Latest [Oct 01, 2021] Google Professional-Machine-Learning-Engineer Exam Practice Test To Gain Brilliante Result
Take a Leap Forward in Your Career by Earning Google Professional-Machine-Learning-Engineer
NEW QUESTION 26
You are designing an ML recommendation model for shoppers on your company's ecommerce website. You will use Recommendations Al to build, test, and deploy your system. How should you develop recommendations that increase revenue while following best practices?
- A. Because it will take time to collect and record product data, use placeholder values for the product catalog to test the viability of the model.
- B. Use the "Other Products You May Like" recommendation type to increase the click-through rate
- C. Import your user events and then your product catalog to make sure you have the highest quality event stream
- D. Use the "Frequently Bought Together' recommendation type to increase the shopping cart size for each order.
Answer: D
Explanation:
Frequently bought together' recommendations aim to up-sell and cross-sell customers by providing product.
NEW QUESTION 27
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 C
- C. Option A
- D. Option B
Answer: B
NEW QUESTION 28
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: A
NEW QUESTION 29
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 30
You recently designed and built a custom neural network that uses critical dependencies specific to your organization's framework. You need to train the model using a managed training service on Google Cloud. However, the ML framework and related dependencies are not supported by Al Platform Training. Also, both your model and your data are too large to fit in memory on a single machine. Your ML framework of choice uses the scheduler, workers, and servers distribution structure. What should you do?
- A. Build your custom container to run jobs on Al Platform Training
- B. Reconfigure your code to a ML framework with dependencies that are supported by Al Platform Training
- C. Use a built-in model available on Al Platform Training
- D. Build your custom containers to run distributed training jobs on Al Platform Training
Answer: D
NEW QUESTION 31
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 32
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 33
You are an ML engineer at a bank that has a mobile application. Management has asked you to build an ML-based biometric authentication for the app that verifies a customer's identity based on their fingerprint. Fingerprints are considered highly sensitive personal information and cannot be downloaded and stored into the bank databases. Which learning strategy should you recommend to train and deploy this ML model?
- A. Differential privacy
- B. Federated learning
- C. MD5 to encrypt data
- D. Data Loss Prevention API
Answer: B
NEW QUESTION 34
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. Reinforcement Learning
- D. Classification
Answer: C
NEW QUESTION 35
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. Three individual features binned latitude, binned longitude, and one-hot encoded car type
- C. One feature obtained as an element-wise product between latitude, longitude, and car type
- D. 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
Answer: B
NEW QUESTION 36
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 needs to be completely re-engineered because it is unable to handle product inventory changes.
- B. The model should be periodically retrained from scratch using the original data while adding a regularization term to handle product inventory changes
- C. The model's hyperparameters should be periodically updated to prevent drift.
- D. The model should be periodically retrained using the original training data plus new data as product inventory changes.
Answer: D
NEW QUESTION 37
Your team is building a convolutional neural network (CNN)-based architecture from scratch. The preliminary experiments running on your on-premises CPU-only infrastructure were encouraging, but have slow convergence. You have been asked to speed up model training to reduce time-to-market. You want to experiment with virtual machines (VMs) on Google Cloud to leverage more powerful hardware. Your code does not include any manual device placement and has not been wrapped in Estimator model-level abstraction. Which environment should you train your model on?
- A. A Deep Learning VM with an n1-standard-2 machine and 1 GPU with all libraries pre-installed.
- B. AVM on Compute Engine and 1 TPU with all dependencies installed manually.
- C. A Deep Learning VM with more powerful CPU e2-highcpu-16 machines with all libraries pre-installed.
- D. AVM on Compute Engine and 8 GPUs with all dependencies installed manually.
Answer: B
NEW QUESTION 38
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. 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.
- 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. 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
Answer: B
NEW QUESTION 39
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. Configure Kubeflow Pipelines to schedule your multi-step workflow from training to deploying your model.
- B. Write a Cloud Functions script that launches a training and deploying job on Ai Platform that is triggered by Cloud Scheduler
- C. Use a model trained and deployed on BigQuery ML and trigger retraining with the scheduled query feature in BigQuery
- D. Use Cloud Composer to programmatically schedule a Dataflow job that executes the workflow from training to deploying your model
Answer: C
NEW QUESTION 40
A data scientist uses an Amazon SageMaker notebook instance to conduct data exploration and analysis. This requires certain Python packages that are not natively available on Amazon SageMaker to be installed on the notebook instance.
How can a machine learning specialist ensure that required packages are automatically available on the notebook instance for the data scientist to use?
- A. Create an Amazon SageMaker lifecycle configuration with package installation commands and assign the lifecycle configuration to the notebook instance.
- B. Use the conda package manager from within the Jupyter notebook console to apply the necessary conda packages to the default kernel of the notebook.
- C. Create a Jupyter notebook file (.ipynb) with cells containing the package installation commands to execute and place the file under the /etc/init directory of each Amazon SageMaker notebook instance.
- D. Install AWS Systems Manager Agent on the underlying Amazon EC2 instance and use Systems Manager Automation to execute the package installation commands.
Answer: C
Explanation:
Explanation
Explanation/Reference: https://towardsdatascience.com/automating-aws-sagemaker-notebooks-2dec62bc2c84
NEW QUESTION 41
You are designing an architecture with a serveress ML system to enrich customer support tickets with informative metadata before they are routed to a support agent. You need a set of models to predict ticket priority, predict ticket resolution time, and perform sentiment analysis to help agents make strategic decisions when they process support requests. Tickets are not expected to have any domain-specific terms or jargon.
The proposed architecture has the following flow:
Which endpoints should the Enrichment Cloud Functions call?
- A. 1 = Al Platform, 2 = Al Platform, 3 = AutoML Vision
- B. 1 = Al Platform, 2 = Al Platform, 3 = Cloud Natural Language API
- C. 1 = cloud Natural Language API, 2 = Al Platform, 3 = Cloud Vision API
- D. 1 = Al Platform, 2 = Al Platform, 3 = AutoML Natural Language
Answer: D
NEW QUESTION 42
You are an ML engineer in the contact center of a large enterprise. You need to build a sentiment analysis tool that predicts customer sentiment from recorded phone conversations. You need to identify the best approach to building a model while ensuring that the gender, age, and cultural differences of the customers who called the contact center do not impact any stage of the model development pipeline and results. What should you do?
- A. Convert the speech to text and build a model based on the words
- B. Extract sentiment directly from the voice recordings
- C. Convert the speech to text and extract sentiments based on the sentences
- D. Convert the speech to text and extract sentiment using syntactical analysis
Answer: C
NEW QUESTION 43
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. Decrease the batch size argument in your transformation
- B. Increase the buffer size for the shuffle option.
- C. Use the interleave option for reading data
- D. Set the prefetch option equal to the training batch size
- E. Reduce the value of the repeat parameter
Answer: A,C
NEW QUESTION 44
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 45
When submitting Amazon SageMaker training jobs using one of the built-in algorithms, which common parameters MUST be specified? (Choose three.)
- A. Hyperparameters in a JSON array as documented for the algorithm used.
- B. The training channel identifying the location of training data on an Amazon S3 bucket.
- C. The validation channel identifying the location of validation data on an Amazon S3 bucket.
- D. The output path specifying where on an Amazon S3 bucket the trained model will persist.
- E. The Amazon EC2 instance class specifying whether training will be run using CPU or GPU.
- F. The IAM role that Amazon SageMaker can assume to perform tasks on behalf of the users.
Answer: B,D,E
Explanation:
Explanation
NEW QUESTION 46
A company wants to predict the sale prices of houses based on available historical sales data. The target variable in the company's dataset is the sale price. The features include parameters such as the lot size, living area measurements, non-living area measurements, number of bedrooms, number of bathrooms, year built, and postal code. The company wants to use multi-variable linear regression to predict house sale prices.
Which step should a machine learning specialist take to remove features that are irrelevant for the analysis and reduce the model's complexity?
- A. Build a heatmap showing the correlation of the dataset against itself. Remove features with low mutual correlation scores.
- B. Run a correlation check of all features against the target variable. Remove features with low target variable correlation scores.
- C. Plot a histogram of the features and compute their standard deviation. Remove features with low variance.
- D. Plot a histogram of the features and compute their standard deviation. Remove features with high variance.
Answer: B
NEW QUESTION 47
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. 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.
- B. Serialize the trained model so the format is compressed for deployment. Build the image and upload it to Docker Hub.
- C. Build the Docker image with the inference code. Tag the Docker image with the registry hostname and upload it to Amazon ECR.
- D. Build the Docker image with the inference code. Configure Docker Hub and upload the image to Amazon ECR.
Answer: D
NEW QUESTION 48
......
Authentic Best resources for Professional-Machine-Learning-Engineer Online Practice Exam: https://www.prep4sureexam.com/Professional-Machine-Learning-Engineer-dumps-torrent.html
Updates Up to 365 days On Developing Professional-Machine-Learning-Engineer Braindumps: https://drive.google.com/open?id=1uUN0cHKkzpzzDvussUYlEXwzNKrE9UTW