[Oct-2021] Professional-Machine-Learning-Engineer exam torrent Google study guide
Use Valid New Professional-Machine-Learning-Engineer Test Notes & Professional-Machine-Learning-Engineer Valid Exam Guide
What is the duration, language, and format of Professional Machine Learning Engineer - Google
- Type of Questions: Multiple choice (MCQs), multiple answers
- Language of Exam: English, Japanese, Korean
- Duration of Exam: 120 minutes
- No negative marking for wrong answers
How to study the Professional Machine Learning Engineer - Google
A broad range of Google Professional-Machine-Learning-Engineer dumps for Google accredited Developer Certification have been recognized for certification issues. The reality that students need to prepare attentively does not make certificates easy. It also takes a long time to learn from Google accredited Developer. Every exam includes answers and questions that help students pass their final test. You will pass the test after you have taken and learned our modules. But it doesn’t end there; thanks to our full guides, you will still be good in your career. You will produce your goods in the future. To plan any material for you, we have an advanced method. In the development of and commodity, we have used the latest details.
We offer a terrific research study review and fantastic solutions for any type of expert who intends to take accreditation testing on the first initiative. By taking the training product developed by our professionals, you will have the opportunity to pass the exams in the very first effort. We provide a 100% guarantee of success and we are positive that you will certainly do well Prep4away is among the relied on, validated as well as valued sites giving its clients on the internet with extremely comprehensive and relevant online exam preparation items. Prep4away offers every little thing you need to pass the qualification test. If you are seeking a certification and are unsuccessful, currently is the moment for you to try what we provide. Google Professional-Machine-Learning-Engineer practice test and Google Professional-Machine-Learning-Engineer practice exam is easy to use, so that anyone can appreciate them.
NEW QUESTION 28
A Machine Learning Specialist uploads a dataset to an Amazon S3 bucket protected with server-side encryption using AWS KMS.
How should the ML Specialist define the Amazon SageMaker notebook instance so it can read the same dataset from Amazon S3?
- A. Define security group(s) to allow all HTTP inbound/outbound traffic and assign those security group(s) to the Amazon SageMaker notebook instance.
- B. Assign the same KMS key used to encrypt data in Amazon S3 to the Amazon SageMaker notebook instance.
- C. Сonfigure the Amazon SageMaker notebook instance to have access to the VPC. Grant permission in the KMS key policy to the notebook's KMS role.
- D. Assign an IAM role to the Amazon SageMaker notebook with S3 read access to the dataset. Grant permission in the KMS key policy to that role.
Answer: B
Explanation:
Explanation/Reference: https://docs.aws.amazon.com/sagemaker/latest/dg/encryption-at-rest.html
NEW QUESTION 29
You need to build classification workflows over several structured datasets currently stored in BigQuery. Because you will be performing the classification several times, you want to complete the following steps without writing code: exploratory data analysis, feature selection, model building, training, and hyperparameter tuning and serving. What should you do?
- A. Use Al Platform Notebooks to run the classification model with pandas library
- B. Use Al Platform to run the classification model job configured for hyperparameter tuning
- C. Configure AutoML Tables to perform the classification task
- D. Run a BigQuery ML task to perform logistic regression for the classification
Answer: D
Explanation:
BigQuery ML supports supervised learning with the logistic regression model type.
NEW QUESTION 30
A company is using Amazon Polly to translate plaintext documents to speech for automated company announcements. However, company acronyms are being mispronounced in the current documents.
How should a Machine Learning Specialist address this issue for future documents?
- A. Convert current documents to SSML with pronunciation tags.
- B. Create an appropriate pronunciation lexicon.
- C. Output speech marks to guide in pronunciation.
- D. Use Amazon Lex to preprocess the text files for pronunciation
Answer: A
Explanation:
Explanation/Reference: https://docs.aws.amazon.com/polly/latest/dg/ssml.html
NEW QUESTION 31
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.)
- A. Bundle a Docker container with TensorFlow Estimator loaded with an Inception network and use this for model training.
- B. Create a support case with the SageMaker team to change the default image classification algorithm to Inception.
- C. Use custom code in Amazon SageMaker with TensorFlow Estimator to load the model with an Inception network, and use this for model training.
- D. Customize the built-in image classification algorithm to use Inception and use this for model training.
- E. Download and apt-get installthe inception network code into an Amazon EC2 instance and use this instance as a Jupyter notebook in Amazon SageMaker.
Answer: C,D
NEW QUESTION 32
A Machine Learning team uses Amazon SageMaker to train an Apache MXNet handwritten digit classifier model using a research dataset. The team wants to receive a notification when the model is overfitting.
Auditors want to view the Amazon SageMaker log activity report to ensure there are no unauthorized API calls.
What should the Machine Learning team do to address the requirements with the least amount of code and fewest steps?
- A. Use AWS CloudTrail to log Amazon SageMaker API calls to Amazon S3. Add code to push a custom metric to Amazon CloudWatch. Create an alarm in CloudWatch with Amazon SNS to receive a notification when the model is overfitting.
- B. Implement an AWS Lambda function to log Amazon SageMaker API calls to AWS CloudTrail. Add code to push a custom metric to Amazon CloudWatch. Create an alarm in CloudWatch with Amazon SNS to receive a notification when the model is overfitting.
- C. Use AWS CloudTrail to log Amazon SageMaker API calls to Amazon S3. Set up Amazon SNS to receive a notification when the model is overfitting
- D. Implement an AWS Lambda function to log Amazon SageMaker API calls to Amazon S3. Add code to push a custom metric to Amazon CloudWatch. Create an alarm in CloudWatch with Amazon SNS to receive a notification when the model is overfitting.
Answer: B
NEW QUESTION 33
A Machine Learning Specialist working for an online fashion company wants to build a data ingestion solution for the company's Amazon S3-based data lake.
The Specialist wants to create a set of ingestion mechanisms that will enable future capabilities comprised of:
* Real-time analytics
* Interactive analytics of historical data
* Clickstream analytics
* Product recommendations
Which services should the Specialist use?
- A. AWS Glue as the data catalog; Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for historical data insights; Amazon Kinesis Data Firehose for delivery to Amazon ES for clickstream analytics; Amazon EMR to generate personalized product recommendations
- B. Amazon Athena as the data catalog; Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for historical data insights; Amazon DynamoDB streams for clickstream analytics; AWS Glue to generate personalized product recommendations
- C. AWS Glue as the data catalog; Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for real- time data insights; Amazon Kinesis Data Firehose for delivery to Amazon ES for clickstream analytics; Amazon EMR to generate personalized product recommendations
- D. Amazon Athena as the data catalog: Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for near-real-time data insights; Amazon Kinesis Data Firehose for clickstream analytics; AWS Glue to generate personalized product recommendations
Answer: C
Explanation:
Explanation
NEW QUESTION 34
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?
- A. 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
- B. 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
- C. Convert your PySpark into SparkSQL queries to transform the data and then run your pipeline on Dataproc to write the data into BigQuery.
- D. Use Data Fusion's GUI to build the transformation pipelines, and then write the data into BigQuery
Answer: C
NEW QUESTION 35
A Data Scientist wants to gain real-time insights into a data stream of GZIP files.
Which solution would allow the use of SQL to query the stream with the LEAST latency?
- A. AWS Glue with a custom ETL script to transform the data.
- B. An Amazon Kinesis Client Library to transform the data and save it to an Amazon ES cluster.
- C. Amazon Kinesis Data Firehose to transform the data and put it into an Amazon S3 bucket.
- D. Amazon Kinesis Data Analytics with an AWS Lambda function to transform the data.
Answer: D
Explanation:
Explanation/Reference: https://aws.amazon.com/big-data/real-time-analytics-featured-partners/
NEW QUESTION 36
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. Decrease the size of the training batch
- B. Decrease the learning rate hyperparameter
- C. Increase the learning rate hyperparameter
- D. Increase the size of the training batch
Answer: C
NEW QUESTION 37
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 Edge mobile-versatile-1 model
- B. AutoML Vision Edge mobile-low-latency-1 model
- C. AutoML Vision model
- D. AutoML Vision Edge mobile-high-accuracy-1 model
Answer: C
NEW QUESTION 38
A city wants to monitor its air quality to address the consequences of air pollution. A Machine Learning Specialist needs to forecast the air quality in parts per million of contaminates for the next 2 days in the city. As this is a prototype, only daily data from the last year is available.
Which model is MOST likely to provide the best results in Amazon SageMaker?
- A. Use the Amazon SageMaker k-Nearest-Neighbors (kNN) algorithm on the single time series consisting of the full year of data with a predictor_typeof regressor.
- B. Use Amazon SageMaker Random Cut Forest (RCF) on the single time series consisting of the full year of data.
- C. Use the Amazon SageMaker Linear Learner algorithm on the single time series consisting of the full year of data with a predictor_typeof regressor.
- D. Use the Amazon SageMaker Linear Learner algorithm on the single time series consisting of the full year of data with a predictor_typeof classifier.
Answer: C
Explanation:
Explanation/Reference: https://aws.amazon.com/blogs/machine-learning/build-a-model-to-predict-the-impact-of-weather- on-urban-air-quality-using-amazon-sagemaker/?ref=Welcome.AI
NEW QUESTION 39
Your company manages a video sharing website where users can watch and upload videos. You need to create an ML model to predict which newly uploaded videos will be the most popular so that those videos can be prioritized on your company's website.
Which result should you use to determine whether the model is successful?
- A. The model predicts 97.5% of the most popular clickbait videos measured by number of clicks.
- B. The model predicts videos as popular if the user who uploads them has over 10,000 likes.
- C. The Pearson correlation coefficient between the log-transformed number of views after 7 days and 30 days after publication is equal to 0.
- D. The model predicts 95% of the most popular videos measured by watch time within 30 days of being uploaded.
Answer: D
NEW QUESTION 40
You need to build classification workflows over several structured datasets currently stored in BigQuery. Because you will be performing the classification several times, you want to complete the following steps without writing code: exploratory data analysis, feature selection, model building, training, and hyperparameter tuning and serving. What should you do?
- A. Use Al Platform Notebooks to run the classification model with pandas library
- B. Run a BigQuery ML task to perform logistic regression for the classification
- C. Use Al Platform to run the classification model job configured for hyperparameter tuning
- D. Configure AutoML Tables to perform the classification task
Answer: A
NEW QUESTION 41
Your team has been tasked with creating an ML solution in Google Cloud to classify support requests for one of your platforms. You analyzed the requirements and decided to use TensorFlow to build the classifier so that you have full control of the model's code, serving, and deployment. You will use Kubeflow pipelines for the ML platform. To save time, you want to build on existing resources and use managed services instead of building a completely new model. How should you build the classifier?
- A. Use an established text classification model on Al Platform as-is to classify support requests
- B. Use the Natural Language API to classify support requests
- C. Use AutoML Natural Language to build the support requests classifier
- D. Use an established text classification model on Al Platform to perform transfer learning
Answer: A
NEW QUESTION 42
A financial services company is building a robust serverless data lake on Amazon S3. The data lake should be flexible and meet the following requirements:
* Support querying old and new data on Amazon S3 through Amazon Athena and Amazon Redshift Spectrum.
* Support event-driven ETL pipelines
* Provide a quick and easy way to understand metadata
Which approach meets these requirements?
- A. Use an AWS Glue crawler to crawl S3 data, an Amazon CloudWatch alarm to trigger an AWS Batch job, and an AWS Glue Data Catalog to search and discover metadata.
- B. Use an AWS Glue crawler to crawl S3 data, an AWS Lambda function to trigger an AWS Batch job, and an external Apache Hive metastore to search and discover metadata.
- C. Use an AWS Glue crawler to crawl S3 data, an Amazon CloudWatch alarm to trigger an AWS Glue ETL job, and an external Apache Hive metastore to search and discover metadata.
- D. Use an AWS Glue crawler to crawl S3 data, an AWS Lambda function to trigger an AWS Glue ETL job, and an AWS Glue Data catalog to search and discover metadata.
Answer: D
NEW QUESTION 43
......
How to Prepare For Professional Machine Learning Engineer - Google
Preparation Guide for Professional Machine Learning Engineer - Google
Introduction for Professional Machine Learning Engineer - Google
A Professional Machine Learning Engineer designs, builds, and productionizes ML models to solve business challenges using Google Cloud technologies and knowledge of proven ML models and techniques. The ML Engineer is proficient in all aspects of model architecture, data pipeline interaction, and metrics interpretation and needs familiarity with application development, infrastructure management, data engineering, and security.
The Professional Machine Learning Engineer exam assesses your ability to:
- Architect ML solutions
- Monitor, optimize, and maintain ML solutions
- Prepare and process data
- Automate & orchestrate ML pipelines
- Frame ML problems
- Develop ML models
We prepare Google Professional-Machine-Learning-Engineer practice exams and Google Professional-Machine-Learning-Engineer practice tests to prepare you for all these requirements.
Professional-Machine-Learning-Engineer Exam questions and answers: https://www.prep4away.com/Google-certification/braindumps.Professional-Machine-Learning-Engineer.ete.file.html
Professional-Machine-Learning-Engineer Actual Questions Answers PDF 100% Cover Real Exam Questions: https://drive.google.com/open?id=1Hs1jz9xU7No_xIfyKigB3xpSNjK2friQ