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NEW QUESTION: 1
開発者がAmazon Kinesis Data Streamsアプリケーションを作成しました。時間の経過とともに使用量が増加してトラフィックが増加するため、アプリケーションは定期的にProvisionedThroughputExceededExceptionエラーメッセージを受信します
エラーを解決するために、開発者はどの手順を実行する必要がありますか？ （2つ選択してください。）
A. Shardlteratorパラメーターを使用してシャードイテレーターを指定します。
B. GetRecords呼び出しとPutRecords呼び出しの間の遅延を増やします。
C. GetRecords呼び出しとPutRecords呼び出しに指数バックオフを実装します。
D. データストリーム内のシャードの数を増やす
E. Auto Scalingを使用してストリームをスケーリングし、パフォーマンスを向上させます
Answer: A,D
Explanation:
Reference:
https://docs.aws.amazon.com/streams/latest/dev/troubleshooting-consumers.html
NEW QUESTION: 2
A pentester must attempt to crack passwords on a windows domain that enforces strong complex passwords. Which of the following would crack the MOST passwords in the shortest time period?
A. Brute force attack
B. Rainbow tables attack
C. Dictionary attack
D. Online password testing
Answer: B
Explanation:
The passwords in a Windows (Active Directory) domain are encrypted.
When a password is "tried" against a system it is "hashed" using encryption so that the actual password is never sent in clear text across the communications line. This prevents eavesdroppers from intercepting the password. The hash of a password usually looks like a bunch of garbage and is typically a different length than the original password. Your password might be "shitzu" but the hash of your password would look something like "7378347eedbfdd761619451949225ec1".
To verify a user, a system takes the hash value created by the password hashing function on the client computer and compares it to the hash value stored in a table on the server. If the hashes match, then the user is authenticated and granted access.
Password cracking programs work in a similar way to the login process. The cracking program starts by taking plaintext passwords, running them through a hash algorithm, such as MD5, and then compares the hash output with the hashes in the stolen password file. If it finds a match then the program has cracked the password.
Rainbow Tables are basically huge sets of precomputed tables filled with hash values that are pre-matched to possible plaintext passwords. The Rainbow Tables essentially allow hackers to reverse the hashing function to determine what the plaintext password might be.
The use of Rainbow Tables allow for passwords to be cracked in a very short amount of time compared with brute-force methods, however, the trade-off is that it takes a lot of storage (sometimes Terabytes) to hold the Rainbow Tables themselves.
NEW QUESTION: 3
Your organization is considering a move to a cloud environment and is looking for certifications or audit reports from cloud providers to ensure adequate security controls and processes.
Which of the following is NOT a security certification or audit report that would be pertinent?
Response:
A. PCI DSS
B. FedRAMP
C. SOC Type 2
D. FIPS 140-2
Answer: D
NEW QUESTION: 4
モデルの適合の問題を修正する必要があります。
順番に実行する必要がある3つのアクションはどれですか？回答するには、適切なアクションをアクションのリストから回答エリアに移動し、正しい順序に並べます。
Answer:
Explanation:
Explanation:
Step 1: Augment the data
Scenario: Columns in each dataset contain missing and null values. The datasets also contain many outliers.
Step 2: Add the Bayesian Linear Regression module.
Scenario: You produce a regression model to predict property prices by using the Linear Regression and Bayesian Linear Regression modules.
Step 3: Configure the regularization weight.
Regularization typically is used to avoid overfitting. For example, in L2 regularization weight, type the value to use as the weight for L2 regularization. We recommend that you use a non-zero value to avoid overfitting.
Scenario:
Model fit: The model shows signs of overfitting. You need to produce a more refined regression model that reduces the overfitting.
Incorrect Answers:
Multiclass Decision Jungle module:
Decision jungles are a recent extension to decision forests. A decision jungle consists of an ensemble of decision directed acyclic graphs (DAGs).
L-BFGS:
L-BFGS stands for "limited memory Broyden-Fletcher-Goldfarb-Shanno". It can be found in the wwo-Class Logistic Regression module, which is used to create a logistic regression model that can be used to predict two (and only two) outcomes.
References:
<https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/linear-regr ession>
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