[Oct 23, 2021] Professional-Machine-Learning-Engineer Dumps Full Questions - Exam Study Guide
Google Certification Free Certification Exam Material from ExamsLabs with 72 Questions
NEW QUESTION 24
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?
- A. Increase the number of false positives
- B. Decrease the recall.
- C. Decrease the number of false negatives
- D. Increase the recall
Answer: C
NEW QUESTION 25
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 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.
- B. 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
- C. 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
- D. 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
Answer: B
NEW QUESTION 26
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. Use IAM policies to grant access to Amazon S3 and Amazon SageMaker.
- D. Associate the Amazon SageMaker notebook with a private subnet in a VPC. Place the Amazon SageMaker endpoint and S3 buckets within the same VPC.
Answer: B
NEW QUESTION 27
You are an ML engineer at a global car manufacturer. You need to build an ML model to predict car sales in different cities around the world. Which features or feature crosses should you use to train city-specific relationships between car type and number of sales?
- A. One feature obtained as an element-wise product between binned latitude, binned longitude, and one-hot encoded car type
- B. One feature obtained as an element-wise product between latitude, longitude, and car type
- C. Two feature crosses as a element-wise product the first between binned latitude and one-hot encoded car type, and the second between binned longitude and one-hot encoded car type
- D. Three individual features binned latitude, binned longitude, and one-hot encoded car type
Answer: A
NEW QUESTION 28
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?
- A. Significantly increase the max_batch_size TensorFlow Serving parameter
- B. Switch to the tensorflow-model-server-universal version of TensorFlow Serving
- C. Significantly increase the max_enqueued_batches TensorFlow Serving parameter
- D. Recompile TensorFlow Serving using the source to support CPU-specific optimizations Instruct GKE to choose an appropriate baseline minimum CPU platform for serving nodes
Answer: A
NEW QUESTION 29
You have a functioning end-to-end ML pipeline that involves tuning the hyperparameters of your ML model using Al Platform, and then using the best-tuned parameters for training. Hypertuning is taking longer than expected and is delaying the downstream processes. You want to speed up the tuning job without significantly compromising its effectiveness. Which actions should you take?
Choose 2 answers
- A. Decrease the range of floating-point values
- B. Change the search algorithm from Bayesian search to random search.
- C. Decrease the number of parallel trials
- D. Set the early stopping parameter to TRUE
- E. Decrease the maximum number of trials during subsequent training phases.
Answer: A,B
NEW QUESTION 30
An interactive online dictionary wants to add a widget that displays words used in similar contexts. A Machine Learning Specialist is asked to provide word features for the downstream nearest neighbor model powering the widget.
What should the Specialist do to meet these requirements?
- A. Create word embedding vectors that store edit distance with every other word.
- B. Create one-hot word encoding vectors.
- C. Download word embeddings pre-trained on a large corpus.
- D. Produce a set of synonyms for every word using Amazon Mechanical Turk.
Answer: B
Explanation:
Explanation/Reference: https://aws.amazon.com/blogs/machine-learning/amazon-sagemaker-object2vec-adds-new- features-that-support-automatic-negative-sampling-and-speed-up-training/
NEW QUESTION 31
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. Use the Cloud Natural Language API to extract custom entities for classification
- B. Build a custom model to identify the product keywords from the transcribed calls, and then run the keywords through a classification algorithm
- C. Use the Al Platform Training built-in algorithms to create a custom model
- D. Use AutoML Natural Language to extract custom entities for classification
Answer: C
NEW QUESTION 32
You work for a public transportation company and need to build a model to estimate delay times for multiple transportation routes. Predictions are served directly to users in an app in real time. Because different seasons and population increases impact the data relevance, you will retrain the model every month. You want to follow Google-recommended best practices. How should you configure the end-to-end architecture of the predictive model?
- A. Use a model trained and deployed on BigQuery ML and trigger retraining with the scheduled query feature in BigQuery
- B. Write a Cloud Functions script that launches a training and deploying job on Ai Platform that is triggered by Cloud Scheduler
- C. Use Cloud Composer to programmatically schedule a Dataflow job that executes the workflow from training to deploying your model
- D. Configure Kubeflow Pipelines to schedule your multi-step workflow from training to deploying your model.
Answer: D
NEW QUESTION 33
You have been asked to develop an input pipeline for an ML training model that processes images from disparate sources at a low latency. You discover that your input data does not fit in memory. How should you create a dataset following Google-recommended best practices?
- A. Convert the images Into TFRecords, store the images in Cloud Storage, and then use the tf. data API to read the images for training
- B. Convert the images to tf .Tensor Objects, and then run tf. data. Dataset. from_tensors ().
- C. Create a tf.data.Dataset.prefetch transformation
- D. Convert the images to tf .Tensor Objects, and then run Dataset. from_tensor_slices{).
Answer: A
NEW QUESTION 34
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?
- A. 1 = Cloud Function, 2 = Cloud SQL
- B. 1 = Dataflow, 2 = BigQuery
- C. 1 = Dataflow, 2 = Cloud SQL
- D. 1 = Pub/Sub, 2 = Datastore
Answer: A
NEW QUESTION 35
You work for a large hotel chain and have been asked to assist the marketing team in gathering predictions for a targeted marketing strategy. You need to make predictions about user lifetime value (LTV) over the next 30 days so that marketing can be adjusted accordingly. The customer dataset is in BigQuery, and you are preparing the tabular data for training with AutoML Tables. This data has a time signal that is spread across multiple columns. How should you ensure that AutoML fits the best model to your data?
- A. Manually combine all columns that contain a time signal into an array Allow AutoML to interpret this array appropriately Choose an automatic data split across the training, validation, and testing sets
- B. Submit the data for training without performing any manual transformations Allow AutoML to handle the appropriate transformations Choose an automatic data split across the training, validation, and testing sets
- C. Submit the data for training without performing any manual transformations, and indicate an appropriate column as the Time column Allow AutoML to split your data based on the time signal provided, and reserve the more recent data for the validation and testing sets
- D. Submit the data for training without performing any manual transformations Use the columns that have a time signal to manually split your data Ensure that the data in your validation set is from 30 days after the data in your training set and that the data in your testing set is from 30 days after your validation set
Answer: D
NEW QUESTION 36
Your organization wants to make its internal shuttle service route more efficient. The shuttles currently stop at all pick-up points across the city every 30 minutes between 7 am and 10 am. The development team has already built an application on Google Kubernetes Engine that requires users to confirm their presence and shuttle station one day in advance. What approach should you take?
- A. 1. Build a reinforcement learning model with tree-based classification models that predict the presence of passengers at shuttle stops as agents and a reward function around a distance-based metric
2. Dispatch an appropriately sized shuttle and provide the map with the required stops based on the simulated outcome. - B. 1. Build a tree-based classification model that predicts whether the shuttle should pick up passengers at each shuttle station.
2. Dispatch an available shuttle and provide the map with the required stops based on the prediction - C. 1. Define the optimal route as the shortest route that passes by all shuttle stations with confirmed attendance at the given time under capacity constraints.
2 Dispatch an appropriately sized shuttle and indicate the required stops on the map - D. 1. Build a tree-based regression model that predicts how many passengers will be picked up at each shuttle station.
2. Dispatch an appropriately sized shuttle and provide the map with the required stops based on the prediction.
Answer: D
NEW QUESTION 37
A data scientist has developed a machine learning translation model for English to Japanese by using Amazon SageMaker's built-in seq2seq algorithm with 500,000 aligned sentence pairs. While testing with sample sentences, the data scientist finds that the translation quality is reasonable for an example as short as five words. However, the quality becomes unacceptable if the sentence is 100 words long.
Which action will resolve the problem?
- A. Change preprocessing to use n-grams.
- B. Adjust hyperparameters related to the attention mechanism.
- C. Add more nodes to the recurrent neural network (RNN) than the largest sentence's word count.
- D. Choose a different weight initialization type.
Answer: C
NEW QUESTION 38
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. Create a Managed Instance Group with autoscaling
- B. Create a cluster on Dataproc for training
- C. Use Al Platform for distributed training
- D. Use Kubeflow Pipelines to train on a Google Kubernetes Engine cluster.
Answer: A
NEW QUESTION 39
A Data Scientist is training a multilayer perception (MLP) on a dataset with multiple classes. The target class of interest is unique compared to the other classes within the dataset, but it does not achieve and acceptable recall metric. The Data Scientist has already tried varying the number and size of the MLP's hidden layers, which has not significantly improved the results. A solution to improve recall must be implemented as quickly as possible.
Which techniques should be used to meet these requirements?
- A. Add class weights to the MLP's loss function and then retrain
- B. Gather more data using Amazon Mechanical Turk and then retrain
- C. Train an XGBoost model instead of an MLP
- D. Train an anomaly detection model instead of an MLP
Answer: C
NEW QUESTION 40
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 ForecastFrequency to W for weekly.
- B. Set ForecastHorizon to 4.
- C. Set PerformAutoML to true.
- D. Set FeaturizationMethodName to filling.
- E. Set PerformHPO to true.
Answer: A,E
Explanation:
Explanation/Reference: https://docs.aws.amazon.com/forecast/latest/dg/forecast.dg.pdf
NEW QUESTION 41
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. Configure the training program as an ENTRYPOINTnamed train
- B. Use CMD configin the Dockerfile to add the training program as a CMD of the image
- C. Modify the bash_profile file in the container and add a bashcommand to start the training program
- D. Copy the training program to directory /opt/ml/train
Answer: B
NEW QUESTION 42
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?
- A. Use the gcloud command-line tool to submit training jobs on Al Platform when you update your code
- B. Use Cloud Functions to identify changes to your code in Cloud Storage and trigger a retraining job
- C. Use Cloud Build linked with Cloud Source Repositories to trigger retraining when new code is pushed to the repository
- D. Create an automated workflow in Cloud Composer that runs daily and looks for changes in code in Cloud Storage using a sensor.
Answer: B
NEW QUESTION 43
You are training a deep learning model for semantic image segmentation with reduced training time. While using a Deep Learning VM Image, you receive the following error: The resource 'projects/deeplearning-platforn/zones/europe-west4-c/acceleratorTypes/nvidia-tesla-k80' was not found. What should you do?
- A. Ensure that you have preemptible GPU quota in the selected region.
- B. Ensure that the selected GPU has enough GPU memory for the workload.
- C. Ensure that the required GPU is available in the selected region.
- D. Ensure that you have GPU quota in the selected region.
Answer: D
NEW QUESTION 44
You built and manage a production system that is responsible for predicting sales numbers. Model accuracy is crucial, because the production model is required to keep up with market changes. Since being deployed to production, the model hasn't changed; however the accuracy of the model has steadily deteriorated. What issue is most likely causing the steady decline in model accuracy?
- A. Too few layers in the model for capturing information
- B. Incorrect data split ratio during model training, evaluation, validation, and test
- C. Lack of model retraining
- D. Poor data quality
Answer: B
NEW QUESTION 45
A company wants to classify user behavior as either fraudulent or normal. Based on internal research, a machine learning specialist will build a binary classifier based on two features: age of account, denoted by x, and transaction month, denoted by y. The class distributions are illustrated in the provided figure. The positive class is portrayed in red, while the negative class is portrayed in black.
Which model would have the HIGHEST accuracy?
- A. Linear support vector machine (SVM)
- B. Single perceptron with a Tanh activation function
- C. Support vector machine (SVM) with a radial basis function kernel
- D. Decision tree
Answer: C
NEW QUESTION 46
A trucking company is collecting live image data from its fleet of trucks across the globe. The data is growing rapidly and approximately 100 GB of new data is generated every day. The company wants to explore machine learning uses cases while ensuring the data is only accessible to specific IAM users.
Which storage option provides the most processing flexibility and will allow access control with IAM?
- A. Setup up Amazon EMR with Hadoop Distributed File System (HDFS) to store the files, and restrict access to the EMR instances using IAM policies.
- B. Configure Amazon EFS with IAM policies to make the data available to Amazon EC2 instances owned by the IAM users.
- C. Use a database, such as Amazon DynamoDB, to store the images, and set the IAM policies to restrict access to only the desired IAM users.
- D. Use an Amazon S3-backed data lake to store the raw images, and set up the permissions using bucket policies.
Answer: A
Explanation:
Explanation
NEW QUESTION 47
......
Dumps Brief Outline Of The Professional-Machine-Learning-Engineer Exam: https://www.examslabs.com/Google/Google-Certification/best-Professional-Machine-Learning-Engineer-exam-dumps.html
Use Real Professional-Machine-Learning-Engineer - 100% Cover Real Exam Questions: https://drive.google.com/open?id=1BnNy8f6PUMz2y3Wv9CRXlQj2Cy3vdTH2