Best Quality Google Professional-Machine-Learning-Engineer Exam Questions ValidTorrent Realistic Practice Exams [2021]
Critical Information To Google Professional Machine Learning Engineer Pass the First Time
NEW QUESTION 33
A web-based company wants to improve its conversion rate on its landing page. Using a large historical dataset of customer visits, the company has repeatedly trained a multi-class deep learning network algorithm on Amazon SageMaker. However, there is an overfitting problem: training data shows 90% accuracy in predictions, while test data shows 70% accuracy only.
The company needs to boost the generalization of its model before deploying it into production to maximize conversions of visits to purchases.
Which action is recommended to provide the HIGHEST accuracy model for the company's test and validation data?
- A. Reduce the number of layers and units (or neurons) from the deep learning network
- B. Increase the randomization of training data in the mini-batches used in training
- C. Apply L1 or L2 regularization and dropouts to the training
- D. Allocate a higher proportion of the overall data to the training dataset
Answer: A
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
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 Al Platform Training to execute the experiments Write the accuracy metrics to Cloud Monitoring, and query the results using the Monitoring API.
- B. Use Al Platform Training to execute the experiments Write the accuracy metrics to BigQuery, and query the results using the BigQueryAPI.
- C. 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
- D. Use Kubeflow Pipelines to execute the experiments Export the metrics file, and query the results using the Kubeflow Pipelines API.
Answer: D
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. 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 37
A company uses a long short-term memory (LSTM) model to evaluate the risk factors of a particular energy sector. The model reviews multi-page text documents to analyze each sentence of the text and categorize it as either a potential risk or no risk. The model is not performing well, even though the Data Scientist has experimented with many different network structures and tuned the corresponding hyperparameters.
Which approach will provide the MAXIMUM performance boost?
- A. Initialize the words by term frequency-inverse document frequency (TF-IDF) vectors pretrained on a large collection of news articles related to the energy sector.
- B. Use gated recurrent units (GRUs) instead of LSTM and run the training process until the validation loss stops decreasing.
- C. Reduce the learning rate and run the training process until the training loss stops decreasing.
- D. Initialize the words by word2vec embeddings pretrained on a large collection of news articles related to the energy sector.
Answer: C
NEW QUESTION 38
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. 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
- 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. Using Cloud Build, set an automated trigger to execute the unit tests when changes are pushed to your development branch.
Answer: D
NEW QUESTION 39
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 DeepAR forecasting model based on a recurrent neural network (RNN)
- C. AnXGBoost model where the objective parameter is set to multi:softmax
- D. A regression forest where the number of trees is set equal to the number of product categories
Answer: A
NEW QUESTION 40
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 41
You are developing a Kubeflow pipeline on Google Kubernetes Engine. The first step in the pipeline is to issue a query against BigQuery. You plan to use the results of that query as the input to the next step in your pipeline. You want to achieve this in the easiest way possible. What should you do?
- A. Write a Python script that uses the BigQuery API to execute queries against BigQuery Execute this script as the first step in your Kubeflow pipeline
- B. Use the BigQuery console to execute your query and then save the query results Into a new BigQuery table.
- C. Locate the Kubeflow Pipelines repository on GitHub Find the BigQuery Query Component, copy that component's URL, and use it to load the component into your pipeline. Use the component to execute queries against BigQuery
- D. Use the Kubeflow Pipelines domain-specific language to create a custom component that uses the Python BigQuery client library to execute queries
Answer: B
NEW QUESTION 42
A Data Scientist needs to analyze employment data. The dataset contains approximately 10 million observations on people across 10 different features. During the preliminary analysis, the Data Scientist notices that income and age distributions are not normal. While income levels shows a right skew as expected, with fewer individuals having a higher income, the age distribution also show a right skew, with fewer older individuals participating in the workforce.
Which feature transformations can the Data Scientist apply to fix the incorrectly skewed data? (Choose two.)
- A. Logarithmic transformation
- B. Cross-validation
- C. One hot encoding
- D. High-degree polynomial transformation
- E. Numerical value binning
Answer: B,E
NEW QUESTION 43
You were asked to investigate failures of a production line component based on sensor readings. After receiving the dataset, you discover that less than 1% of the readings are positive examples representing failure incidents. You have tried to train several classification models, but none of them converge. How should you resolve the class imbalance problem?
- A. Remove negative examples until the numbers of positive and negative examples are equal
- B. Use the class distribution to generate 10% positive examples
- C. Downsample the data with upweighting to create a sample with 10% positive examples
- D. Use a convolutional neural network with max pooling and softmax activation
Answer: A
NEW QUESTION 44
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 using a less complex algorithm.
- B. Address data leakage by removing features highly correlated with the target value.
- C. Address data leakage by applying nested cross-validation during model training.
- D. Address the model overfitting by tuning the hyperparameters to reduce the AUC ROC value.
Answer: C
NEW QUESTION 45
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 46
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 47
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 learning rate hyperparameter
- B. Increase the learning rate hyperparameter
- C. Decrease the size of the training batch
- D. Increase the size of the training batch
Answer: B
NEW QUESTION 48
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 49
You are an ML engineer at a global shoe store. You manage the ML models for the company's website. You are asked to build a model that will recommend new products to the user based on their purchase behavior and similarity with other users. What should you do?
- A. Build a knowledge-based filtering model
- B. Build a classification model
- C. Build a collaborative-based filtering model
- D. Build a regression model using the features as predictors
Answer: C
NEW QUESTION 50
A Machine Learning Specialist at a company sensitive to security is preparing a dataset for model training. The dataset is stored in Amazon S3 and contains Personally Identifiable Information (PII).
The dataset:
* Must be accessible from a VPC only.
* Must not traverse the public internet.
How can these requirements be satisfied?
- A. Create a VPC endpoint and apply a bucket access policy that restricts access to the given VPC endpoint and the VPC.
- B. Create a VPC endpoint and apply a bucket access policy that allows access from the given VPC endpoint and an Amazon EC2 instance.
- C. Create a VPC endpoint and use security groups to restrict access to the given VPC endpoint and an Amazon EC2 instance
- D. Create a VPC endpoint and use Network Access Control Lists (NACLs) to allow traffic between only the given VPC endpoint and an Amazon EC2 instance.
Answer: A
NEW QUESTION 51
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 training is reproducible
- B. Ensure that model performance is monitored
- C. Ensure that all hyperparameters are tuned
- D. Ensure that feature expectations are captured in the schema
Answer: A
NEW QUESTION 52
You are building a model to predict daily temperatures. You split the data randomly and then transformed the training and test datasets. Temperature data for model training is uploaded hourly. During testing, your model performed with 97% accuracy; however, after deploying to production, the model's accuracy dropped to 66%. How can you make your production model more accurate?
- A. Normalize the data for the training, and test datasets as two separate steps.
- B. Apply data transformations before splitting, and cross-validate to make sure that the transformations are applied to both the training and test sets.
- C. Split the training and test data based on time rather than a random split to avoid leakage
- D. Add more data to your test set to ensure that you have a fair distribution and sample for testing
Answer: B
NEW QUESTION 53
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 54
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 55
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 56
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 57
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