception neural network architecture instead of a ResNet architecture.
Which of the following will accomplish this? (Choose two.)

Customize the built-in image classification algorithm to use Inception and use this for model training.

Create a support case with the SageMaker team to change the default image classification algorithm to Inception.

Bundle a Docker container with TensorFlow Estimator loaded with an Inception network and use this for model training.

Use custom code in Amazon SageMaker with TensorFlow Estimator to load the model with an Inception network, and use this for model training.

Download and apt-get installthe inception network code into an Amazon EC2 instance and use this instance as a Jupyter notebook in Amazon SageMaker.

NEW QUESTION 43
You have trained a deep neural network model on Google Cloud. The model has low loss on the training data, but is performing worse on the validation dat a. You want the model to be resilient to overfitting. Which strategy should you use when retraining the model?

Apply a dropout parameter of 0 2, and decrease the learning rate by a factor of 10

Apply a 12 regularization parameter of 0.4, and decrease the learning rate by a factor of 10.

Run a hyperparameter tuning job on Al Platform to optimize for the L2 regularization and dropout parameters

Run a hyperparameter tuning job on Al Platform to optimize for the learning rate, and increase the number of neurons by a factor of 2.

NEW QUESTION 44
A Machine Learning Specialist is training a model to identify the make and model of vehicles in images. The Specialist wants to use transfer learning and an existing model trained on images of general objects. The Specialist collated a large custom dataset of pictures containing different vehicle makes and models.
What should the Specialist do to initialize the model to re-train it with the custom data?

Initialize the model with random weights in all layers including the last fully connected layer.

Initialize the model with pre-trained weights in all layers and replace the last fully connected layer.

Initialize the model with random weights in all layers and replace the last fully connected layer.

Initialize the model with pre-trained weights in all layers including the last fully connected layer.

NEW QUESTION 45
A Data Scientist needs to analyze employment data. The dataset contains approximately 10 million observations on people across 10 different features. During the preliminary analysis, the Data Scientist notices that income and age distributions are not normal. While income levels shows a right skew as expected, with fewer individuals having a higher income, the age distribution also show a right skew, with fewer older individuals participating in the workforce.
Which feature transformations can the Data Scientist apply to fix the incorrectly skewed data? (Choose two.)

Cross-validation

Numerical value binning

High-degree polynomial transformation

Logarithmic transformation

One hot encoding

NEW QUESTION 46
You are developing ML models with Al Platform for image segmentation on CT scans. You frequently update your model architectures based on the newest available research papers, and have to rerun training on the same dataset to benchmark their performance. You want to minimize computation costs and manual intervention while having version control for your code. What should you do?

Use Cloud Functions to identify changes to your code in Cloud Storage and trigger a retraining job

Use the gcloud command-line tool to submit training jobs on Al Platform when you update your code

Use Cloud Build linked with Cloud Source Repositories to trigger retraining when new code is pushed to the repository

Create an automated workflow in Cloud Composer that runs daily and looks for changes in code in Cloud Storage using a sensor.

NEW QUESTION 47
You want to rebuild your ML pipeline for structured data on Google Cloud. You are using PySpark to conduct data transformations at scale, but your pipelines are taking over 12 hours to run. To speed up development and pipeline run time, you want to use a serverless tool and SQL syntax. You have already moved your raw data into Cloud Storage. How should you build the pipeline on Google Cloud while meeting the speed and processing requirements?

Use Data Fusion’s GUI to build the transformation pipelines, and then write the data into BigQuery

Convert your PySpark into SparkSQL queries to transform the data and then run your pipeline on Dataproc to write the data into BigQuery.

Ingest your data into Cloud SQL convert your PySpark commands into SQL queries to transform the data, and then use federated queries from BigQuery for machine learning

Ingest your data into BigQuery using BigQuery Load, convert your PySpark commands into BigQuery SQL queries to transform the data, and then write the transformations to a new table

NEW QUESTION 48
A Machine Learning Specialist is working with a large cybersecurity company that manages security events in real time for companies around the world. The cybersecurity company wants to design a solution that will allow it to use machine learning to score malicious events as anomalies on the data as it is being ingested. The company also wants be able to save the results in its data lake for later processing and analysis.
What is the MOST efficient way to accomplish these tasks?

Ingest the data using Amazon Kinesis Data Firehose, and use Amazon Kinesis Data Analytics Random Cut Forest (RCF) for anomaly detection. Then use Kinesis Data Firehose to stream the results to Amazon S3.

Ingest the data into Apache Spark Streaming using Amazon EMR, and use Spark MLlib with k-means to perform anomaly detection. Then store the results in an Apache Hadoop Distributed File System (HDFS) using Amazon EMR with a replication factor of three as the data lake.

Ingest the data and store it in Amazon S3. Use AWS Batch along with the AWS Deep Learning AMIs to train a k-means model using TensorFlow on the data in Amazon S3.

Ingest the data and store it in Amazon S3. Have an AWS Glue job that is triggered on demand transform the new data. Then use the built-in Random Cut Forest (RCF) model within Amazon SageMaker to detect anomalies in the data.

NEW QUESTION 49
An online reseller has a large, multi-column dataset with one column missing 30% of its data. A Machine Learning Specialist believes that certain columns in the dataset could be used to reconstruct the missing data.
Which reconstruction approach should the Specialist use to preserve the integrity of the dataset?

Listwise deletion

Last observation carried forward

Multiple imputation

Mean substitution

NEW QUESTION 50
The displayed graph is from a forecasting model for testing a time series.

Considering the graph only, which conclusion should a Machine Learning Specialist make about the behavior of the model?

The model predicts both the trend and the seasonality well

The model predicts the trend well, but not the seasonality.

The model predicts the seasonality well, but not the trend.

The model does not predict the trend or the seasonality well.

NEW QUESTION 51
A real estate company wants to create a machine learning model for predicting housing prices based on a historical dataset. The dataset contains 32 features.
Which model will meet the business requirement?

Logistic regression

Linear regression

K-means

Principal component analysis (PCA)

NEW QUESTION 52
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?

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

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.

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.

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

NEW QUESTION 53
You are an ML engineer at a bank that has a mobile application. Management has asked you to build an ML-based biometric authentication for the app that verifies a customer’s identity based on their fingerprint. Fingerprints are considered highly sensitive personal information and cannot be downloaded and stored into the bank databases. Which learning strategy should you recommend to train and deploy this ML model?

Differential privacy

Federated learning

MD5 to encrypt data

Data Loss Prevention API

NEW QUESTION 54
You have trained a model on a dataset that required computationally expensive preprocessing operations. You need to execute the same preprocessing at prediction time. You deployed the model on Al Platform for high-throughput online prediction. Which architecture should you use?

Validate the accuracy of the model that you trained on preprocessed data
* Create a new model that uses the raw data and is available in real time
* Deploy the new model onto Al Platform for online prediction

Send incoming prediction requests to a Pub/Sub topic
* Transform the incoming data using a Dataflow job
* Submit a prediction request to Al Platform using the transformed data
* Write the predictions to an outbound Pub/Sub queue

Stream incoming prediction request data into Cloud Spanner
* Create a view to abstract your preprocessing logic.
* Query the view every second for new records
* Submit a prediction request to Al Platform using the transformed data
* Write the predictions to an outbound Pub/Sub queue.

Send incoming prediction requests to a Pub/Sub topic
* Set up a Cloud Function that is triggered when messages are published to the Pub/Sub topic.
* Implement your preprocessing logic in the Cloud Function
* Submit a prediction request to Al Platform using the transformed data
* Write the predictions to an outbound Pub/Sub queue

Understanding functional and technical aspects of Professional Machine Learning Engineer – Google ML Solution Architecture

The following will be discussed in Google Professional-Machine-Learning-Engineer exam dumps:

Automation
Identifying potential regulatory issues
Building secure ML systems
Automation of data preparation and model training/deployment
SDLC best practices
Design reliable, scalable, highly available ML solution
Design architecture that complies with regulatory and security concerns
Exploration/analysis
Selection of quotas and compute/accelerators with components
Serving
Optimizing data use and storage
Logging/management
Data connections
Privacy implications of data usage
Feature engineering
A variety of component types – data collection; data management

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New PremiumVCEDump Professional-Machine-Learning-Engineer Exam Questions Real Professional-Machine-Learning-Engineer Dumps Updated on Jun 05, 2022 [Q34-Q54]

Understanding functional and technical aspects of Professional Machine Learning Engineer – Google ML Solution Architecture

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Category Archives: Professional-Machine-Learning-Engineer

New PremiumVCEDump Professional-Machine-Learning-Engineer Exam Questions Real Professional-Machine-Learning-Engineer Dumps Updated on Jun 05, 2022 [Q34-Q54]

Understanding functional and technical aspects of Professional Machine Learning Engineer – Google ML Solution Architecture