Google Professional-Machine-Learning-Engineer Dumps Questions [2022] Pass for Professional-Machine-Learning-Engineer Exam
Updated Google Study Guide Professional-Machine-Learning-Engineer Dumps Questions
NEW QUESTION 25
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?
- A. Binomial distribution
- B. Normal distribution
- C. Poisson distribution
- D. Uniform distribution
Answer: A
NEW QUESTION 26
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. Reduce the value of the repeat parameter
- C. Set the prefetch option equal to the training batch size
- D. Increase the buffer size for the shuffle option.
- E. Decrease the batch size argument in your transformation
Answer: C,E
Explanation:
https://towardsdatascience.com/overcoming-data-preprocessing-bottlenecks-with-tensorflow-data-service-nvidia-dali-and-other-d6321917f851
NEW QUESTION 27
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-high-accuracy-1 model
- C. AutoML Vision model
- D. AutoML Vision Edge mobile-low-latency-1 model
Answer: C
NEW QUESTION 28
Your data science team has requested a system that supports scheduled model retraining, Docker containers, and a service that supports autoscaling and monitoring for online prediction requests. Which platform components should you choose for this system?
- A. Cloud Composer, Al Platform Training with custom containers , and App Engine
- B. Kubeflow Pipelines and Al Platform Prediction
- C. Kubeflow Pipelines and App Engine
- D. Cloud Composer, BigQuery ML , and Al Platform Prediction
Answer: B
NEW QUESTION 29
A retail chain has been ingesting purchasing records from its network of 20,000 stores to Amazon S3 using Amazon Kinesis Data Firehose. To support training an improved machine learning model, training records will require new but simple transformations, and some attributes will be combined. The model needs to be retrained daily.
Given the large number of stores and the legacy data ingestion, which change will require the LEAST amount of development effort?
- A. Insert an Amazon Kinesis Data Analytics stream downstream of the Kinesis Data Firehose stream that transforms raw record attributes into simple transformed values using SQL.
- B. Deploy an Amazon EMR cluster running Apache Spark with the transformation logic, and have the cluster run each day on the accumulating records in Amazon S3, outputting new/transformed records to Amazon S3.
- C. Require that the stores to switch to capturing their data locally on AWS Storage Gateway for loading into Amazon S3, then use AWS Glue to do the transformation.
- D. Spin up a fleet of Amazon EC2 instances with the transformation logic, have them transform the data records accumulating on Amazon S3, and output the transformed records to Amazon S3.
Answer: A
NEW QUESTION 30
A Data Science team within a large company uses Amazon SageMaker notebooks to access data stored in Amazon S3 buckets. The IT Security team is concerned that internet-enabled notebook instances create a security vulnerability where malicious code running on the instances could compromise data privacy. The company mandates that all instances stay within a secured VPC with no internet access, and data communication traffic must stay within the AWS network.
How should the Data Science team configure the notebook instance placement to meet these requirements?
- A. Associate the Amazon SageMaker notebook with a private subnet in a VPC. Ensure the VPC has a NAT gateway and an associated security group allowing only outbound connections to Amazon S3 and Amazon SageMaker.
- B. Associate the Amazon SageMaker notebook with a private subnet in a VPC. Ensure the VPC has S3 VPC endpoints and Amazon SageMaker VPC endpoints attached to it.
- C. Associate the Amazon SageMaker notebook with a private subnet in a VPC. Place the Amazon SageMaker endpoint and S3 buckets within the same VPC.
- D. Associate the Amazon SageMaker notebook with a private subnet in a VPC. Use IAM policies to grant access to Amazon S3 and Amazon SageMaker.
Answer: A
NEW QUESTION 31
A retail company intends to use machine learning to categorize new products. A labeled dataset of current products was provided to the Data Science team. The dataset includes 1,200 products. The labeled dataset has 15 features for each product such as title dimensions, weight, and price. Each product is labeled as belonging to one of six categories such as books, games, electronics, and movies.
Which model should be used for categorizing new products using the provided dataset for training?
- A. A deep convolutional neural network (CNN) with a softmax activation function for the last layer
- B. A regression forest where the number of trees is set equal to the number of product categories
- C. A DeepAR forecasting model based on a recurrent neural network (RNN)
- D. AnXGBoost model where the objective parameter is set to multi:softmax
Answer: A
NEW QUESTION 32
You recently joined a machine learning team that will soon release a new project. As a lead on the project, you are asked to determine the production readiness of the ML components. The team has already tested features and data, model development, and infrastructure. Which additional readiness check should you recommend to the team?
- A. Ensure that all hyperparameters are tuned
- B. Ensure that training is reproducible
- C. Ensure that model performance is monitored
- D. Ensure that feature expectations are captured in the schema
Answer: B
NEW QUESTION 33
A Machine Learning Specialist deployed a model that provides product recommendations on a company's website. Initially, the model was performing very well and resulted in customers buying more products on average. However, within the past few months, the Specialist has noticed that the effect of product recommendations has diminished and customers are starting to return to their original habits of spending less.
The Specialist is unsure of what happened, as the model has not changed from its initial deployment over a year ago.
Which method should the Specialist try to improve model performance?
- A. The model's hyperparameters should be periodically updated to prevent drift.
- B. The model should be periodically retrained using the original training data plus new data as product inventory changes.
- C. The model should be periodically retrained from scratch using the original data while adding a regularization term to handle product inventory changes
- D. The model needs to be completely re-engineered because it is unable to handle product inventory changes.
Answer: B
NEW QUESTION 34
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 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.
- B. 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
- C. Set up restrictive I AM permissions on the Al Platform notebooks so that only a single user or group can access a given instance.
- D. 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.
Answer: B
Explanation:
https://cloud.google.com/ai-platform/prediction/docs/resource-labels#overview_of_labels You can add labels to your AI Platform Prediction jobs, models, and model versions, then use those labels to organize resources into categories when viewing or monitoring the resources. For example, you can label jobs by team (such as engineering or research) and development phase (prod or test), then filter the jobs based on the team and phase. Labels are also available on operations, but these labels are derived from the resource to which the operation applies. You cannot add or update labels on an operation.
https://cloud.google.com/ai-platform/prediction/docs/sharing-models.
NEW QUESTION 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?
- A. Use the last relevant week of data as a validation set to ensure that your model is performing accurately on current data
- B. Use k-fold cross-validation as a validation strategy to ensure that your model is ready for production.
- C. Use the TFX ModelValidator tools to specify performance metrics for production readiness
- D. Use the entire dataset and treat the area under the receiver operating characteristics curve (AUC ROC) as the main metric.
Answer: C
Explanation:
https://www.tensorflow.org/tfx/guide/evaluator
NEW QUESTION 36
You work for a large technology company that wants to modernize their contact center. You have been asked to develop a solution to classify incoming calls by product so that requests can be more quickly routed to the correct support team. You have already transcribed the calls using the Speech-to-Text API. You want to minimize data preprocessing and development time. How should you build the model?
- A. Build a custom model to identify the product keywords from the transcribed calls, and then run the keywords through a classification algorithm
- B. Use the Al Platform Training built-in algorithms to create a custom model
- C. Use AutoML Natural Language to extract custom entities for classification
- D. Use the Cloud Natural Language API to extract custom entities for classification
Answer: C
NEW QUESTION 37
You work with a data engineering team that has developed a pipeline to clean your dataset and save it in a Cloud Storage bucket. You have created an ML model and want to use the data to refresh your model as soon as new data is available. As part of your CI/CD workflow, you want to automatically run a Kubeflow Pipelines training job on Google Kubernetes Engine (GKE). How should you architect this workflow?
- A. Use App Engine to create a lightweight python client that continuously polls Cloud Storage for new files As soon as a file arrives, initiate the training job
- B. Configure your pipeline with Dataflow, which saves the files in Cloud Storage After the file is saved, start the training job on a GKE cluster
- C. Configure a Cloud Storage trigger to send a message to a Pub/Sub topic when a new file is available in a storage bucket. Use a Pub/Sub-triggered Cloud Function to start the training job on a GKE cluster
- D. Use Cloud Scheduler to schedule jobs at a regular interval. For the first step of the job. check the timestamp of objects in your Cloud Storage bucket If there are no new files since the last run, abort the job.
Answer: B
NEW QUESTION 38
A logistics company needs a forecast model to predict next month's inventory requirements for a single item in
10 warehouses. A machine learning specialist uses Amazon Forecast to develop a forecast model from 3 years of monthly data. There is no missing data. The specialist selects the DeepAR+ algorithm to train a predictor. The predictor means absolute percentage error (MAPE) is much larger than the MAPE produced by the current human forecasters.
Which changes to the CreatePredictor API call could improve the MAPE? (Choose two.)
- A. Set ForecastHorizon to 4.
- B. Set PerformAutoML to true.
- C. Set FeaturizationMethodName to filling.
- D. Set ForecastFrequency to W for weekly.
- E. Set PerformHPO to true.
Answer: D,E
Explanation:
Explanation/Reference: https://docs.aws.amazon.com/forecast/latest/dg/forecast.dg.pdf
NEW QUESTION 39
A Machine Learning Specialist wants to bring a custom algorithm to Amazon SageMaker. The Specialist implements the algorithm in a Docker container supported by Amazon SageMaker.
How should the Specialist package the Docker container so that Amazon SageMaker can launch the training correctly?
- A. Copy the training program to directory /opt/ml/train
- B. Configure the training program as an ENTRYPOINTnamed train
- C. Use CMD configin the Dockerfile to add the training program as a CMD of the image
- D. Modify the bash_profile file in the container and add a bashcommand to start the training program
Answer: C
NEW QUESTION 40
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. Reinforcement Learning
- C. Classification
- D. Convolutional Neural Networks (CNN)
Answer: B
NEW QUESTION 41
You are responsible for building a unified analytics environment across a variety of on-premises data marts. Your company is experiencing data quality and security challenges when integrating data across the servers, caused by the use of a wide range of disconnected tools and temporary solutions. You need a fully managed, cloud-native data integration service that will lower the total cost of work and reduce repetitive work. Some members on your team prefer a codeless interface for building Extract, Transform, Load (ETL) process. Which service should you use?
- A. Apache Flink
- B. Dataprep
- C. Dataflow
- D. Cloud Data Fusion
Answer: D
NEW QUESTION 42
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. Deploy the model on Al Platform and create a version of it for online inference.
- B. Create a serving pipeline in Compute Engine for prediction
- C. Use the batch prediction functionality of Al Platform
- D. Use Cloud Functions for prediction each time a new data point is ingested
Answer: C
Explanation:
https://cloud.google.com/ai-platform/prediction/docs/batch-predict
NEW QUESTION 43
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