[Oct 23, 2021] Professional-Machine-Learning-Engineer Sample with Accurate & Updated Questions

Professional-Machine-Learning-Engineer Exam Info and Free Practice Test | TestSimulate

Understanding functional and technical aspects of Professional Machine Learning Engineer - Google ML Pipeline Automation & Orchestration

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

Design pipeline. Considerations include:

• Use CI/CD to test and deploy models
• Decoupling components with Cloud Build
• Identification of components, parameters, triggers, and compute needs
• Google Cloud serving options
• Organization and tracking experiments and pipeline runs
• Orchestration framework
• Model binary options
• Hooking models into existing CI/CD deployment system
• A/B and canary testing
• Testing for target performance
• Track and audit metadata
• Tuning compute performance
• Model/dataset lineage
• Performing data validation
• Constructing and testing of parameterized pipeline definition in SDK
• Storing data and generated artifacts
• Implement training pipeline
• Setup of trigger and pipeline schedule
• Implement serving pipeline
• Hybrid or multi-cloud strategies
• Hooking into model and dataset versioning

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NEW QUESTION 13
A Machine Learning Specialist at a company sensitive to security is preparing a dataset for model training. The dataset is stored in Amazon S3 and contains Personally Identifiable Information (PII).
The dataset:
* Must be accessible from a VPC only.
* Must not traverse the public internet.
How can these requirements be satisfied?

• A. Create a VPC endpoint and use Network Access Control Lists (NACLs) to allow traffic between only the given VPC endpoint and an Amazon EC2 instance.
• B. Create a VPC endpoint and apply a bucket access policy that restricts access to the given VPC endpoint and the VPC.
• C. Create a VPC endpoint and apply a bucket access policy that allows access from the given VPC endpoint and an Amazon EC2 instance.
• D. Create a VPC endpoint and use security groups to restrict access to the given VPC endpoint and an Amazon EC2 instance

Answer: B

 

NEW QUESTION 14
Your team is building an application for a global bank that will be used by millions of customers. You built a forecasting model that predicts customers1 account balances 3 days in the future. Your team will use the results in a new feature that will notify users when their account balance is likely to drop below $25. How should you serve your predictions?

• A. 1. Build a notification system on Firebase
  2. Register each user with a user ID on the Firebase Cloud Messaging server, which sends a notification when the average of all account balance predictions drops below the $25 threshold
• B. 1 Build a notification system on Firebase
  2. Register each user with a user ID on the Firebase Cloud Messaging server, which sends a notification when your model predicts that a user's account balance will drop below the $25 threshold
• C. 1. Create a Pub/Sub topic for each user
  2. Deploy an application on the App Engine standard environment that sends a notification when your model predicts that a user's account balance will drop below the $25 threshold
• D. 1. Create a Pub/Sub topic for each user
  2 Deploy a Cloud Function that sends a notification when your model predicts that a user's account balance will drop below the $25 threshold.

Answer: D

 

NEW QUESTION 15
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. Use the batch prediction functionality of Al Platform
• B. Use Cloud Functions for prediction each time a new data point is ingested
• C. Deploy the model on Al Platform and create a version of it for online inference.
• D. Create a serving pipeline in Compute Engine for prediction

Answer: C

 

NEW QUESTION 16
A retail company is using Amazon Personalize to provide personalized product recommendations for its customers during a marketing campaign. The company sees a significant increase in sales of recommended items to existing customers immediately after deploying a new solution version, but these sales decrease a short time after deployment. Only historical data from before the marketing campaign is available for training.
How should a data scientist adjust the solution?

• A. Add user metadata and use the HRNN-Metadata recipe in Amazon Personalize.
• B. Use the event tracker in Amazon Personalize to include real-time user interactions.
• C. Implement a new solution using the built-in factorization machines (FM) algorithm in Amazon SageMaker.
• D. Add event type and event value fields to the interactions dataset in Amazon Personalize.

Answer: D

 

NEW QUESTION 17
A company is setting up an Amazon SageMaker environment. The corporate data security policy does not allow communication over the internet.
How can the company enable the Amazon SageMaker service without enabling direct internet access to Amazon SageMaker notebook instances?

• A. Create VPC peering with Amazon VPC hosting Amazon SageMaker.
• B. Route Amazon SageMaker traffic through an on-premises network.
• C. Create Amazon SageMaker VPC interface endpoints within the corporate VPC.
• D. Create a NAT gateway within the corporate VPC.

Answer: D

Explanation:
Explanation/Reference: https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-dg.pdf (46)

 

NEW QUESTION 18
A technology startup is using complex deep neural networks and GPU compute to recommend the company's products to its existing customers based upon each customer's habits and interactions. The solution currently pulls each dataset from an Amazon S3 bucket before loading the data into a TensorFlow model pulled from the company's Git repository that runs locally. This job then runs for several hours while continually outputting its progress to the same S3 bucket. The job can be paused, restarted, and continued at any time in the event of a failure, and is run from a central queue.
Senior managers are concerned about the complexity of the solution's resource management and the costs involved in repeating the process regularly. They ask for the workload to be automated so it runs once a week, starting Monday and completing by the close of business Friday.
Which architecture should be used to scale the solution at the lowest cost?

• A. Implement the solution using AWS Deep Learning Containers, run the workload using AWS Fargate running on Spot Instances, and then schedule the task using the built-in task scheduler
• B. Implement the solution using AWS Deep Learning Containers and run the container as a job using AWS Batch on a GPU-compatible Spot Instance
• C. Implement the solution using a low-cost GPU-compatible Amazon EC2 instance and use the AWS Instance Scheduler to schedule the task
• D. Implement the solution using Amazon ECS running on Spot Instances and schedule the task using the ECS service scheduler

Answer: A

 

NEW QUESTION 19
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. Split the training and test data based on time rather than a random split to avoid leakage
• B. Normalize the data for the training, and test datasets as two separate steps.
• C. Apply data transformations before splitting, and cross-validate to make sure that the transformations are applied to both the training and test sets.
• D. Add more data to your test set to ensure that you have a fair distribution and sample for testing

Answer: C

 

NEW QUESTION 20
A data scientist needs to identify fraudulent user accounts for a company's ecommerce platform. The company wants the ability to determine if a newly created account is associated with a previously known fraudulent user.
The data scientist is using AWS Glue to cleanse the company's application logs during ingestion.
Which strategy will allow the data scientist to identify fraudulent accounts?

• A. Create a FindMatches machine learning transform in AWS Glue.
• B. Create an AWS Glue crawler to infer duplicate accounts in the source data.
• C. Execute the built-in FindDuplicates Amazon Athena query.
• D. Search for duplicate accounts in the AWS Glue Data Catalog.

Answer: A

Explanation:
Explanation/Reference: https://docs.aws.amazon.com/glue/latest/dg/machine-learning.html

 

NEW QUESTION 21
Machine Learning Specialist is building a model to predict future employment rates based on a wide range of economic factors. While exploring the data, the Specialist notices that the magnitude of the input features vary greatly. The Specialist does not want variables with a larger magnitude to dominate the model.
What should the Specialist do to prepare the data for model training?

• A. Apply the orthogonal sparse bigram (OSB) transformation to apply a fixed-size sliding window to generate new features of a similar magnitude.
• B. Apply normalization to ensure each field will have a mean of 0 and a variance of 1 to remove any significant magnitude.
• C. Apply quantile binning to group the data into categorical bins to keep any relationships in the data by replacing the magnitude with distribution.
• D. Apply the Cartesian product transformation to create new combinations of fields that are independent of the magnitude.

Answer: B

Explanation:
Explanation/Reference: https://docs.aws.amazon.com/machine-learning/latest/dg/data-transformations-reference.html

 

NEW QUESTION 22
Your team is building an application for a global bank that will be used by millions of customers. You built a forecasting model that predicts customers1 account balances 3 days in the future. Your team will use the results in a new feature that will notify users when their account balance is likely to drop below $25. How should you serve your predictions?

• A. 1. Build a notification system on Firebase
  2. Register each user with a user ID on the Firebase Cloud Messaging server, which sends a notification when the average of all account balance predictions drops below the $25 threshold
• B. 1 Build a notification system on Firebase
  2. Register each user with a user ID on the Firebase Cloud Messaging server, which sends a notification when your model predicts that a user's account balance will drop below the $25 threshold
• C. 1. Create a Pub/Sub topic for each user
  2 Deploy a Cloud Function that sends a notification when your model predicts that a user's account balance will drop below the $25 threshold.
• D. 1. Create a Pub/Sub topic for each user
  2. Deploy an application on the App Engine standard environment that sends a notification when your model predicts that a user's account balance will drop below the $25 threshold

Answer: D

 

NEW QUESTION 23
You have deployed multiple versions of an image classification model on Al Platform. You want to monitor the performance of the model versions overtime. How should you perform this comparison?

• A. Compare the mean average precision across the models using the Continuous Evaluation feature
• B. Compare the loss performance for each model on the validation data
• C. Compare the loss performance for each model on a held-out dataset.
• D. Compare the receiver operating characteristic (ROC) curve for each model using the What-lf Tool

Answer: B

 

NEW QUESTION 24
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. Recurrent Neural Networks (RNN)
• B. Convolutional Neural Networks (CNN)
• C. Classification
• D. Reinforcement Learning

Answer: D

 

NEW QUESTION 25
A Machine Learning Specialist is required to build a supervised image-recognition model to identify a cat. The ML Specialist performs some tests and records the following results for a neural network-based image classifier:
Total number of images available = 1,000
Test set images = 100 (constant test set)
The ML Specialist notices that, in over 75% of the misclassified images, the cats were held upside down by their owners.
Which techniques can be used by the ML Specialist to improve this specific test error?

• A. Increase the dropout rate for the second-to-last layer.
• B. Increase the number of layers for the neural network.
• C. Increase the training data by adding variation in rotation for training images.
• D. Increase the number of epochs for model training

Answer: D

 

NEW QUESTION 26
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 Linear Learner algorithm on the single time series consisting of the full year of data with a predictor_typeof classifier.
• 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 k-Nearest-Neighbors (kNN) 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 regressor.

Answer: D

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 27
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. Convert your PySpark into SparkSQL queries to transform the data and then run your pipeline on Dataproc to write the data into BigQuery.
• 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. Use Data Fusion's GUI to build the transformation pipelines, and then write the data into BigQuery
• D. 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

Answer: A

 

NEW QUESTION 28
A Machine Learning Specialist is designing a system for improving sales for a company. The objective is to use the large amount of information the company has on users' behavior and product preferences to predict which products users would like based on the users' similarity to other users.
What should the Specialist do to meet this objective?

• A. Build a content-based filtering recommendation engine with Apache Spark ML on Amazon EMR
• B. Build a combinative filtering recommendation engine with Apache Spark ML on Amazon EMR
• C. Build a collaborative filtering recommendation engine with Apache Spark ML on Amazon EMR.
• D. Build a model-based filtering recommendation engine with Apache Spark ML on Amazon EMR

Answer: C

Explanation:
Many developers want to implement the famous Amazon model that was used to power the "People who bought this also bought these items" feature on Amazon.com. This model is based on a method called Collaborative Filtering. It takes items such as movies, books, and products that were rated highly by a set of users and recommending them to other users who also gave them high ratings. This method works well in domains where explicit ratings or implicit user actions can be gathered and analyzed.
Reference: https://aws.amazon.com/blogs/big-data/building-a-recommendation-engine-with-spark-ml-on-amazon-emr-using-zeppelin/

 

NEW QUESTION 29
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 Glue ETL job, and an AWS Glue Data catalog to search and discover metadata.
• C. 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.
• D. 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.

Answer: C

 

NEW QUESTION 30
A Mobile Network Operator is building an analytics platform to analyze and optimize a company's operations using Amazon Athena and Amazon S3.
The source systems send data in .CSV format in real time. The Data Engineering team wants to transform the data to the Apache Parquet format before storing it on Amazon S3.
Which solution takes the LEAST effort to implement?

• A. Ingest .CSV data from Amazon Kinesis Data Streams and use Amazon Glue to convert data into Parquet.
• B. Ingest .CSV data using Apache Kafka Streams on Amazon EC2 instances and use Kafka Connect S3 to serialize data as Parquet
• C. Ingest .CSV data from Amazon Kinesis Data Streams and use Amazon Kinesis Data Firehose to convert data into Parquet.
• D. Ingest .CSV data using Apache Spark Structured Streaming in an Amazon EMR cluster and use Apache Spark to convert data into Parquet.

Answer: A

Explanation:
Explanation/Reference:

 

NEW QUESTION 31
You are developing models to classify customer support emails. You created models with TensorFlow Estimators using small datasets on your on-premises system, but you now need to train the models using large datasets to ensure high performance. You will port your models to Google Cloud and want to minimize code refactoring and infrastructure overhead for easier migration from on-prem to cloud. What should you do?

• A. Use Kubeflow Pipelines to train on a Google Kubernetes Engine cluster.
• B. Create a cluster on Dataproc for training
• C. Create a Managed Instance Group with autoscaling
• D. Use Al Platform for distributed training

Answer: A

 

NEW QUESTION 32
You are training a Resnet model on Al Platform using TPUs to visually categorize types of defects in automobile engines. You capture the training profile using the Cloud TPU profiler plugin and observe that it is highly input-bound. You want to reduce the bottleneck and speed up your model training process. Which modifications should you make to the tf .data dataset?
Choose 2 answers

• A. Use the interleave option for reading data
• B. Decrease the batch size argument in your transformation
• C. Increase the buffer size for the shuffle option.
• D. Reduce the value of the repeat parameter
• E. Set the prefetch option equal to the training batch size

Answer: A,E

 

NEW QUESTION 33
A company is using Amazon Textract to extract textual data from thousands of scanned text-heavy legal documents daily. The company uses this information to process loan applications automatically. Some of the documents fail business validation and are returned to human reviewers, who investigate the errors. This activity increases the time to process the loan applications.
What should the company do to reduce the processing time of loan applications?

• A. Use an Amazon Textract synchronous operation instead of an asynchronous operation.
• B. Configure Amazon Textract to route low-confidence predictions to Amazon SageMaker Ground Truth.
  Perform a manual review on those words before performing a business validation.
• C. Use Amazon Rekognition's feature to detect text in an image to extract the data from scanned images. Use this information to process the loan applications.
• D. Configure Amazon Textract to route low-confidence predictions to Amazon Augmented AI (Amazon A2I).
  Perform a manual review on those words before performing a business validation.

Answer: D

 

NEW QUESTION 34
You have trained a text classification model in TensorFlow using Al Platform. You want to use the trained model for batch predictions on text data stored in BigQuery while minimizing computational overhead. What should you do?

• A. Deploy and version the model on Al Platform.
• B. Use Dataflow with the SavedModel to read the data from BigQuery
• C. Submit a batch prediction job on Al Platform that points to the model location in Cloud Storage.
• D. Export the model to BigQuery ML.

Answer: D

 

NEW QUESTION 35
......

What is the duration, language, and format of Professional Machine Learning Engineer - Google

• Language of Exam: English, Japanese, Korean
• Duration of Exam: 120 minutes
• No negative marking for wrong answers
• Type of Questions: Multiple choice (MCQs), multiple answers

 

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