72 Exam Questions for Professional-Machine-Learning-Engineer Updated Versions With Test Engine
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Understanding functional and technical aspects of Professional Machine Learning Engineer - Google ML Model Development
The following will be discussed in Google Professional-Machine-Learning-Engineer dumps:
- Build a model
- Model explainability on Cloud AI Platform
- Model generalization
- Distributed training
- Retraining/redeployment evaluation
- Hardware accelerators
- Training a model as a job in different environments
- Modeling techniques given interpretability requirements
- Choice of framework and model
- Scale model training and serving
- Transfer learning
- Model performance against baselines, simpler models, and across the time dimension
- Unit tests for model training and serving
- Scalable model analysis (e.g. Cloud Storage output files, Dataflow, BigQuery, Google Data Studio)
- Tracking metrics during training
- Productionizing
- Overfitting
Understanding functional and technical aspects of Professional Machine Learning Engineer - Google ML Problem Framing
The following will be discussed in Google Professional-Machine-Learning-Engineer dumps:
- Key results
- Defining problem type (classification, regression, clustering, etc.)
- Defining business problems
- Identifying nonML solutions
- Define business success criteria
- Assessing ML solution readiness
- Defining outcome of model predictions
- Assessing and communicating business impact
- Managing incorrect results
- Defining output use
- Define ML problem
- Defining the input (features) and predicted output format
- Aligning with Google AI principles and practices (e.g. different biases)
- Identify risks to feasibility and implementation of ML solution. Considerations include:
- Assessing data readiness
- Identifying data sources
- Determination of when a model is deemed unsuccessful
- Success metrics
NEW QUESTION 14
A company's Machine Learning Specialist needs to improve the training speed of a time-series forecasting model using TensorFlow. The training is currently implemented on a single-GPU machine and takes approximately 23 hours to complete. The training needs to be run daily.
The model accuracy is acceptable, but the company anticipates a continuous increase in the size of the training data and a need to update the model on an hourly, rather than a daily, basis. The company also wants to minimize coding effort and infrastructure changes.
What should the Machine Learning Specialist do to the training solution to allow it to scale for future demand?
- A. Change the TensorFlow code to implement a Horovod distributed framework supported by Amazon SageMaker. Parallelize the training to as many machines as needed to achieve the business goals.
- B. Move the training to Amazon EMR and distribute the workload to as many machines as needed to achieve the business goals.
- C. Do not change the TensorFlow code. Change the machine to one with a more powerful GPU to speed up the training.
- D. Switch to using a built-in AWS SageMaker DeepAR model. Parallelize the training to as many machines as needed to achieve the business goals.
Answer: A
NEW QUESTION 15
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 latitude, longitude, and car type
- B. 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
- C. Three individual features binned latitude, binned longitude, and one-hot encoded car type
- D. One feature obtained as an element-wise product between binned latitude, binned longitude, and one-hot encoded car type
Answer: C
NEW QUESTION 16
A manufacturing company has a large set of labeled historical sales data. The manufacturer would like to predict how many units of a particular part should be produced each quarter.
Which machine learning approach should be used to solve this problem?
- A. Principal component analysis (PCA)
- B. Random Cut Forest (RCF)
- C. Linear regression
- D. Logistic regression
Answer: B
NEW QUESTION 17
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 18
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 19
You have written unit tests for a Kubeflow Pipeline that require custom libraries. You want to automate the execution of unit tests with each new push to your development branch in Cloud Source Repositories. What should you do?
- A. Write a script that sequentially performs the push to your development branch and executes the unit tests on Cloud Run
- B. Using Cloud Build, set an automated trigger to execute the unit tests when changes are pushed to your development branch.
- C. Set up a Cloud Logging sink to a Pub/Sub topic that captures interactions with Cloud Source Repositories Configure a Pub/Sub trigger for Cloud Run, and execute the unit tests on Cloud Run.
- D. Set up a Cloud Logging sink to a Pub/Sub topic that captures interactions with Cloud Source Repositories. Execute the unit tests using a Cloud Function that is triggered when messages are sent to the Pub/Sub topic
Answer: B
NEW QUESTION 20
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. 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
- B. 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.
- C. 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.
- D. Set up restrictive I AM permissions on the Al Platform notebooks so that only a single user or group can access a given instance.
Answer: C
NEW QUESTION 21
You have trained a model on a dataset that required computationally expensive preprocessing operations. You need to execute the same preprocessing at prediction time. You deployed the model on Al Platform for high-throughput online prediction. Which architecture should you use?
- A. Stream incoming prediction request data into Cloud Spanner
* Create a view to abstract your preprocessing logic.
* Query the view every second for new records
* Submit a prediction request to Al Platform using the transformed data
* Write the predictions to an outbound Pub/Sub queue. - B. Validate the accuracy of the model that you trained on preprocessed data
* Create a new model that uses the raw data and is available in real time
* Deploy the new model onto Al Platform for online prediction - C. Send incoming prediction requests to a Pub/Sub topic
* Set up a Cloud Function that is triggered when messages are published to the Pub/Sub topic.
* Implement your preprocessing logic in the Cloud Function
* Submit a prediction request to Al Platform using the transformed data
* Write the predictions to an outbound Pub/Sub queue - D. Send incoming prediction requests to a Pub/Sub topic
* Transform the incoming data using a Dataflow job
* Submit a prediction request to Al Platform using the transformed data
* Write the predictions to an outbound Pub/Sub queue
Answer: C
NEW QUESTION 22
You trained a text classification model. You have the following SignatureDefs:
What is the correct way to write the predict request?
- A. data = json dumps({"signature_name": "serving_default"! "instances": [['a', 'b', "c", 'd', 'e', 'f']]})
- B. data = json.dumps({"signature_name": "serving_default, "instances": [['a', 'b\ 'c'1, [d\ 'e\ T]]})
- C. data = json dumps({"signature_name": f,serving_default", "instances": [['a', 'b'], [c\ 'd'], ['e\ T]]})
- D. data = json.dumps({"signature_name": "serving_default'\ "instances": [fab', 'be1, 'cd']]})
Answer: A
NEW QUESTION 23
Your team is working on an NLP research project to predict political affiliation of authors based on articles they have written. You have a large training dataset that is structured like this:
A)
B)
C)
D)
- A. Option D
- B. Option B
- C. Option A
- D. Option C
Answer: A
NEW QUESTION 24
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 Al Platform Training built-in algorithms to create a custom model
- 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 AutoML Natural Language to extract custom entities for classification
- D. Use the Cloud Natural Language API to extract custom entities for classification
Answer: A
NEW QUESTION 25
You need to train a computer vision model that predicts the type of government ID present in a given image using a GPU-powered virtual machine on Compute Engine. You use the following parameters:
* Optimizer: SGD
* Image shape = 224x224
* Batch size = 64
* Epochs = 10
* Verbose = 2
During training you encounter the following error: ResourceExhaustedError: out of Memory (oom) when allocating tensor. What should you do?
- A. Reduce the image shape
- B. Change the optimizer
- C. Change the learning rate
- D. Reduce the batch size
Answer: B
NEW QUESTION 26
Your team needs to build a model that predicts whether images contain a driver's license, passport, or credit card. The data engineering team already built the pipeline and generated a dataset composed of 10,000 images with driver's licenses, 1,000 images with passports, and 1,000 images with credit cards. You now have to train a model with the following label map: ['driversjicense', 'passport', 'credit_card']. Which loss function should you use?
- A. Categorical cross-entropy
- B. Categorical hinge
- C. Sparse categorical cross-entropy
- D. Binary cross-entropy
Answer: C
Explanation:
se sparse_categorical_crossentropy. Examples for above 3-class classification problem: [1] , [2], [3]
NEW QUESTION 27
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 28
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. Dataflow
- B. Dataprep
- C. Cloud Data Fusion
- D. Apache Flink
Answer: C
NEW QUESTION 29
You started working on a classification problem with time series data and achieved an area under the receiver operating characteristic curve (AUC ROC) value of 99% for training data after just a few experiments. You haven't explored using any sophisticated algorithms or spent any time on hyperparameter tuning. What should your next step be to identify and fix the problem?
- A. Address the model overfitting by tuning the hyperparameters to reduce the AUC ROC value.
- B. Address data leakage by applying nested cross-validation during model training.
- C. Address the model overfitting by using a less complex algorithm.
- D. Address data leakage by removing features highly correlated with the target value.
Answer: B
NEW QUESTION 30
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What is the duration, language, and format of Professional Machine Learning Engineer - Google
- Duration of Exam: 120 minutes
- Type of Questions: Multiple choice (MCQs), multiple answers
- Language of Exam: English, Japanese, Korean
- No negative marking for wrong answers
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