Professional-Machine-Learning-Engineer

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The Professional Machine Learning Engineer exam is a performance-based assessment that evaluates the candidate’s ability to solve real-world problems using machine learning techniques. Professional-Machine-Learning-Engineer exam consists of a series of hands-on tasks that require the candidate to demonstrate their understanding of various machine learning concepts and their ability to apply them in practical scenarios. Professional-Machine-Learning-Engineer exam is conducted online and can be taken from anywhere in the world.

NO.32 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?

Categorical hinge

Binary cross-entropy

Categorical cross-entropy

Sparse categorical cross-entropy

NO.33 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?

Redaction, reproducibility, and explainability

Traceability, reproducibility, and explainability

Federated learning, reproducibility, and explainability

Differential privacy federated learning, and explainability

NO.34 A Machine Learning Specialist kicks off a hyperparameter tuning job for a tree-based ensemble model using Amazon SageMaker with Area Under the ROC Curve (AUC) as the objective metric. This workflow will eventually be deployed in a pipeline that retrains and tunes hyperparameters each night to model click-through on data that goes stale every 24 hours.
With the goal of decreasing the amount of time it takes to train these models, and ultimately to decrease costs, the Specialist wants to reconfigure the input hyperparameter range(s).
Which visualization will accomplish this?

A histogram showing whether the most important input feature is Gaussian.

A scatter plot with points colored by target variable that uses t-Distributed Stochastic Neighbor Embedding (t-SNE) to visualize the large number of input variables in an easier-to-read dimension.

A scatter plot showing the performance of the objective metric over each training iteration.

A scatter plot showing the correlation between maximum tree depth and the objective metric.

NO.35 You are the Director of Data Science at a large company, and your Data Science team has recently begun using the Kubeflow Pipelines SDK to orchestrate their training pipelines. Your team is struggling to integrate their custom Python code into the Kubeflow Pipelines SDK. How should you instruct them to proceed in order to quickly integrate their code with the Kubeflow Pipelines SDK?

Use the func_to_container_op function to create custom components from the Python code.

Use the predefined components available in the Kubeflow Pipelines SDK to access Dataproc, and run the custom code there.

Package the custom Python code into Docker containers, and use the load_component_from_file function to import the containers into the pipeline.

Deploy the custom Python code to Cloud Functions, and use Kubeflow Pipelines to trigger the Cloud Function.

NO.36 You trained a text classification model. You have the following SignatureDefs:

What is the correct way to write the predict request?

data = json.dumps({“signature_name”: “serving_default’ “instances”: [fab’, ‘be1, ‘cd’]]})

data = json dumps({“signature_name”: “serving_default”! “instances”: [[‘a’, ‘b’, “c”, ‘d’, ‘e’, ‘f’]]})

data = json.dumps({“signature_name”: “serving_default, “instances”: [[‘a’, ‘b ‘c’1, [d ‘e T]]})

data = json dumps({“signature_name”: f,serving_default”, “instances”: [[‘a’, ‘b’], [c ‘d’], [‘e T]]})

NO.37 You built a custom ML model using scikit-learn. Training time is taking longer than expected. You decide to migrate your model to Vertex AI Training, and you want to improve the model’s training time. What should you try out first?

Migrate your model to TensorFlow, and train it using Vertex AI Training.

Train your model in a distributed mode using multiple Compute Engine VMs.

Train your model with DLVM images on Vertex AI, and ensure that your code utilizes NumPy and SciPy internal methods whenever possible.

Train your model using Vertex AI Training with GPUs.

NO.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?

Write a script that sequentially performs the push to your development branch and executes the unit tests on Cloud Run

Using Cloud Build, set an automated trigger to execute the unit tests when changes are pushed to your development branch.

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.

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

NO.39 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?

Dataflow

Dataprep

Apache Flink

Cloud Data Fusion

NO.40 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?

Use the BigQuery console to execute your query and then save the query results Into a new BigQuery table.

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

Use the Kubeflow Pipelines domain-specific language to create a custom component that uses the Python BigQuery client library to execute queries

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

NO.41 Machine Learning Specialist is training a model to identify the make and model of vehicles in images. The Specialist wants to use transfer learning and an existing model trained on images of general objects. The Specialist collated a large custom dataset of pictures containing different vehicle makes and models.
What should the Specialist do to initialize the model to re-train it with the custom data?

Initialize the model with random weights in all layers including the last fully connected layer.

Initialize the model with pre-trained weights in all layers and replace the last fully connected layer.

Initialize the model with random weights in all layers and replace the last fully connected layer.

Initialize the model with pre-trained weights in all layers including the last fully connected layer.

NO.42 You need to execute a batch prediction on 100 million records in a BigQuery table with a custom TensorFlow DNN regressor model, and then store the predicted results in a BigQuery table. You want to minimize the effort required to build this inference pipeline. What should you do?

Import the TensorFlow model with BigQuery ML, and run the ml.predict function.

Use the TensorFlow BigQuery reader to load the data, and use the BigQuery API to write the results to BigQuery.

Create a Dataflow pipeline to convert the data in BigQuery to TFRecords. Run a batch inference on Vertex AI Prediction, and write the results to BigQuery.

Load the TensorFlow SavedModel in a Dataflow pipeline. Use the BigQuery I/O connector with a custom function to perform the inference within the pipeline, and write the results to BigQuery.

NO.43 You are a data scientist at an industrial equipment manufacturing company. You are developing a regression model to estimate the power consumption in the company’s manufacturing plants based on sensor data collected from all of the plants. The sensors collect tens of millions of records every day. You need to schedule daily training runs for your model that use all the data collected up to the current date. You want your model to scale smoothly and require minimal development work. What should you do?

Develop a custom TensorFlow regression model, and optimize it using Vertex Al Training.

Develop a regression model using BigQuery ML.

Develop a custom scikit-learn regression model, and optimize it using Vertex Al Training

Develop a custom PyTorch regression model, and optimize it using Vertex Al Training

NO.44 One of your models is trained using data provided by a third-party data broker. The data broker does not reliably notify you of formatting changes in the dat a. You want to make your model training pipeline more robust to issues like this. What should you do?

Use TensorFlow Data Validation to detect and flag schema anomalies.

Use TensorFlow Transform to create a preprocessing component that will normalize data to the expected distribution, and replace values that don’t match the schema with 0.

Use tf.math to analyze the data, compute summary statistics, and flag statistical anomalies.

Use custom TensorFlow functions at the start of your model training to detect and flag known formatting errors.

NO.45 You are an ML engineer at a bank. You have developed a binary classification model using AutoML Tables to predict whether a customer will make loan payments on time. The output is used to approve or reject loan requests. One customer’s loan request has been rejected by your model, and the bank’s risks department is asking you to provide the reasons that contributed to the model’s decision. What should you do?

Use local feature importance from the predictions.

Use the correlation with target values in the data summary page.

Use the feature importance percentages in the model evaluation page.

Vary features independently to identify the threshold per feature that changes the classification.

NO.46 You are working on a binary classification ML algorithm that detects whether an image of a classified scanned document contains a company’s logo. In the dataset, 96% of examples don’t have the logo, so the dataset is very skewed. Which metrics would give you the most confidence in your model?

F-score where recall is weighed more than precision

RMSE

F1 score

F-score where precision is weighed more than recall

NO.47 A Machine Learning Specialist is preparing data for training on Amazon SageMaker. The Specialist is using one of the SageMaker built-in algorithms for the training. The dataset is stored in .CSV format and is transformed into a numpy.array, which appears to be negatively affecting the speed of the training.
What should the Specialist do to optimize the data for training on SageMaker?

Use the SageMaker batch transform feature to transform the training data into a DataFrame.

Use AWS Glue to compress the data into the Apache Parquet format.

Transform the dataset into the RecordIO protobufformat.

Use the SageMaker hyperparameter optimization feature to automatically optimize the data.

NO.48 A Data Scientist wants to gain real-time insights into a data stream of GZIP files.
Which solution would allow the use of SQL to query the stream with the LEAST latency?

Amazon Kinesis Data Analytics with an AWS Lambda function to transform the data.

AWS Glue with a custom ETL script to transform the data.

An Amazon Kinesis Client Library to transform the data and save it to an Amazon ES cluster.

Amazon Kinesis Data Firehose to transform the data and put it into an Amazon S3 bucket.

NO.49 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?

Significantly increase the max_batch_size TensorFlow Serving parameter

Switch to the tensorflow-model-server-universal version of TensorFlow Serving

Significantly increase the max_enqueued_batches TensorFlow Serving parameter

Recompile TensorFlow Serving using the source to support CPU-specific optimizations Instruct GKE to choose an appropriate baseline minimum CPU platform for serving nodes

NO.50 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?

Ensure that you have GPU quota in the selected region.

Ensure that the required GPU is available in the selected region.

Ensure that you have preemptible GPU quota in the selected region.

Ensure that the selected GPU has enough GPU memory for the workload.

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Category Professional-Machine-Learning-Engineer

100% Free Real Updated Professional-Machine-Learning-Engineer Questions & Answers Pass Your Exam Easily [Q32-Q50]