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Die Google Professional Machine Learning Engineer -Zertifizierungsprüfung ist eine professionelle Zertifizierung, mit der die Kenntnisse einer Person in Bezug auf das Entwerfen, Erstellen und Bereitstellen von Modellen für maschinelles Lernen auf der Google Cloud -Plattform getestet werden sollen. Diese Zertifizierung richtet sich an Personen, die ein gründliches Verständnis der Prinzipien für maschinelles Lernen und Erfahrung mit der Google Cloud -Plattform haben. Die Prüfung soll die Fähigkeit einer Person testen, Daten zu analysieren und zu interpretieren, maschinelles Lernmodelle zu entwerfen, Modelle zu trainieren und zu optimieren und Modelle in der Produktion einzusetzen.

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Google Professional-Machine-Learning-Engineer Prüfungsfragen & Professional-Machine-Learning-Engineer Tests

Die Google Professional-Machine-Learning-Engineer Zertifizierungsprüfung gehört zu den beliebtesten IT-Zertifizierungen. Viele ambitionierte IT-Fachleute wollen auch Google Professional-Machine-Learning-Engineer Prüfung bestehen. Viele Kandidaten sollen genügende Vorbereitungen treffen, um eine hohe Note zu bekommen und sich den Bedürfnissen des Marktes anzupassen.

Google Professional Machine Learning Engineer Professional-Machine-Learning-Engineer Prüfungsfragen mit Lösungen (Q288-Q293):

288. Frage
You built a deep learning-based image classification model by using on-premises data. You want to use Vertex Al to deploy the model to production Due to security concerns you cannot move your data to the cloud. You are aware that the input data distribution might change over time You need to detect model performance changes in production. What should you do?

A. Create a Vertex Al Model Monitoring job. Enable training-serving skew detection for your model.
B. Create a Vertex Al Model Monitoring job. Enable feature attribution skew and dnft detection for your model.
C. Use Vertex Explainable Al for model explainability Configure feature-based explanations.
D. Use Vertex Explainable Al for model explainability Configure example-based explanations.

Antwort: A

Begründung:
Vertex AI Model Monitoring is a service that allows you to monitor the performance and quality of your ML models in production. You can use Vertex AI Model Monitoring to detect changes in the input data distribution, the prediction output distribution, or the model accuracy over time. Training-serving skew detection is a feature of Vertex AI Model Monitoring that compares the statistics of the data used for training the model and the data used for serving the model. If there is a significant difference between the two data distributions, it indicates that the model might be outdated or inaccurate. By enabling training-serving skew detection for your model, you can detect model performance changes in production and trigger retraining or redeployment of your model as needed. This way, you can ensure that your model is always up-to-date and accurate, without moving your data to the cloud. References:
* Vertex AI Model Monitoring documentation
* Training-serving skew detection documentation
* Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate

289. Frage
Machine Learning Specialist is building a model to predict future employment rates based on a wide range of economic factors. While exploring the data, the Specialist notices that the magnitude of the input features vary greatly. The Specialist does not want variables with a larger magnitude to dominate the model.
What should the Specialist do to prepare the data for model training?

A. Apply normalization to ensure each field will have a mean of 0 and a variance of 1 to remove any significant magnitude.
B. Apply the orthogonal sparse bigram (OSB) transformation to apply a fixed-size sliding window to generate new features of a similar magnitude.
C. Apply quantile binning to group the data into categorical bins to keep any relationships in the data by replacing the magnitude with distribution.
D. Apply the Cartesian product transformation to create new combinations of fields that are independent of the magnitude.

Antwort: A

Begründung:
Explanation/Reference: https://docs.aws.amazon.com/machine-learning/latest/dg/data-transformations-reference.html

290. Frage
You are training a TensorFlow model on a structured data set with 100 billion records stored in several CSV files. You need to improve the input/output execution performance. What should you do?

A. Load the data into BigQuery and read the data from BigQuery.
B. Convert the CSV files into shards of TFRecords, and store the data in the Hadoop Distributed File System (HDFS)
C. Convert the CSV files into shards of TFRecords, and store the data in Cloud Storage
D. Load the data into Cloud Bigtable, and read the data from Bigtable

Antwort: C

Begründung:
The input/output execution performance of a TensorFlow model depends on how efficiently the model can read and process the data from the data source. Reading and processing data from CSV files can be slow and inefficient, especially if the data is large and distributed. Therefore, to improve the input/output execution performance, one should use a more suitable data format and storage system.
One of the best options for improving the input/output execution performance is to convert the CSV files into shards of TFRecords, and store the data in Cloud Storage. TFRecord is a binary data format that can store a sequence of serialized TensorFlow examples. TFRecord has several advantages over CSV, such as:
* Faster data loading: TFRecord can be read and processed faster than CSV, as it avoids the overhead of parsing and decoding the text data. TFRecord also supports compression and checksums, which can reduce the data size and ensure data integrity1
* Better performance: TFRecord can improve the performance of the model, as it allows the model to access the data in a sequential and streaming manner, and leverage the tf.data API to build efficient data pipelines. TFRecord also supports sharding and interleaving, which can increase the parallelism and throughput of the data processing2
* Easier integration: TFRecord can integrate seamlessly with TensorFlow, as it is the native data format for TensorFlow. TFRecord also supports various types of data, such as images, text, audio, and video, and can store the data schema and metadata along with the data3 Cloud Storage is a scalable and reliable object storage service that can store any amount of data. Cloud Storage has several advantages over other storage systems, such as:
* High availability: Cloud Storage can provide high availability and durability for the data, as it replicates the data across multiple regions and zones, and supports versioning and lifecycle management. Cloud Storage also offers various storage classes, such as Standard, Nearline, Coldline, and Archive, to meet different performance and cost requirements4
* Low latency: Cloud Storage can provide low latency and high bandwidth for the data, as it supports HTTP and HTTPS protocols, and integrates with other Google Cloud services, such as AI Platform, Dataflow, and BigQuery. Cloud Storage also supports resumable uploads and downloads, and parallel composite uploads, which can improve the data transfer speed and reliability5
* Easy access: Cloud Storage can provide easy access and management for the data, as it supports various tools and libraries, such as gsutil, Cloud Console, and Cloud Storage Client Libraries. Cloud Storage
* also supports fine-grained access control and encryption, which can ensure the data security and privacy.
The other options are not as effective or feasible. Loading the data into BigQuery and reading the data from BigQuery is not recommended, as BigQuery is mainly designed for analytical queries on large-scale data, and does not support streaming or real-time data processing. Loading the data into Cloud Bigtable and reading the data from Bigtable is not ideal, as Cloud Bigtable is mainly designed for low-latency and high-throughput key-value operations on sparse and wide tables, and does not support complex data types or schemas.
Converting the CSV files into shards of TFRecords and storing the data in the Hadoop Distributed File System (HDFS) is not optimal, as HDFS is not natively supported by TensorFlow, and requires additional configuration and dependencies, such as Hadoop, Spark, or Beam.
References: 1: TFRecord and tf.Example 2: Better performance with the tf.data API 3: TensorFlow Data Validation 4: Cloud Storage overview 5: Performance : [How-to guides]

291. Frage
You are creating a social media app where pet owners can post images of their pets. You have one million user uploaded images with hashtags. You want to build a comprehensive system that recommends images to users that are similar in appearance to their own uploaded images.
What should you do?

A. Use the provided hashtags to create a collaborative filtering algorithm to make recommendations.
B. Download a pretrained convolutional neural network, and use the model to generate embeddings of the input images. Measure similarity between embeddings to make recommendations.
C. Download a pretrained convolutional neural network, and fine-tune the model to predict hashtags based on the input images. Use the predicted hashtags to make recommendations.
D. Retrieve image labels and dominant colors from the input images using the Vision API. Use these properties and the hashtags to make recommendations.

Antwort: B

Begründung:
The best option to build a comprehensive system that recommends images to users that are similar in appearance to their own uploaded images is to download a pretrained convolutional neural network (CNN), and use the model to generate embeddings of the input images. Embeddings are low-dimensional representations of high-dimensional data that capture the essential features and semantics of the data. By using a pretrained CNN, you can leverage the knowledge learned from large-scale image datasets, such as ImageNet, and apply it to your own domain. A pretrained CNN can be used as a feature extractor, where the output of the last hidden layer (or any intermediate layer) is taken as the embedding vector for the input image. You can then measure the similarity between embeddings using a distance metric, such as cosine similarity or Euclidean distance, and recommend images that have the highest similarity scores to the user's uploaded image. Option A is incorrect because downloading a pretrained CNN and fine-tuning the model to predict hashtags based on the input images may not capture the visual similarity of the images, as hashtags may not reflect the appearance of the images accurately. For example, two images of different breeds of dogs may have the same hashtag #dog, but they may not look similar to each other. Moreover, fine-tuning the model may require additional data and computational resources, and it may not generalize well to new images that have different or missing hashtags. Option B is incorrect because retrieving image labels and dominant colors from the input images using the Vision API may not capture the visual similarity of the images, as labels and colors may not reflect the fine-grained details of the images. For example, two images of the same breed of dog may have different labels and colors depending on the background, lighting, and angle of the image. Moreover, using the Vision API may incur additional costs and latency, and it may not be able to handle custom or domain-specific labels. Option C is incorrect because using the provided hashtags to create a collaborative filtering algorithm may not capture the visual similarity of the images, as collaborative filtering relies on the ratings or preferences of users, not the features of the images. For example, two images of different animals may have similar ratings or preferences from users, but they may not look similar to each other. Moreover, collaborative filtering may suffer from the cold start problem, where new images or users that have no ratings or preferences cannot be recommended. Reference:
Image similarity search with TensorFlow
Image embeddings documentation
Pretrained models documentation
Similarity metrics documentation

292. Frage
You are training a TensorFlow model on a structured data set with 100 billion records stored in several CSV files. You need to improve the input/output execution performance. What should you do?

A. Load the data into Cloud Bigtable, and read the data from Bigtable
B. Load the data into BigQuery and read the data from BigQuery.
C. Convert the CSV files into shards of TFRecords, and store the data in the Hadoop Distributed File System (HDFS)
D. Convert the CSV files into shards of TFRecords, and store the data in Cloud Storage

Antwort: A

293. Frage
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