Easy To Download Google Professional-Machine-Learning-Engineer Exam Dumps Updated 72 Questions
New Updated Professional-Machine-Learning-Engineer Exam Questions 2021
The benefit of obtaining the Professional Machine Learning Engineer - Google Certification
- Professional Cloud Architect was the highest paying certification of 2020 and 2019
- 87% of Google Cloud certified individuals are more confident about their cloud skills
- More than 1 in 4 of Google Cloud certified individuals took on more responsibility or leadership roles at work
NEW QUESTION 41
A Machine Learning Specialist is working with a large company to leverage machine learning within its products. The company wants to group its customers into categories based on which customers will and will not churn within the next 6 months. The company has labeled the data available to the Specialist.
Which machine learning model type should the Specialist use to accomplish this task?
- A. Clustering
- B. Classification
- C. Reinforcement learning
- D. Linear regression
Answer: B
Explanation:
The goal of classification is to determine to which class or category a data point (customer in our case) belongs to. For classification problems, data scientists would use historical data with predefined target variables AKA labels (churner/non-churner) - answers that need to be predicted - to train an algorithm. With classification, businesses can answer the following questions:
* Will this customer churn or not?
* Will a customer renew their subscription?
* Will a user downgrade a pricing plan?
* Are there any signs of unusual customer behavior?
Reference: https://www.kdnuggets.com/2019/05/churn-prediction-machine-learning.html
NEW QUESTION 42
You are an ML engineer at a regulated insurance company. You are asked to develop an insurance approval model that accepts or rejects insurance applications from potential customers. What factors should you consider before building the model?
- A. Redaction, reproducibility, and explainability
- B. Federated learning, reproducibility, and explainability
- C. Differential privacy federated learning, and explainability
- D. Traceability, reproducibility, and explainability
Answer: D
NEW QUESTION 43
A Machine Learning Specialist is developing a daily ETL workflow containing multiple ETL jobs. The workflow consists of the following processes:
* Start the workflow as soon as data is uploaded to Amazon S3.
* When all the datasets are available in Amazon S3, start an ETL job to join the uploaded datasets with multiple terabyte-sized datasets already stored in Amazon S3.
* Store the results of joining datasets in Amazon S3.
* If one of the jobs fails, send a notification to the Administrator.
Which configuration will meet these requirements?
- A. Use AWS Lambda to trigger an AWS Step Functions workflow to wait for dataset uploads to complete in Amazon S3. Use AWS Glue to join the datasets. Use an Amazon CloudWatch alarm to send an SNS notification to the Administrator in the case of a failure.
- B. Develop the ETL workflow using AWS Lambda to start an Amazon SageMaker notebook instance. Use a lifecycle configuration script to join the datasets and persist the results in Amazon S3. Use an Amazon CloudWatch alarm to send an SNS notification to the Administrator in the case of a failure.
- C. Use AWS Lambda to chain other Lambda functions to read and join the datasets in Amazon S3 as soon as the data is uploaded to Amazon S3. Use an Amazon CloudWatch alarm to send an SNS notification to the Administrator in the case of a failure.
- D. Develop the ETL workflow using AWS Batch to trigger the start of ETL jobs when data is uploaded to Amazon S3. Use AWS Glue to join the datasets in Amazon S3. Use an Amazon CloudWatch alarm to send an SNS notification to the Administrator in the case of a failure.
Answer: A
Explanation:
Explanation/Reference: https://aws.amazon.com/step-functions/use-cases/
NEW QUESTION 44
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 45
A large mobile network operating company is building a machine learning model to predict customers who are likely to unsubscribe from the service. The company plans to offer an incentive for these customers as the cost of churn is far greater than the cost of the incentive.
The model produces the following confusion matrix after evaluating on a test dataset of 100 customers:
Based on the model evaluation results, why is this a viable model for production?
- A. The model is 86% accurate and the cost incurred by the company as a result of false positives is less than the false negatives.
- B. The model is 86% accurate and the cost incurred by the company as a result of false negatives is less than the false positives.
- C. The precision of the model is 86%, which is greater than the accuracy of the model.
- D. The precision of the model is 86%, which is less than the accuracy of the model.
Answer: B
NEW QUESTION 46
Your data science team needs to rapidly experiment with various features, model architectures, and hyperparameters. They need to track the accuracy metrics for various experiments and use an API to query the metrics over time. What should they use to track and report their experiments while minimizing manual effort?
- A. Use Kubeflow Pipelines to execute the experiments Export the metrics file, and query the results using the Kubeflow Pipelines API.
- B. Use Al Platform Notebooks to execute the experiments. Collect the results in a shared Google Sheets file, and query the results using the Google Sheets API
- C. Use Al Platform Training to execute the experiments Write the accuracy metrics to Cloud Monitoring, and query the results using the Monitoring API.
- D. Use Al Platform Training to execute the experiments Write the accuracy metrics to BigQuery, and query the results using the BigQueryAPI.
Answer: D
NEW QUESTION 47
A company ingests machine learning (ML) data from web advertising clicks into an Amazon S3 data lake. Click data is added to an Amazon Kinesis data stream by using the Kinesis Producer Library (KPL). The data is loaded into the S3 data lake from the data stream by using an Amazon Kinesis Data Firehose delivery stream.
As the data volume increases, an ML specialist notices that the rate of data ingested into Amazon S3 is relatively constant. There also is an increasing backlog of data for Kinesis Data Streams and Kinesis Data Firehose to ingest.
Which next step is MOST likely to improve the data ingestion rate into Amazon S3?
- A. Increase the number of S3 prefixes for the delivery stream to write to.
- B. Increase the number of shards for the data stream.
- C. Add more consumers using the Kinesis Client Library (KCL).
- D. Decrease the retention period for the data stream.
Answer: B
Explanation:
Explanation/Reference:
NEW QUESTION 48
A Machine Learning Specialist is packaging a custom ResNet model into a Docker container so the company can leverage Amazon SageMaker for training. The Specialist is using Amazon EC2 P3 instances to train the model and needs to properly configure the Docker container to leverage the NVIDIA GPUs.
What does the Specialist need to do?
- A. Organize the Docker container's file structure to execute on GPU instances.
- B. Bundle the NVIDIA drivers with the Docker image.
- C. Build the Docker container to be NVIDIA-Docker compatible.
- D. Set the GPU flag in the Amazon SageMaker CreateTrainingJob request body.
Answer: B
NEW QUESTION 49
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 feature expectations are captured in the schema
- C. Ensure that training is reproducible
- D. Ensure that model performance is monitored
Answer: A
NEW QUESTION 50
You are building a linear regression model on BigQuery ML to predict a customer's likelihood of purchasing your company's products. Your model uses a city name variable as a key predictive component. In order to train and serve the model, your data must be organized in columns. You want to prepare your data using the least amount of coding while maintaining the predictable variables. What should you do?
- A. Use Dataprep to transform the state column using a one-hot encoding method, and make each city a column with binary values.
- B. Use TensorFlow to create a categorical variable with a vocabulary list Create the vocabulary file, and upload it as part of your model to BigQuery ML.
- C. Create a new view with BigQuery that does not include a column with city information
- D. Use Cloud Data Fusion to assign each city to a region labeled as 1, 2, 3, 4, or 5r and then use that number to represent the city in the model.
Answer: D
NEW QUESTION 51
You work for a credit card company and have been asked to create a custom fraud detection model based on historical data using AutoML Tables. You need to prioritize detection of fraudulent transactions while minimizing false positives. Which optimization objective should you use when training the model?
- A. An optimization objective that maximizes the area under the precision-recall curve (AUC PR) value
- B. An optimization objective that minimizes Log loss
- C. An optimization objective that maximizes the area under the receiver operating characteristic curve (AUC ROC) value
- D. An optimization objective that maximizes the Precision at a Recall value of 0.50
Answer: A
NEW QUESTION 52
A Machine Learning Specialist previously trained a logistic regression model using scikit-learn on a local machine, and the Specialist now wants to deploy it to production for inference only.
What steps should be taken to ensure Amazon SageMaker can host a model that was trained locally?
- A. Build the Docker image with the inference code. Tag the Docker image with the registry hostname and upload it to Amazon ECR.
- B. Serialize the trained model so the format is compressed for deployment. Build the image and upload it to Docker Hub.
- C. Serialize the trained model so the format is compressed for deployment. Tag the Docker image with the registry hostname and upload it to Amazon S3.
- D. Build the Docker image with the inference code. Configure Docker Hub and upload the image to Amazon ECR.
Answer: D
NEW QUESTION 53
You are going to train a DNN regression model with Keras APIs using this code:
How many trainable weights does your model have? (The arithmetic below is correct.)
- A. 501*256+257*128+128*2=161408
- B. 501*256+257*128+2 = 161154
- C. 500*256+256*128+128*2 = 161024
- D. 500*256*0 25+256*128*0 25+128*2 = 40448
Answer: D
NEW QUESTION 54
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. Set the early stopping parameter to TRUE
- B. Decrease the maximum number of trials during subsequent training phases.
- C. Decrease the number of parallel trials
- D. Change the search algorithm from Bayesian search to random search.
- E. Decrease the range of floating-point values
Answer: D,E
NEW QUESTION 55
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. Create a tf.data.Dataset.prefetch transformation
- B. Convert the images to tf .Tensor Objects, and then run Dataset. from_tensor_slices{).
- C. Convert the images to tf .Tensor Objects, and then run tf. data. Dataset. from_tensors ().
- D. Convert the images Into TFRecords, store the images in Cloud Storage, and then use the tf. data API to read the images for training
Answer: D
NEW QUESTION 56
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?
- A. 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.
- B. 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.
- C. 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.
- D. 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.
Answer: D
NEW QUESTION 57
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Topics of Professional Machine Learning Engineer - Google
Candidates must know the exam topics before they start preparation. Because it will help them in hitting the core. Google Professional-Machine-Learning-Engineer dumps pdf will include the following topics:
- ML Pipeline Automation & Orchestration
- ML Model Development
- Data Preparation and Processing
- ML Solution Monitoring, Optimization, and Maintenance
- ML Solution Architecture
- ML Problem Framing
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