Google Google Certifications PROFESSIONAL-DATA-ENGINEER Questions & Answers

• Question 21:

  Which of these statements about exporting data from BigQuery is false?

  A. To export more than 1 GB of data, you need to put a wildcard in the destination filename.

  B. The only supported export destination is Google Cloud Storage.

  C. Data can only be exported in JSON or Avro format.

  D. The only compression option available is GZIP.

  Correct Answer: C

  Data can be exported in CSV, JSON, or Avro format. If you are exporting nested or repeated data, then CSV format is not supported. Reference: https://cloud.google.com/bigquery/docs/exporting-data

• Question 22:

  If you're running a performance test that depends upon Cloud Bigtable, all the choices except one below are recommended steps. Which is NOT a recommended step to follow?

  A. Do not use a production instance.

  B. Run your test for at least 10 minutes.

  C. Before you test, run a heavy pre-test for several minutes.

  D. Use at least 300 GB of data.

  Correct Answer: A

  If you're running a performance test that depends upon Cloud Bigtable, be sure to follow these steps as you plan and execute your test: Use a production instance. A development instance will not give you an accurate sense of how a production instance performs under load. Use at least 300 GB of data. Cloud Bigtable performs best with 1 TB or more of data. However, 300 GB of data is enough to provide reasonable results in a performance test on a 3-node cluster. On larger clusters, use 100 GB of data per node. Before you test, run a heavy pre-test for several minutes. This step gives Cloud Bigtable a chance to balance data across your nodes based on the access patterns it observes. Run your test for at least 10 minutes. This step lets Cloud Bigtable further optimize your data, and it helps ensure that you will test reads from disk as well as cached reads from memory. Reference: https://cloud.google.com/bigtable/docs/performance

• Question 23:

  Cloud Dataproc is a managed Apache Hadoop and Apache _____ service.

  A. Blaze

  B. Spark

  C. Fire

  D. Ignite

  Correct Answer: B

  Cloud Dataproc is a managed Apache Spark and Apache Hadoop service that lets you use open source data tools for batch processing, querying, streaming, and machine learning. Reference: https://cloud.google.com/dataproc/docs/

• Question 24:

  Which of the following is NOT one of the three main types of triggers that Dataflow supports?

  A. Trigger based on element size in bytes

  B. Trigger that is a combination of other triggers

  C. Trigger based on element count

  D. Trigger based on time

  Correct Answer: A

  There are three major kinds of triggers that Dataflow supports: 1. Time-based triggers 2. Data-driven triggers. You can set a trigger to emit results from a window when that window has received a certain number of data elements. 3. Composite triggers. These triggers combine multiple time-based or data-driven triggers in some logical way Reference: https://cloud.google.com/dataflow/model/triggers

• Question 25:

  When running a pipeline that has a BigQuery source, on your local machine, you continue to get permission denied errors. What could be the reason for that?

  A. Your gcloud does not have access to the BigQuery resources

  B. BigQuery cannot be accessed from local machines

  C. You are missing gcloud on your machine

  D. Pipelines cannot be run locally

  Correct Answer: A

  When reading from a Dataflow source or writing to a Dataflow sink using DirectPipelineRunner, the Cloud Platform account that you configured with the gcloud executable will need access to the corresponding source/sink Reference: https:// cloud.google.com/dataflow/java- sdk/JavaDoc/com/google/cloud/dataflow/sdk/runners/DirectPipelineRunner

• Question 26:

  How can you get a neural network to learn about relationships between categories in a categorical feature?

  A. Create a multi-hot column

  B. Create a one-hot column

  C. Create a hash bucket

  D. Create an embedding column

  Correct Answer: D

  There are two problems with one-hot encoding. First, it has high dimensionality, meaning that instead of having just one value, like a continuous feature, it has many values, or dimensions. This makes computation more time-consuming, especially if a feature has a very large number of categories. The second problem is that it doesn't encode any relationships between the categories. They are completely independent from each other, so the network has no way of knowing which ones are similar to each other. Both of these problems can be solved by representing a categorical feature with an embedding column. The idea is that each category has a smaller vector with, let's say, 5 values in it. But unlike a one-hot vector, the values are not usually 0. The values are weights, similar to the weights that are used for basic features in a neural network. The difference is that each category has a set of weights (5 of them in this case). You can think of each value in the embedding vector as a feature of the category. So, if two categories are very similar to each other, then their embedding vectors should be very similar too. Reference: https://cloudacademy.com/google/introduction-to-google-cloud-machine- learning-engine-course/a-wide-and-deep-model.html

• Question 27:

  Which TensorFlow function can you use to configure a categorical column if you don't know all of the possible values for that column?

  A. categorical_column_with_vocabulary_list

  B. categorical_column_with_hash_bucket

  C. categorical_column_with_unknown_values

  D. sparse_column_with_keys

  Correct Answer: B

  If you know the set of all possible feature values of a column and there are only a few of them, you can use categorical_column_with_vocabulary_list. Each key in the list will get assigned an auto-incremental ID starting from 0. What if we don't know the set of possible values in advance? Not a problem. We can use categorical_column_with_hash_bucket instead. What will happen is that each possible value in the feature column occupation will be hashed to an integer ID as we encounter them in training. Reference: https://www.tensorflow.org/tutorials/wide

• Question 28:

  You are developing a software application using Google's Dataflow SDK, and want to use conditional, for loops and other complex programming structures to create a branching pipeline. Which component will be used for the data processing operation?

  A. PCollection

  B. Transform

  C. Pipeline

  D. Sink API

  Correct Answer: B

  In Google Cloud, the Dataflow SDK provides a transform component. It is responsible for the data processing operation. You can use conditional, for loops, and other complex programming structure to create a branching pipeline. Reference: https://cloud.google.com/dataflow/model/programming-model

• Question 29:

  For the best possible performance, what is the recommended zone for your Compute Engine instance and Cloud Bigtable instance?

  A. Have the Compute Engine instance in the furthest zone from the Cloud Bigtable instance.

  B. Have both the Compute Engine instance and the Cloud Bigtable instance to be in different zones.

  C. Have both the Compute Engine instance and the Cloud Bigtable instance to be in the same zone.

  D. Have the Cloud Bigtable instance to be in the same zone as all of the consumers of your data.

  Correct Answer: C

  It is recommended to create your Compute Engine instance in the same zone as your Cloud Bigtable instance for the best possible performance, If it's not possible to create a instance in the same zone, you should create your instance in another zone within the same region. For example, if your Cloud Bigtable instance is located in us-central1-b, you could create your instance in us-central1-f. This change may result in several milliseconds of additional latency for each Cloud Bigtable request. It is recommended to avoid creating your Compute Engine instance in a different region from your Cloud Bigtable instance, which can add hundreds of milliseconds of latency to each Cloud Bigtable request. Reference: https://cloud.google.com/bigtable/docs/creating-compute-instance

• Question 30:

  Which of the following are examples of hyperparameters? (Select 2 answers.)

  A. Number of hidden layers

  B. Number of nodes in each hidden layer

  C. Biases

  D. Weights

  Correct Answer: AB

  If model parameters are variables that get adjusted by training with existing data, your hyperparameters are the variables about the training process itself. For example, part of setting up a deep neural network is deciding how many "hidden"

  layers of nodes to use between the input layer and the output layer, as well as how many nodes each layer should use. These variables are not directly related to the training data at all. They are configuration variables. Another difference is

  that parameters change during a training job, while the hyperparameters are usually constant during a job. Weights and biases are variables that get adjusted during the training process, so they are not hyperparameters.

  Reference: https://cloud.google.com/ml-engine/docs/hyperparameter-tuning-overview